diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 0000000..d1ccf9c --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,28 @@ +name: deploy + +on: + push: + branches: + - master # Set a branch to deploy + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + + - name: Setup Hugo + uses: peaceiris/actions-hugo@v2 + with: + hugo-version: '0.79.0' + + - name: Build + run: hugo --minify + + - name: Deploy + uses: peaceiris/actions-gh-pages@v3 + with: + personal_token: ${{ secrets.RESENDIS_LAB_2023 }} + external_repository: resendislab/resendislab.github.io + publish_branch: master + publish_dir: ./public diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a48cf0d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +public diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..8dada3e --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "{}" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright {yyyy} {name of copyright owner} + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..93c6d4a --- /dev/null +++ b/README.md @@ -0,0 +1,104 @@ +![deploy](https://github.com/resendislab/website/workflows/deploy/badge.svg) + +# Versión estatica de la página + +"Estaticá" significa que el contenido de la página es escrito en una versión de texto claro llamado [Markdown](https://es.wikipedia.org/wiki/Markdown). Hay un pase de "compilación" que convierte esto a la página. Nosotros usamos +[Hugo](https://gohugo.io) para esto. Si quieren editar la página y ver los cambios en su propio compu, necesitan instalar hugo (es *no* es necesario si nada más quieren editarlo). + +En Mac lo pueden instalar con homebrew + +```bash +brew install hugo +``` + +Para Linux y Windows sigan las instrucciónes [aquí](http://gohugo.io/overview/quickstart/). + +## Basicos de edición + +Para editar el contenido de la página nada más tienen que hacer caso a 3 carpetas: + +1. `content` que contiene el contenido en texto claro +2. `data` que contiene la lista de miembros de laboratorio +3. `static` que contiene archivos addicionales que quieren usar como imagenes, PDFs, etc. + +### Agregando o modificando contenido + +En hugo contenido esta formado por dos partes: + +1. un "front matter" que configura propiedades del contenido como la fecha de creación, autores, categorías y muchas más +2. el texto del contenido + +Estos dos partes aparecen en un solo archivo de texto en la siguiente forma: + + +++ + categories = [ + "science", + "tutorial", + ] + author = "Your Name" + title = "test" + date = "2016-12-06T09:19:57-06:00" + image = "" + + +++ + + ## This is an example post + + Please substitute all text below "+++" with your own! + + Una formula de Latex: + + $$ + \int_a^b e^{2\pi\cdot x} dx + $$ + + Un poco de codigo: + + ```R + library(data.table) + df <- data.table(x=1:10) + ``` + + +Aquí todo entre `+++ ... +++` es el "front matter" y lo demás es el texto. El front matter es diferente para cada tipo de contenido. Pueden ver ejemplos para cada tipo en la carpeta `archetypes`. Entonces para agregar contenido hagan lo siguiente: + +1. Agregan un nuevo archivo con la terminacion `*.md` en la sub-carpeta de `content` respetivo. Por ejemplo para agregar un + nuevo post del blog usan `content/posts/my_post.md` +2. Llenan todos los entradas del "front matter" y el texto. Pueden usar uno de los otros archivos como referencia. Si tienen instalado hugo pueden generar un archivo de templado con `hugo new posts/my_post.md`. Esto también va a llenar la fecha + automatico. + +### Cuerpo de texto + +Para el cuerpo de texto pueden usar todos los features de markdown como [descrito aquí](https://guides.github.com/features/mastering-markdown/) más los shortcodes +de Hugo [descritos aquí](http://gohugo.io/extras/shortcodes/). + +Para los posts del Blog pueden usar formulas matematicas de Latex usando +`$$ ... $$`. También para bloques de codígo usen o 3 `` ` `` o `~`. Por ejemplo para codígo de R: + + + ```R + x <- 1:10 + ``` + + +### Archivos + +Si usan archivos adicionales en su post tienen que copiar los a `static/media` +antes. Luego se pueden usar con + +```markdown +![descripcion corta]("media/imagen.jpg") +``` +para imagenes o + +```markdown +[texto enlace]("media/archivo.pdf") +``` +para enlaces normales + +## Puntos avanzados + +- para publicaciones en `pubs` la fecha (`date`) es la fecha de publicación +- para editar la sección de miembros del laboratorio es suficiente nada más + ajustar el archivo `data/members.yml` que está escrito en + [YAML](https://es.wikipedia.org/wiki/YAML). diff --git a/about/index.html b/about/index.html deleted file mode 100644 index d5b214f..0000000 --- a/about/index.html +++ /dev/null @@ -1,22 +0,0 @@ -Webpage of the Resendis Lab

Who we are

Welcome to the webpage of the Human Systems Biology group in the National -Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is -interdisciplinary and have the objective to develop a systems biology framework -to analyze mainly human diseases and metabolic phenotype in microorganisms -through the use of computational models and high-throughput technologies.

group photo

Currently, our laboratory focuses on the analysis of metabolic alterations in -cancer cells by the implementation of genome scale metabolic reconstructions and -assess the predictions in terms of experimental data at different scales. We -have developed some approaches for modeling cancer metabolism and currently we -are developing computational schemes with capacities to integrate metabolome and -RNA-seq data for elucidating metabolic mechanism in cancer cell lines and -tissues.

Lastly, our laboratory is leading efforts to test the utility of computational -schemes to explore themes related with cancer studies, such as the influence of -microbiome in cancer, the study of the biological networks regulating the -epithelial messenchymal transition and tumor heterogeneity in cancer.

Contact

Directions


Osbaldo Resendis-Antonio, PhD
Laboratory in Systems Biology and Human Diseases
Associated Professor
Instituto Nacional de Medicina Genomica – INMEGEN
Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX
Phone: +52 55 5350 1900 - Ext.1198

\ No newline at end of file diff --git a/about/index.xml b/about/index.xml deleted file mode 100644 index 4b477ac..0000000 --- a/about/index.xml +++ /dev/null @@ -1,7 +0,0 @@ -Abouts on Webpage of the Resendis Labhttps://resendislab.github.io/about/Recent content in Abouts on Webpage of the Resendis LabHugo -- gohugo.ioen-usMon, 05 Dec 2016 14:48:16 -0600Who we arehttps://resendislab.github.io/about/we/Mon, 05 Dec 2016 14:48:16 -0600https://resendislab.github.io/about/we/Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. -Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales.Contacthttps://resendislab.github.io/about/contact/Mon, 01 Jan 1900 00:00:00 +0000https://resendislab.github.io/about/contact/Directions Osbaldo Resendis-Antonio, PhD -Laboratory in Systems Biology and Human Diseases -Associated Professor -Instituto Nacional de Medicina Genomica – INMEGEN -Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX -Phone: +52 55 5350 1900 - Ext.1198 \ No newline at end of file diff --git a/archetypes/events.md b/archetypes/events.md new file mode 100644 index 0000000..416c5e4 --- /dev/null +++ b/archetypes/events.md @@ -0,0 +1,10 @@ ++++ +date = {{ .Date }} +title = "Event name" +end = "" +place = "INMEGEN" +maps = "https://goo.gl/maps/1CUc2NA46Bu" +webpage = "https://" ++++ + +Put your event description here! diff --git a/archetypes/positions.md b/archetypes/positions.md new file mode 100644 index 0000000..7511cd0 --- /dev/null +++ b/archetypes/positions.md @@ -0,0 +1,9 @@ ++++ +date = {{ .Date }} +author = "Osbaldo Resendis Antonio" +title = "The available position" ++++ + +## Available positions + +Please substitute all text below "+++" with your own! diff --git a/archetypes/posts.md b/archetypes/posts.md new file mode 100644 index 0000000..7a93367 --- /dev/null +++ b/archetypes/posts.md @@ -0,0 +1,11 @@ ++++ +date = {{ .Date }} +image = "" +categories = ["science", "tutorial"] +author = "Your Name" +title = "" ++++ + +## This is an example post + +Please substitute all text below "+++" with your own! diff --git a/archetypes/projects.md b/archetypes/projects.md new file mode 100644 index 0000000..ea27022 --- /dev/null +++ b/archetypes/projects.md @@ -0,0 +1,9 @@ ++++ +date = {{ .Date }} +people = ["Project Leader", "Project Member #2"] +title = "Project" ++++ + +## This is an example project + +Please substitute all text below "+++" with your own! diff --git a/archetypes/pubs.md b/archetypes/pubs.md new file mode 100644 index 0000000..d34e645 --- /dev/null +++ b/archetypes/pubs.md @@ -0,0 +1,11 @@ ++++ +authors = ["First Author", "Second Author"] +title = "" +journal = "" +what = "article" +doi = "" +pubmed = "" +date = "" ++++ + +An optional short description. diff --git a/archetypes/software.md b/archetypes/software.md new file mode 100644 index 0000000..58b9160 --- /dev/null +++ b/archetypes/software.md @@ -0,0 +1,11 @@ ++++ +authors = ["Project Leader", "Project Member #2"] +title = "Project" +repo = "" +docs = "" +logo = "" ++++ + +## This is an example software + +Please substitute all text below "+++" with your own! diff --git a/articles.xml b/articles.xml new file mode 100644 index 0000000..d6770d7 --- /dev/null +++ b/articles.xml @@ -0,0 +1,6192 @@ + + + + 29704665 + + 2018 + 05 + 07 + +
+ + 1095-9130 + + + 2018 + Apr + 26 + + + Methods (San Diego, Calif.) + Methods + + Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. + S1046-2023(17)30436-X + 10.1016/j.ymeth.2018.04.024 + + Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue. + Copyright © 2018. Published by Elsevier Inc. + + + + Hale + Vanessa L + VL + + Department of Veterinary Preventative Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Jeraldo + Patricio + P + + Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Mundy + Michael + M + + Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Yao + Janet + J + + Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. + + + + Keeney + Gary + G + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Scott + Nancy + N + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Cheek + E Heidi + EH + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Davidson + Jennifer + J + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Green + Megan + M + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Martinez + Christine + C + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Lehman + John + J + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Pettry + Chandra + C + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Reed + Erica + E + + Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. + + + + Lyke + Kelly + K + + Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + White + Bryan A + BA + + Carl R. Woese Institute for Genomic Biology, Department of Animal Sciences, Division of Nutritional Sciences, University of Illinois, Champaign-Urbana, IL, USA. + + + + Diener + Christian + C + + Human Systems Biology Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico. + + + + Resendis-Antonio + Osbaldo + O + + Human Systems Biology Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico; Coordinación de la Investigación Científica, Red de Apoyo a la Investigación, UNAM, Mexico City, Mexico. + + + + Gransee + Jaime + J + + Mayo Clinic Metabolomics Core Laboratory, Mayo Clinic, Rochester, MN, USA. + + + + Dutta + Tumpa + T + + Mayo Clinic Metabolomics Core Laboratory, Mayo Clinic, Rochester, MN, USA. + + + + Petterson + Xuan-Mai + XM + + Mayo Clinic Metabolomics Core Laboratory, Mayo Clinic, Rochester, MN, USA. + + + + Boardman + Lisa + L + + Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA. + + + + Larson + David + D + + Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Nelson + Heidi + H + + Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA. + + + + Chia + Nicholas + N + + Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA. Electronic address: chia.nicholas@mayo.edu. + + + + eng + + Journal Article + + + 2018 + 04 + 26 + +
+ + United States + Methods + 9426302 + 1046-2023 + + + + + + 2017 + 10 + 12 + + + 2018 + 03 + 05 + + + 2018 + 04 + 22 + + + 2018 + 4 + 29 + 6 + 0 + + + 2018 + 4 + 29 + 6 + 0 + + + 2018 + 4 + 29 + 6 + 0 + + + aheadofprint + + 29704665 + S1046-2023(17)30436-X + 10.1016/j.ymeth.2018.04.024 + + + + + + + + + 28536537 + + 2017 + 05 + 24 + + + 2017 + 05 + 28 + +
+ + + 8 + + 2017 + + + Frontiers in physiology + Front Physiol + + "Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases. + + 286 + + 10.3389/fphys.2017.00286 + + The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the "psychiatric phenotype" is to provide an improved vision of the shape of the phenotype as it is visualized by "Gestalt" psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term "Gestaltomics" as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed. + + + + Gutierrez Najera + Nora A + NA + + Instituto Nacional de Medicina GenómicaMexico City, Mexico. + + + + Resendis-Antonio + Osbaldo + O + + Instituto Nacional de Medicina GenómicaMexico City, Mexico. + + + Human Systems Biology Laboratory, Coordinación de la Investigación Científica - Red de Apoyo a la Investigación, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, National Autonomous University of Mexico (UNAM)Mexico City, Mexico. + + + + Nicolini + Humberto + H + + Instituto Nacional de Medicina GenómicaMexico City, Mexico. + + + + eng + + Journal Article + Review + + + 2017 + 05 + 09 + +
+ + Switzerland + Front Physiol + 101549006 + 1664-042X + + + + Tumour Biol. 2015 Jun;36(6):4387-96 + 25874489 + + + Nat Genet. 2010 May;42(5):441-7 + 20418890 + + + Am J Hum Genet. 2014 May 1;94(5):677-94 + 24768552 + + + World J Surg Oncol. 2016 Jul 22;14 (1):191 + 27450204 + + + Biomed Res Int. 2014;2014:428570 + 24982882 + + + Carcinogenesis. 2016 Jun;37(6):583-588 + 26992901 + + + Transl Psychiatry. 2012 Aug 14;2:e149 + 22892715 + + + Front Physiol. 2013 Sep 03;4:237 + 24027532 + + + J Proteome Res. 2011 Dec 2;10(12):5433-43 + 22007635 + + + Am J Med Genet B Neuropsychiatr Genet. 2017 Mar 27;:null + 28349588 + + + Database (Oxford). 2015 Jul 24;2015:bav068 + 26209309 + + + Metabolites. 2016 Mar 02;6(1):null + 26950159 + + + Handb Exp Pharmacol. 2009;(192):235-59 + 19184652 + + + Nat Genet. 2011 Jun;43(6):565-9 + 21572414 + + + Cell Rep. 2015 May 5;11(5):835-48 + 25921536 + + + BMC Genomics. 2016 Feb 27;17:145 + 26921259 + + + Eur J Surg Oncol. 1985 Mar;11(1):15-20 + 3987908 + + + IEEE Trans Nanobioscience. 2004 Sep;3(3):164-71 + 15473068 + + + Med Clin (Barc). 2017 Feb 21;:null + 28236470 + + + Methods Mol Biol. 2016;1462:139-55 + 27604717 + + + Acta Psychiatr Scand Suppl. 1971;221:120-7 + 5286323 + + + Pharmacogenomics. 2007 Jul;8(7):683-6 + 18240899 + + + Sci Rep. 2016 Jun 23;6:28415 + 27335086 + + + Front Physiol. 2016 Dec 27;7:644 + 28082911 + + + Mol Psychiatry. 2017 Mar;22(3):384-395 + 27001617 + + + Biol Psychiatry. 2014 Jun 15;75(12):920-8 + 24286759 + + + Proc Natl Acad Sci U S A. 2006 Apr 18;103(16):6332-7 + 16601104 + + + J Proteome Res. 2008 Oct;7(10):4266-77 + 18778095 + + + Methods Inf Med. 1998 Nov;37(4-5):564-75 + 9865054 + + + Am J Med Genet B Neuropsychiatr Genet. 2007 Dec 5;144B(8):1048-53 + 17525973 + + + Cogn Neuropsychiatry. 2009;14(4-5):419-50 + 19634038 + + + Cogn Neuropsychol. 2005 May;22(3):262-75 + 21038249 + + + Sao Paulo Med J. 2003 May 5;121(3):111-6 + 12920472 + + + J Food Drug Anal. 2013 Dec;21(4):S87-S90 + 25214750 + + + PLoS One. 2014 Sep 18;9(9):e107268 + 25233467 + + + Genet Mol Res. 2016 Dec 19;15(4): + 28002588 + + + Stud Health Technol Inform. 1998;52 Pt 2:1192-6 + 10384648 + + + Sci Data. 2016 Dec 06;3:160110 + 27922632 + + + Pharmacogenet Genomics. 2009 Sep;19(9):666-74 + 19724244 + + + Stud Health Technol Inform. 2004;107(Pt 1):416-9 + 15360846 + + + Eur J Hum Genet. 2013 Nov;21(11):1293-9 + 23443019 + + + Nucleic Acids Res. 2013 Jan;41(Database issue):D801-7 + 23161693 + + + Br J Psychiatry. 1990 Jan;156:79-83 + 2297623 + + + Nat Genet. 2010 May;42(5):448-53 + 20418888 + + + Hum Mol Genet. 2009 Aug 15;18(16):3125-35 + 19443489 + + + Stud Health Technol Inform. 2001;84(Pt 1):118-22 + 11604718 + + + Cancer Epidemiol Biomarkers Prev. 2016 Apr;25(4):693-9 + 26827895 + + + N Engl J Med. 2015 Feb 26;372(9):793-5 + 25635347 + + + Psychother Psychosom. 2015;84(1):22-9 + 25547421 + + + Syst Rev. 2017 Apr 4;6(1):69 + 28376926 + + + Neurology. 2005 Mar 8;64(5):895-8 + 15753432 + + + Int J Data Min Bioinform. 2008;2(1):54-77 + 18399328 + + + Epigenetics. 2013 Oct;8(10):1030-8 + 23907097 + + + Sci Rep. 2016 Jun 13;6:27923 + 27295340 + + + Am J Med Genet B Neuropsychiatr Genet. 2006 Apr 5;141B(3):261-8 + 16526044 + + + J Proteome Res. 2012 Dec 7;11(12):5856-62 + 23106572 + + + J Psychiatr Res. 2007 Sep;41(6):493-501 + 16750834 + + + Carcinogenesis. 2016 Mar;37(3):269-279 + 26818358 + + + Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15533-8 + 16223876 + + + Br J Psychiatry. 2000 Sep;177:212-7 + 11040880 + + + Cell. 2013 Dec 19;155(7):1451-63 + 24315484 + + + Neuroscience. 2009 Nov 24;164(1):30-42 + 19344640 + + + Mol Psychiatry. 2015 Nov;20(11):1266-85 + 26283638 + + + Neuropsychobiology. 2006;54(1):70-4 + 17028448 + + + J Natl Cancer Inst. 2011 Sep 7;103(17 ):1342-6 + 21747048 + + + Neuropharmacology. 2004;47 Suppl 1:148-66 + 15464134 + + + J Nerv Ment Dis. 2009 Jul;197(7):530-5 + 19597361 + + + Postepy Hig Med Dosw (Online). 2016 Mar 04;70:186-93 + 26943316 + + + Oncogene. 2013 Mar 14;32(11):1384-95 + 22614008 + + + CNS Spectr. 2015 Oct;20(5):469-73 + 26349811 + + + Nat Rev Genet. 2010 Dec;11(12):855-66 + 21085204 + + + Drug Discov Today. 2005 Nov 15;10(22):1551-8 + 16257378 + + + J Abnorm Psychol. 2013 Aug;122(3):917-27 + 23647123 + + + Mol Psychiatry. 2008 Dec;13(12):1118-28 + 17938634 + + + Nucleic Acids Res. 2009 Jan;37(Database issue):D603-10 + 18953024 + + + Mol Psychiatry. 2016 Oct;21(10):1381-90 + 26666204 + + + PLoS Genet. 2012;8(10):e1003005 + 23093944 + + + Eur J Hum Genet. 2015 Aug;23(8):1106-10 + 25424713 + + + Am J Med Genet B Neuropsychiatr Genet. 2016 Apr;171B(3):414-26 + 26892569 + + + Genet Mol Res. 2016 Sep 23;15(3): + 27706775 + + + Schizophr Res. 2009 Oct;114(1-3):6-16 + 19695837 + + + Mol Psychiatry. 2016 May;21(5):594-600 + 26952864 + + + Neurobiol Dis. 2009 Aug;35(2):157-64 + 18938247 + + + Int J Neuropsychopharmacol. 2014 Oct 31;18(2):null + 25609598 + + + Lancet Oncol. 2014 Nov;15(12):e568-80 + 25439699 + + + Clin Epigenetics. 2015 Oct 16;7:113 + 26478754 + + + Psychiatry Investig. 2015 Oct;12(4):523-31 + 26508964 + + + Am J Psychiatry. 2010 May;167(5):509-27 + 20231323 + + + Xenobiotica. 2010 Jun;40(6):381-92 + 20307138 + + + Int J Bipolar Disord. 2015 Dec;3(1):21 + 26440506 + + + J Affect Disord. 2013 May 15;148(1):57-65 + 23276701 + + + Brief Bioinform. 2013 Nov;14(6):696-712 + 22962340 + + + Electrophoresis. 2016 Jun;37(11):1549-61 + 26957254 + + + Neuroimage. 2017 Jan 15;145(Pt B):389-408 + 26658930 + + + Anal Bioanal Chem. 2013 Jun;405(15):5291-300 + 23571465 + + + Acta Psychiatr Scand. 2008 May;117(5):323-36 + 18331573 + + + Hum Mol Genet. 2011 Dec 15;20(24):4786-96 + 21908516 + + + Int J Neurosci. 2012 Apr;122(4):165-76 + 22115417 + + + Salud Publica Mex. 2009;51 Suppl 2:s263-9 + 19967282 + + + Methods Mol Biol. 2016;1462:157-74 + 27604718 + + + Mol Psychiatry. 2015 Mar;20(3):286-8 + 25582618 + + + Int J Neuropsychopharmacol. 2015 Oct 09;19(3):pyv096 + 26453695 + + + Nature. 2014 Jan 16;505(7483):361-6 + 24352232 + + + + diagnosis + lung cancer + omics + psychiatry + systems biology + + + + + + 2016 + 09 + 20 + + + 2017 + 04 + 19 + + + 2017 + 5 + 25 + 6 + 0 + + + 2017 + 5 + 26 + 6 + 0 + + + 2017 + 5 + 26 + 6 + 0 + + + epublish + + 28536537 + 10.3389/fphys.2017.00286 + PMC5422874 + + + + + + + + 28410570 + + 2017 + 04 + 15 + + + 2017 + 04 + 18 + + + 2017 + 04 + 18 + +
+ + 1471-2148 + + 17 + 1 + + 2017 + Apr + 14 + + + BMC evolutionary biology + BMC Evol. Biol. + + Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora. + + 99 + + 10.1186/s12862-017-0947-6 + + Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore. + We reconstructed the metabolic networks of the chromatophore of P. chromatophora CCAC 0185 and a close free-living relative, the cyanobacterium Synechococcus sp. WH 5701. We found that the evolution of free-living to host-restricted lifestyle rendered a fragile metabolic network where >80% of genes in the chromatophore are essential for metabolic functionality. Despite the lack of experimental information, the metabolic reconstruction of the chromatophore suggests that the host provides several metabolites to the endosymbiont. By using these metabolites as intracellular conditions, in silico simulations of genome evolution by gene lose recover with 77% accuracy the actual metabolic gene content of the chromatophore. Also, the metabolic model of the chromatophore allowed us to predict by flux balance analysis a maximum rate of reduced-carbon released by the endosymbiont to the host. By inspecting the central metabolism of the chromatophore and the free-living cyanobacteria we found that by improvements in the gluconeogenic pathway the metabolism of the endosymbiont uses more efficiently the carbon source for reduced-carbon production. In addition, our in silico simulations of the evolutionary process leading to the reduced metabolic network of the chromatophore showed that the predicted rate of released reduced-carbon is obtained in less than 5% of the times under a process guided by random gene deletion and genetic drift. We interpret previous findings as evidence that natural selection at holobiont level shaped the rate at which reduced-carbon is exported to the host. Finally, our model also predicts that the ABC phosphate transporter (pstSACB) which is conserved in the genome of the chromatophore of P. chromatophora strain CCAC 0185 is a necessary component to release reduced-carbon molecules to the host. + Our evolutionary analysis suggests that in the case of Paulinella chromatophora natural selection at the holobiont level played a prominent role in shaping the metabolic specialization of the chromatophore. We propose that natural selection acted as a "metabolic engineer" by favoring metabolic restructurings that led to an increased release of reduced-carbon to the host. + + + + Valadez-Cano + Cecilio + C + + Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Irapuato, Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36821, Guanajuato, Irapuato, Mexico. + + + + Olivares-Hernández + Roberto + R + + Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana-Cuajimalpa, Av. Vasco de Quiroga 4871, Santa Fe, Del. Cuajimalpa, C.P. 05348, Ciudad de Mexico, México, Mexico. + + + + Resendis-Antonio + Osbaldo + O + + Human Systems Biology Laboratory, Coordinación de la Investigación Científica-Red de Apoyo a la Investigación (RAI), UNAM, México City, Mexico. + + + Instituto Nacional de Medicina Genómica (INMEGEN), 14610, México City, Mexico. + + + + DeLuna + Alexander + A + + Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados del IPN, Guanajuato, Irapuato, Mexico. + + + + Delaye + Luis + L + http://orcid.org/0000-0003-4193-2720 + + Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Irapuato, Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36821, Guanajuato, Irapuato, Mexico. luis.delaye@cinvestav.mx. + + + + eng + + Journal Article + + + 2017 + 04 + 14 + +
+ + England + BMC Evol Biol + 100966975 + 1471-2148 + + + + 0 + Hexoses + + + IM + + + Mol Biol Evol. 2014 Apr;31(4):857-71 + 24398322 + + + Bioinformatics. 2003 May 1;19(7):891-2 + 12724301 + + + Cell. 2013 Jun 20;153(7):1567-78 + 23791183 + + + Proc Natl Acad Sci U S A. 2015 Aug 18;112(33):10169-76 + 25713367 + + + Plant Physiol. 1977 Jun;59(6):1146-55 + 16660011 + + + Annu Rev Chem Biomol Eng. 2011;2:211-36 + 22432617 + + + Trends Biotechnol. 2011 Aug;29(8):370-8 + 21561673 + + + Microb Cell Fact. 2012 Dec 11;11:156 + 23232052 + + + Proc Natl Acad Sci U S A. 1996 Apr 2;93(7):2873-8 + 8610134 + + + Elife. 2015 Dec 29;4: + 26714105 + + + Nat Rev Microbiol. 2011 Nov 08;10(1):13-26 + 22064560 + + + Metab Eng. 2011 Jul;13(4):353-63 + 21569861 + + + Proc Biol Sci. 2014 Sep 22;281(1791):20141163 + 25080346 + + + BMC Syst Biol. 2009 Feb 21;3:24 + 19232131 + + + Nature. 2006 Mar 30;440(7084):667-70 + 16572170 + + + Metab Eng. 2008 Sep;10 (5):227-33 + 18611443 + + + Protist. 2005 Dec;156(4):425-32 + 16310747 + + + Genetica. 2008 Oct;134(2):205-10 + 18046510 + + + PLoS Biol. 2005 Apr;3(4):e121 + 15780005 + + + Metab Eng. 1999 Jan;1(1):1-11 + 10935750 + + + PLoS One. 2016 Feb 17;11(2):e0149423 + 26885654 + + + Mol Biol Evol. 2004 Jun;21(6):1110-22 + 15014155 + + + PLoS Genet. 2012;8(5):e1002651 + 22589730 + + + MBio. 2015 Nov 10;6(6):e01732-15 + 26556278 + + + Proc Natl Acad Sci U S A. 2015 Aug 18;112(33):10208-15 + 25825767 + + + PLoS One. 2009;4(3):e4969 + 19305500 + + + Radiology. 1982 Apr;143(1):29-36 + 7063747 + + + J Bacteriol. 2008 Apr;190(8):2790-803 + 18192387 + + + Mol Ecol. 2014 Mar;23(6):1473-96 + 23952067 + + + Biotechnol Bioeng. 2003 Dec 20;84(6):647-57 + 14595777 + + + PLoS Comput Biol. 2008 Oct;4(10):e1000210 + 18974823 + + + Photosynth Res. 1987 Jan;14(3):211-27 + 24430736 + + + BMC Microbiol. 2012 Jan 18;12 Suppl 1:S5 + 22376077 + + + Genetics. 1998 Aug;149(4):2135-46 + 9691064 + + + Nat Protoc. 2011 Aug 04;6(9):1290-307 + 21886097 + + + PLoS One. 2012;7(1):e30652 + 22292008 + + + Proc Natl Acad Sci U S A. 2012 Feb 14;109(7):2678-83 + 22308420 + + + BMC Bioinformatics. 2005 Dec 23;6:308 + 16375763 + + + Insect Mol Biol. 2010 Mar;19 Suppl 2:249-58 + 20482655 + + + BMC Syst Biol. 2011 Jan 21;5:12 + 21251315 + + + Trends Plant Sci. 2013 Dec;18(12):673-9 + 24126104 + + + Photosynth Res. 2011 Jan;107(1):117-32 + 20405214 + + + Proc Biol Sci. 2008 Jan 7;275(1630):91-100 + 17971325 + + + BMC Biol. 2012 Apr 24;10:35 + 22531210 + + + Microbiol Mol Biol Rev. 2004 Dec;68(4):745-70 + 15590782 + + + Genome Biol. 2007;8(10):R212 + 17919328 + + + PLoS Comput Biol. 2011 Feb;7(2):e1001082 + 21379323 + + + Nat Biotechnol. 2010 Mar;28(3):245-8 + 20212490 + + + BMC Genomics. 2006 Apr 30;7:100 + 16646993 + + + Mol Biol Evol. 2010 Jul;27(7):1530-7 + 20123796 + + + BMC Evol Biol. 2009 May 13;9:98 + 19439085 + + + Trends Microbiol. 1998 Jul;6(7):263-8 + 9717214 + + + PLoS Biol. 2004 Mar;2(3):E69 + 15024419 + + + Curr Biol. 2008 Mar 25;18(6):410-8 + 18356055 + + + Nature. 2000 Sep 7;407(6800):81-6 + 10993077 + + + Nat Rev Genet. 2008 Mar;9(3):218-29 + 18268509 + + + Philos Trans R Soc Lond B Biol Sci. 2007 Oct 29;362(1486):1751-62 + 17510022 + + + Mol Biol Evol. 2011 Jan;28(1):407-22 + 20702568 + + + Genome Res. 2009 Aug;19(8):1450-4 + 19502381 + + + Nat Protoc. 2010 Jan;5(1):93-121 + 20057383 + + + Proc Natl Acad Sci U S A. 2012 Apr 3;109(14):5340-5 + 22371600 + + + + + Biological Evolution + + + Cercozoa + cytology + genetics + physiology + + + Computer Simulation + + + Cyanobacteria + genetics + physiology + + + Hexoses + metabolism + + + Selection, Genetic + + + Symbiosis + + + Synechococcus + cytology + metabolism + + + + Adaptation + Endosymbiont + Metabolic evolution + Metabolic integration + + + + + + 2017 + 01 + 04 + + + 2017 + 03 + 28 + + + 2017 + 4 + 16 + 6 + 0 + + + 2017 + 4 + 16 + 6 + 0 + + + 2017 + 4 + 19 + 6 + 0 + + + epublish + + 28410570 + 10.1186/s12862-017-0947-6 + 10.1186/s12862-017-0947-6 + PMC5392233 + + + + + + 28082911 + + 2017 + 01 + 13 + + + 2017 + 01 + 18 + +
+ + + 7 + + 2016 + + + Frontiers in physiology + Front Physiol + + Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies. + + 644 + + 10.3389/fphys.2016.00644 + + Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients. + + + + Diener + Christian + C + + Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica México City, Mexico. + + + + Resendis-Antonio + Osbaldo + O + + Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMéxico City, Mexico; Coordinación de la Investigación Científica, Red de Apoyo a la Investigación, UNAMMéxico City, Mexico. + + + + eng + + Journal Article + + + 2016 + 12 + 27 + +
+ + Switzerland + Front Physiol + 101549006 + 1664-042X + + + NCI60 + TCGA + flux balance analysis + personalized medicine + proliferation + systems biology + + + + + + 2016 + 10 + 03 + + + 2016 + 12 + 09 + + + 2017 + 1 + 14 + 6 + 0 + + + 2017 + 1 + 14 + 6 + 0 + + + 2017 + 1 + 14 + 6 + 0 + + + epublish + + 28082911 + 10.3389/fphys.2016.00644 + PMC5186797 + + + + + + + + 28018236 + + 2016 + 12 + 26 + + + 2016 + 12 + 28 + +
+ + + 7 + + 2016 + + + Frontiers in physiology + Front Physiol + + Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine. + + 606 + + 10.3389/fphys.2016.00606 + + It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology. + + + + Contreras + Alejandra V + AV + + Instituto Nacional de Medicina Genómica Mexico City, Mexico. + + + + Cocom-Chan + Benjamin + B + + Instituto Nacional de Medicina GenómicaMexico City, Mexico; Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMexico City, Mexico. + + + + Hernandez-Montes + Georgina + G + + Coordinación de la Investigación Científica, Red de Apoyo a la Investigación-National Autonomous University of Mexico (UNAM) Mexico City, Mexico. + + + + Portillo-Bobadilla + Tobias + T + + Coordinación de la Investigación Científica, Red de Apoyo a la Investigación-National Autonomous University of Mexico (UNAM) Mexico City, Mexico. + + + + Resendis-Antonio + Osbaldo + O + + Instituto Nacional de Medicina GenómicaMexico City, Mexico; Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMexico City, Mexico; Coordinación de la Investigación Científica, Red de Apoyo a la Investigación-National Autonomous University of Mexico (UNAM)Mexico City, Mexico. + + + + eng + + Review + Journal Article + + + 2016 + 12 + 09 + +
+ + Switzerland + Front Physiol + 101549006 + 1664-042X + + + cancer metabolism + metabolome + microbiome + next generation sequencing (NGS) + precision medicine + systems integration + + + + + + 2016 + 08 + 08 + + + 2016 + 11 + 21 + + + 2016 + 12 + 27 + 6 + 0 + + + 2016 + 12 + 27 + 6 + 0 + + + 2016 + 12 + 27 + 6 + 1 + + + epublish + + 28018236 + 10.3389/fphys.2016.00606 + PMC5145879 + + + + + + + 17559662 + + 2007 + 07 + 03 + + + 2007 + 08 + 07 + + + 2014 + 09 + 04 + +
+ + 1471-2180 + + 7 + + 2007 + Jun + 08 + + + BMC microbiology + BMC Microbiol. + + Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli. + + 53 + + + Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses. + Transcriptome data from isogenic wild type and crp- strains grown in Luria-Bertani medium (LB) or LB + 4 g/L glucose (LB+G) were analyzed to identify differentially transcribed genes. We detected 180 and 200 genes displaying increased and reduced relative transcript levels in the presence of glucose, respectively. The observed expression pattern in LB was consistent with a gluconeogenic metabolic state including active transport and interconversion of small molecules and macromolecules, induction of protease-encoding genes and a partial heat shock response. In LB+G, catabolic repression was detected for transport and metabolic interconversion activities. We also detected an increased capacity for de novo synthesis of nucleotides, amino acids and proteins. Cluster analysis of a subset of genes revealed that CRP mediates catabolite repression for most of the genes displaying reduced transcript levels in LB+G, whereas Fis participates in the upregulation of genes under this condition. An analysis of the regulatory network, in terms of topological functional units, revealed 8 interconnected modules which again exposed the importance of Fis and CRP as directly responsible for the coordinated response of the cell. This effect was also seen with other not extensively connected transcription factors such as FruR and PdhR, which showed a consistent response considering media composition. + This work allowed the identification of eight interconnected regulatory network modules that includes CRP, Fis and other transcriptional factors that respond directly or indirectly to the presence of glucose. In most cases, each of these modules includes genes encoding physiologically related functions, thus indicating a connection between regulatory network topology and related cellular functions involved in nutrient sensing and metabolism. + + + + Gutierrez-Ríos + Rosa María + RM + + Departamentos de Microbiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Apdo. Postal 510-3, Cuernavaca, Morelos, México. rmaria@ibt.unam.mx + + + + Freyre-Gonzalez + Julio A + JA + + + Resendis + Osbaldo + O + + + Collado-Vides + Julio + J + + + Saier + Milton + M + + + Gosset + Guillermo + G + + + eng + + Comparative Study + Journal Article + Research Support, Non-U.S. Gov't + + + 2007 + 06 + 08 + +
+ + England + BMC Microbiol + 100966981 + 1471-2180 + + + + 0 + Culture Media + + + 0 + Cyclic AMP Receptor Protein + + + 0 + Escherichia coli Proteins + + + 0 + Factor For Inversion Stimulation Protein + + + 0 + Fis protein, E coli + + + 0 + RNA, Bacterial + + + 0 + RNA, Messenger + + + 0 + Transcription Factors + + + 0 + crp protein, E coli + + + IY9XDZ35W2 + Glucose + + + IM + + + Mol Gen Genet. 1999 Dec;262(4-5):876-83 + 10628873 + + + Nucleic Acids Res. 2006 Jan 1;34(Database issue):D394-7 + 16381895 + + + Nat Biotechnol. 2000 Dec;18(12):1262-8 + 11101804 + + + Mol Microbiol. 2001 Mar;39(6):1572-84 + 11260474 + + + Appl Environ Microbiol. 2001 May;67(5):2310-8 + 11319117 + + + J Mol Microbiol Biotechnol. 2001 Jul;3(3):329-46 + 11361063 + + + J Biol Chem. 2001 May 25;276(21):17878-86 + 11279109 + + + Microbiology. 2001 Nov;147(Pt 11):2981-9 + 11700349 + + + J Bacteriol. 2001 Dec;183(24):7329-40 + 11717292 + + + J Biol Chem. 2002 Apr 12;277(15):13175-83 + 11815613 + + + J Bacteriol. 2002 Sep;184(17):4881-90 + 12169614 + + + J Biol Chem. 2003 Apr 25;278(17):14776-81 + 12588863 + + + Curr Opin Microbiol. 2003 Oct;6(5):482-9 + 14572541 + + + Genome Res. 2003 Nov;13(11):2435-43 + 14597655 + + + Curr Opin Microbiol. 2004 Apr;7(2):102-8 + 15063844 + + + Appl Environ Microbiol. 2004 Apr;70(4):2354-66 + 15066832 + + + J Bacteriol. 2004 Jun;186(11):3516-24 + 15150239 + + + Bioinformatics. 2004 Jun 12;20(9):1453-4 + 14871861 + + + Annu Rev Microbiol. 1977;31:397-419 + 410356 + + + Cell. 1985 Jun;41(2):587-95 + 3886165 + + + Biochem J. 1986 Jul 15;237(2):547-57 + 3541901 + + + J Bacteriol. 1989 Jun;171(6):3494-503 + 2656658 + + + Genes Dev. 1989 Aug;3(8):1226-32 + 2676724 + + + J Bacteriol. 1991 Mar;173(6):1992-6 + 2002001 + + + Cell. 1991 Sep 20;66(6):1185-95 + 1913806 + + + J Bacteriol. 1992 Feb;174(3):758-64 + 1310091 + + + J Mol Biol. 1993 Nov 5;234(1):28-44 + 8230205 + + + Mol Microbiol. 1994 Apr;12(1):95-104 + 8057842 + + + Mol Microbiol. 1995 May;16(3):521-34 + 7565112 + + + J Bacteriol. 1996 Jan;178(1):280-3 + 8550429 + + + Mol Microbiol. 1996 Aug;21(3):613-20 + 8866483 + + + Annu Rev Microbiol. 1996;50:645-77 + 8905094 + + + J Bacteriol. 1997 Aug;179(16):5126-30 + 9260955 + + + J Bacteriol. 1997 Dec;179(23):7410-9 + 9393706 + + + J Mol Biol. 1998 Jun 26;279(5):1027-43 + 9642081 + + + Mol Microbiol. 1998 Aug;29(4):1053-63 + 9767573 + + + Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8 + 9843981 + + + J Bacteriol. 1999 Mar;181(6):1912-9 + 10074087 + + + J Bacteriol. 1999 Oct;181(20):6425-40 + 10515934 + + + Nucleic Acids Res. 2004;32(19):5874-93 + 15520470 + + + Bioinformatics. 2004 Nov 22;20(17):3246-8 + 15180930 + + + Comput Biol Chem. 2004 Dec;28(5-6):341-50 + 15556475 + + + Nucleic Acids Res. 2005 Jan 1;33(Database issue):D334-7 + 15608210 + + + J Bacteriol. 2005 Feb;187(3):980-90 + 15659676 + + + Trends Genet. 2005 Jan;21(1):16-20 + 15680508 + + + J Biol Chem. 2005 Apr 22;280(16):15921-7 + 15705577 + + + Proc Natl Acad Sci U S A. 2005 May 31;102(22):7841-6 + 15908506 + + + Mol Microbiol. 2005 Jul;57(2):496-510 + 15978080 + + + Genes Dev. 2005 Jul 1;19(13):1499-511 + 15998805 + + + J Bacteriol. 2000 Sep;182(18):5029-35 + 10960084 + + + + + Cluster Analysis + + + Culture Media + chemistry + + + Cyclic AMP Receptor Protein + genetics + physiology + + + Escherichia coli + genetics + metabolism + + + Escherichia coli Proteins + genetics + physiology + + + Factor For Inversion Stimulation Protein + physiology + + + Gene Deletion + + + Gene Expression Profiling + + + Gene Expression Regulation, Bacterial + genetics + physiology + + + Gene Regulatory Networks + + + Glucose + metabolism + + + Metabolic Networks and Pathways + genetics + + + Oligonucleotide Array Sequence Analysis + + + RNA, Bacterial + biosynthesis + genetics + + + RNA, Messenger + biosynthesis + genetics + + + Regulon + + + Transcription Factors + genetics + physiology + + + PMC1905917 + + + + + 2007 + 01 + 15 + + + 2007 + 06 + 08 + + + 2007 + 6 + 15 + 9 + 0 + + + 2007 + 8 + 8 + 9 + 0 + + + 2007 + 6 + 15 + 9 + 0 + + + epublish + + 17559662 + 1471-2180-7-53 + 10.1186/1471-2180-7-53 + PMC1905917 + + + + + + + 17188715 + + 2007 + 03 + 13 + + + 2007 + 05 + 29 + + + 2007 + 12 + 03 + +
+ + 0022-5193 + + 245 + 3 + + 2007 + Apr + 07 + + + Journal of theoretical biology + J. Theor. Biol. + + Robustness and evolvability in genetic regulatory networks. + + 433-48 + + + Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. Here we address this problem by studying the robustness and evolvability of the attractor landscape of genetic regulatory network models under the process of gene duplication followed by divergence. We show that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear. The above is true in a variety of network topologies and even for the case of extreme divergence in which the duplicate gene bears almost no relation with its parent. Our results indicate that networks operating close to the so-called "critical regime" exhibit the maximum robustness and evolvability simultaneously. + + + + Aldana + Maximino + M + + Centro de Ciencias Físicas, Universidad Nacional Autónoma de México, C.P. 62251, Apartado Postal 48-3, Cuernavaca, Morelos, Mexico. max@fis.unam.mx + + + + Balleza + Enrique + E + + + Kauffman + Stuart + S + + + Resendiz + Osbaldo + O + + + eng + + + GM070600-01 + GM + NIGMS NIH HHS + United States + + + + Journal Article + Research Support, N.I.H., Extramural + Research Support, Non-U.S. Gov't + Research Support, U.S. Gov't, Non-P.H.S. + + + 2006 + 11 + 01 + +
+ + England + J Theor Biol + 0376342 + 0022-5193 + + IM + + + Animals + + + Conserved Sequence + + + Evolution, Molecular + + + Gene Expression Regulation + + + Genotype + + + Models, Genetic + + + Mutation + + + Nonlinear Dynamics + + + Phenotype + + + + + + + 2006 + 03 + 29 + + + 2006 + 10 + 17 + + + 2006 + 10 + 25 + + + 2006 + 12 + 26 + 9 + 0 + + + 2007 + 5 + 30 + 9 + 0 + + + 2006 + 12 + 26 + 9 + 0 + + + ppublish + + 17188715 + S0022-5193(06)00517-0 + 10.1016/j.jtbi.2006.10.027 + + + + + + + 27616995 + + 2016 + 09 + 12 + + + 2016 + 09 + 12 + + + 2016 + 09 + 14 + +
+ + + 7 + + 2016 + + + Frontiers in physiology + Front Physiol + + Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks. + + 375 + + 10.3389/fphys.2016.00375 + + + Jalili + Mahdi + M + + Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran. + + + + Salehzadeh-Yazdi + Ali + A + + Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical SciencesTehran, Iran; Department of Systems Biology and Bioinformatics, University of RostockRostock, Germany. + + + + Gupta + Shailendra + S + + Department of Systems Biology and Bioinformatics, University of RostockRostock, Germany; CSIR-Indian Institute of Toxicology ResearchLucknow, India. + + + + Wolkenhauer + Olaf + O + + Department of Systems Biology and Bioinformatics, University of Rostock Rostock, Germany. + + + + Yaghmaie + Marjan + M + + Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran. + + + + Resendis-Antonio + Osbaldo + O + + Human Systems Biology Laboratory, RAI-UNAM and INMEGEN Mexico City, Mexico. + + + + Alimoghaddam + Kamran + K + + Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran. + + + + eng + + Journal Article + + + 2016 + 08 + 26 + +
+ + Switzerland + Front Physiol + 101549006 + 1664-042X + + PMC4999434 + + biological centrality + biological network + centrality + essentiality + topological network analysis + + + + + + 2015 + 08 + 26 + + + 2016 + 08 + 12 + + + 2016 + 9 + 13 + 6 + 0 + + + 2016 + 9 + 13 + 6 + 0 + + + 2016 + 9 + 13 + 6 + 1 + + + epublish + + 27616995 + 10.3389/fphys.2016.00375 + PMC4999434 + + + + + + + + 27335086 + + 2016 + 06 + 23 + + + 2016 + 06 + 28 + +
+ + 2045-2322 + + 6 + + 2016 + Jun + 23 + + + Scientific reports + Sci Rep + + The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome. + + 28415 + + 10.1038/srep28415 + + During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway. + + + + Diener + Christian + C + + Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico. + + + + Muñoz-Gonzalez + Felipe + F + + Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico. + + + + Encarnación + Sergio + S + + Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca 62210, México. + + + + Resendis-Antonio + Osbaldo + O + + Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico. + + + Coordinación de la Investigación Científica - Red de Apoyo a la Investigación UNAM, Mexico. + + + + eng + + Journal Article + + + 2016 + 06 + 23 + +
+ + England + Sci Rep + 101563288 + 2045-2322 + + IM + PMC4917846 + + + + + 2015 + 12 + 17 + + + 2016 + 05 + 31 + + + 2016 + 6 + 24 + 6 + 0 + + + 2016 + 6 + 24 + 6 + 0 + + + 2016 + 6 + 24 + 6 + 0 + + + epublish + + 27335086 + srep28415 + 10.1038/srep28415 + PMC4917846 + + + + + + + + 24747697 + + 2014 + 12 + 26 + + + 2015 + 09 + 01 + + + 2015 + 11 + 19 + +
+ + 1096-3650 + + 30 + + 2015 + Feb + + + Seminars in cancer biology + Semin. Cancer Biol. + + Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer. + + 79-87 + + 10.1016/j.semcancer.2014.04.003 + S1044-579X(14)00051-0 + + Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century. + Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved. + + + + Resendis-Antonio + Osbaldo + O + + Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico. Electronic address: oresendis@inmegen.gob.mx. + + + + González-Torres + Carolina + C + + Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico. + + + + Jaime-Muñoz + Gustavo + G + + Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico; Facultad de Medicina-UNAM, Mexico. + + + + Hernandez-Patiño + Claudia Erika + CE + + Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico; Undergraduate Program in Genomic Sciences-UNAM, Cuernavaca, Mexico. + + + + Salgado-Muñoz + Carlos Felipe + CF + + Laboratory of Human Systems Biology, Instituto Nacional de Medicina Genómica (INMEGEN), México, DF, Mexico; Facultad de Medicina-UNAM, Mexico. + + + + eng + + Journal Article + Research Support, Non-U.S. Gov't + Review + + + 2014 + 04 + 18 + +
+ + England + Semin Cancer Biol + 9010218 + 1044-579X + + IM + + + Humans + + + Models, Biological + + + Neoplasms + metabolism + + + Precision Medicine + + + Systems Biology + + + + Cancer metabolism + Mathematical models + P4 medicine + Systems biology: Constraint-based modeling + + + + + + 2014 + 01 + 25 + + + 2014 + 04 + 01 + + + 2014 + 04 + 04 + + + 2014 + 4 + 22 + 6 + 0 + + + 2014 + 4 + 22 + 6 + 0 + + + 2015 + 9 + 2 + 6 + 0 + + + ppublish + + 24747697 + S1044-579X(14)00051-0 + 10.1016/j.semcancer.2014.04.003 + + + + + + + + 23316163 + + 2013 + 01 + 14 + + + 2013 + 01 + 15 + + + 2013 + 08 + 13 + +
+ + 1664-042X + + 3 + + 2012 + + + Frontiers in physiology + Front Physiol + + Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells. + + 481 + + 10.3389/fphys.2012.00481 + + One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues. + + + + Hernández Patiño + Claudia E + CE + + Undergraduate Program for Genomic Sciences, Universidad Nacional Autónoma de México Cuernavaca, México. + + + + Jaime-Muñoz + Gustavo + G + + + Resendis-Antonio + Osbaldo + O + + + eng + + Journal Article + + + 2013 + 01 + 03 + +
+ + Switzerland + Front Physiol + 101549006 + 1664-042X + + + + Nucleic Acids Res. 2010 Sep;38(17):5735-45 + 20460455 + + + Nat Protoc. 2011 Sep;6(9):1290-307 + 21886097 + + + BMC Syst Biol. 2011;5:120 + 21801415 + + + Cancer Cell. 2012 Mar 20;21(3):297-308 + 22439925 + + + Mol Cancer. 2009;8:41 + 19558692 + + + J Proteomics. 2012 Dec 5;76 Spec No.:163-7 + 22982136 + + + Z Krebsforsch Klin Onkol Cancer Res Clin Oncol. 1976;87(2):115-26 + 136820 + + + Appl Environ Microbiol. 1994 Oct;60(10):3724-31 + 7986045 + + + Nat Rev Cancer. 2002 Jun;2(6):442-54 + 12189386 + + + Science. 1956 Feb 24;123(3191):309-14 + 13298683 + + + Proteomics. 2004 Sep;4(9):2789-95 + 15352252 + + + Nat Rev Microbiol. 2004 Nov;2(11):886-97 + 15494745 + + + Nat Rev Cancer. 2004 Nov;4(11):891-9 + 15516961 + + + Curr Opin Cell Biol. 2006 Dec;18(6):598-608 + 17046224 + + + Cell. 2008 Sep 5;134(5):703-7 + 18775299 + + + Nature. 2009 Feb 12;457(7231):910-4 + 19212411 + + + Nat Struct Mol Biol. 2009 Jun;16(6):670-6 + 19448617 + + + Cell. 2011 Mar 4;144(5):646-74 + 21376230 + + + PLoS One. 2012;7(5):e37231 + 22623999 + + + PLoS Comput Biol. 2012;8(10):e1002720 + 23071431 + + + J Biotechnol. 2012 Dec 31;162(2-3):336-45 + 23036926 + + + Trends Biochem Sci. 1999 Feb;24(2):68-72 + 10098401 + + + Am J Physiol Heart Circ Physiol. 2001 Jun;280(6):H2533-44 + 11356608 + + + Cancer Res. 2005 Jan 15;65(2):613-21 + 15695406 + + + Neoplasia. 2005 Apr;7(4):324-30 + 15967109 + + + Cancer Cell. 2005 Oct;8(4):311-21 + 16226706 + + + Cancer Cell. 2006 Jun;9(6):425-34 + 16766262 + + + J Bioenerg Biomembr. 2007 Jun;39(3):235-41 + 17665292 + + + PLoS Comput Biol. 2007 Oct;3(10):1887-95 + 17922569 + + + J Mol Med (Berl). 2007 Dec;85(12):1301-7 + 18026916 + + + J Biol Chem. 2008 Aug 15;283(33):22700-8 + 18541534 + + + Genes Dev. 2009 Mar 1;23(5):537-48 + 19270154 + + + Nat Biotechnol. 2010 Mar;28(3):245-8 + 20212490 + + + Biotechnol Bioeng. 2013 Feb;110(2):660-6 + 22991240 + + + Genome Biol. 2012;13(9):R78 + 23013597 + + + J Bacteriol. 2012 Feb;194(4):855-65 + 22139506 + + + Oncogene. 2006 Aug 7;25(34):4675-82 + 16892081 + + + Nature. 2008 Mar 13;452(7184):230-3 + 18337823 + + + PLoS One. 2010;5(8):e12383 + 20811631 + + + Hum Mol Genet. 2010 Oct 15;19(R2):R152-61 + 20729297 + + + PMC3539652 + + cancer metabolic phenotype + computational modeling of metabolism + constraint-based modeling + genome scale metabolic reconstruction + high throughput biology + + + + + + 2012 + 07 + 18 + + + 2012 + 12 + 09 + + + 2013 + 1 + 15 + 6 + 0 + + + 2013 + 1 + 15 + 6 + 0 + + + 2013 + 1 + 15 + 6 + 1 + + + epublish + + 23316163 + 10.3389/fphys.2012.00481 + PMC3539652 + + + + + + + + 23071431 + + 2012 + 10 + 16 + + + 2013 + 05 + 20 + + + 2015 + 02 + 22 + +
+ + 1553-7358 + + 8 + 10 + + 2012 + + + PLoS computational biology + PLoS Comput. Biol. + + Functional modules, structural topology, and optimal activity in metabolic networks. + + e1002720 + + 10.1371/journal.pcbi.1002720 + + Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions. + + + + Resendis-Antonio + Osbaldo + O + + Centro de Ciencias Genómicas-UNAM, Col. Chamilpa, Cuernavaca, Morelos, México. resendis@ccg.unam.mx + + + + Hernández + Magdalena + M + + + Mora + Yolanda + Y + + + Encarnación + Sergio + S + + + eng + + Journal Article + Research Support, Non-U.S. Gov't + + + 2012 + 10 + 11 + +
+ + United States + PLoS Comput Biol + 101238922 + 1553-734X + + + + 0 + Proteome + + + 7440-44-0 + Carbon + + + IM + + + Nature. 1999 Dec 2;402(6761 Suppl):C47-52 + 10591225 + + + BMC Syst Biol. 2011;5:120 + 21801415 + + + Science. 2002 Aug 30;297(5586):1551-5 + 12202830 + + + Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15112-7 + 12415116 + + + J Proteome Res. 2003 Sep-Oct;2(5):488-94 + 14582645 + + + Nature. 2004 Jul 1;430(6995):88-93 + 15190252 + + + Can J Microbiol. 1981 May;27(5):491-5 + 6265049 + + + J Bacteriol. 1994 Jun;176(11):3286-94 + 8195084 + + + Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8 + 9843981 + + + Trends Genet. 2005 Jan;21(1):16-20 + 15680508 + + + Methods Mol Biol. 2007;358:129-37 + 17035684 + + + BMC Syst Biol. 2007;1:24 + 17547772 + + + PLoS Comput Biol. 2007 Oct;3(10):1887-95 + 17922569 + + + Nat Biotechnol. 2008 Jun;26(6):659-67 + 18536691 + + + Nat Rev Microbiol. 2009 Feb;7(2):129-43 + 19116616 + + + Methods Mol Biol. 2009;541:145-60 + 19381526 + + + Mol Syst Biol. 2009;5:320 + 19888215 + + + Nat Biotechnol. 2010 Mar;28(3):245-8 + 20212490 + + + PLoS Comput Biol. 2010 Jun;6(6):e1000802 + 20532205 + + + Wiley Interdiscip Rev Syst Biol Med. 2010 May-Jun;2(3):372-82 + 20836035 + + + Nucleic Acids Res. 2000 Jan 1;28(1):27-30 + 10592173 + + + + + Carbon + metabolism + + + Metabolic Networks and Pathways + physiology + + + Metabolome + physiology + + + Metabolomics + methods + + + Models, Biological + + + Nitrogen Fixation + physiology + + + Phaseolus + microbiology + + + Phenotype + + + Proteome + + + Rhizobium etli + genetics + metabolism + + + Symbiosis + physiology + + + Systems Biology + methods + + + PMC3469419 + + + + + 2012 + 03 + 29 + + + 2012 + 08 + 16 + + + 2012 + 10 + 17 + 6 + 0 + + + 2012 + 10 + 17 + 6 + 0 + + + 2013 + 5 + 22 + 6 + 0 + + + ppublish + + 23071431 + 10.1371/journal.pcbi.1002720 + PCOMPBIOL-D-12-00518 + PMC3469419 + + + + + + + + 21801415 + + 2011 + 09 + 02 + + + 2011 + 12 + 19 + + + 2015 + 02 + 04 + +
+ + 1752-0509 + + 5 + + 2011 + Jul + 29 + + + BMC systems biology + BMC Syst Biol + + Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling. + + 120 + + 10.1186/1752-0509-5-120 + + Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process. + In this work we present a systems biology description of the metabolic activity in bacterial nitrogen fixation. This was accomplished by an integrative analysis involving high-throughput data and constraint-based modeling to characterize the metabolic activity in Rhizobium etli bacteroids located at the root nodules of Phaseolus vulgaris (bean plant). Proteome and transcriptome technologies led us to identify 415 proteins and 689 up-regulated genes that orchestrate this biological process. Taking into account these data, we: 1) extended the metabolic reconstruction reported for R. etli; 2) simulated the metabolic activity during symbiotic nitrogen fixation; and 3) evaluated the in silico results in terms of bacteria phenotype. Notably, constraint-based modeling simulated nitrogen fixation activity in such a way that 76.83% of the enzymes and 69.48% of the genes were experimentally justified. Finally, to further assess the predictive scope of the computational model, gene deletion analysis was carried out on nine metabolic enzymes. Our model concluded that an altered metabolic activity on these enzymes induced different effects in nitrogen fixation, all of these in qualitative agreement with observations made in R. etli and other Rhizobiaceas. + In this work we present a genome scale study of the metabolic activity in bacterial nitrogen fixation. This approach leads us to construct a computational model that serves as a guide for 1) integrating high-throughput data, 2) describing and predicting metabolic activity, and 3) designing experiments to explore the genotype-phenotype relationship in bacterial nitrogen fixation. + + + + Resendis-Antonio + Osbaldo + O + + Programa de Genomica Funcional de Procariotes, Centro de Ciencias Genómicas-UNAM, Av, Universidad s/n, Col, Chamilpa, Cuernavaca Morelos, C,P, 62210, Mexico. resendis@ccg.unam.mx + + + + Hernández + Magdalena + M + + + Salazar + Emmanuel + E + + + Contreras + Sandra + S + + + Batallar + Gabriel Martínez + GM + + + Mora + Yolanda + Y + + + Encarnación + Sergio + S + + + eng + + Journal Article + Research Support, Non-U.S. Gov't + + + 2011 + 07 + 29 + +
+ + England + BMC Syst Biol + 101301827 + 1752-0509 + + IM + + + Nucleic Acids Res. 2000 Jan 1;28(1):27-30 + 10592173 + + + Nat Rev Genet. 2010 Apr;11(4):297-307 + 20177425 + + + J Bacteriol. 2001 Apr;183(8):2595-604 + 11274120 + + + Mol Plant Microbe Interact. 2001 Jun;14(6):811-5 + 11386377 + + + Indian J Exp Biol. 2001 Apr;39(4):371-7 + 11491584 + + + Methods. 2001 Dec;25(4):402-8 + 11846609 + + + J Bacteriol. 2002 Apr;184(8):2287-95 + 11914361 + + + J Bacteriol. 2002 Aug;184(15):4071-80 + 12107123 + + + Mol Genet Genomics. 2002 Aug;267(6):820-8 + 12207230 + + + Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15112-7 + 12415116 + + + Indian J Exp Biol. 2002 Jul;40(7):755-64 + 12597544 + + + Microbiology. 2003 Jul;149(Pt 7):1807-18 + 12855732 + + + BMC Bioinformatics. 2003 Sep 11;4:41 + 12969510 + + + Metab Eng. 2003 Oct;5(4):264-76 + 14642354 + + + Mol Plant Microbe Interact. 2004 Mar;17(3):292-303 + 15000396 + + + J Bacteriol. 2004 Apr;186(8):2439-48 + 15060047 + + + Nature. 2004 May 6;429(6987):92-6 + 15129285 + + + Appl Environ Microbiol. 2004 Jun;70(6):3272-81 + 15184121 + + + Mol Plant Microbe Interact. 2004 Jul;17(7):720-8 + 15242166 + + + Nat Rev Microbiol. 2004 Aug;2(8):621-31 + 15263897 + + + J Bacteriol. 1992 Jul;174(14):4790-7 + 1320616 + + + J Bacteriol. 1994 Jun;176(11):3286-94 + 8195084 + + + J Bacteriol. 1995 Jun;177(11):3058-66 + 7768801 + + + J Bacteriol. 1996 Mar;178(6):1646-54 + 8626293 + + + J Bacteriol. 1996 Nov;178(21):6166-72 + 8892815 + + + Mol Microbiol. 1996 Sep;21(6):1239-52 + 8898392 + + + Science. 1997 Oct 24;278(5338):631-7 + 9381173 + + + FEMS Microbiol Rev. 1998 Jun;22(2):105-23 + 9729766 + + + Proc Natl Acad Sci U S A. 2004 Nov 23;101(47):16636-41 + 15542588 + + + Proteomics. 2005 Nov;5(16):4170-84 + 16254929 + + + Proc Natl Acad Sci U S A. 2006 Mar 7;103(10):3834-9 + 16505379 + + + Trends Microbiol. 2006 Apr;14(4):161-8 + 16520035 + + + Proteomics. 2006 May;6(10):3008-28 + 16688787 + + + Nat Protoc. 2007;2(3):727-38 + 17406635 + + + PLoS Comput Biol. 2007 Oct;3(10):1887-95 + 17922569 + + + J Bacteriol. 2007 Nov;189(22):8347-52 + 17827288 + + + Nat Biotechnol. 2008 Jun;26(6):659-67 + 18536691 + + + Mol Plant Microbe Interact. 2008 Sep;21(9):1232-41 + 18700827 + + + Science. 2008 Aug 15;321(5891):926-9 + 18703733 + + + Phytochemistry. 2008 Oct;69(13):2426-38 + 18757068 + + + Nat Rev Microbiol. 2009 Feb;7(2):129-43 + 19116616 + + + Nat Rev Microbiol. 2009 Apr;7(4):312-20 + 19270720 + + + Science. 2009 Sep 18;325(5947):1544-9 + 19762644 + + + Appl Environ Microbiol. 2010 Jul;76(13):4510-20 + 20453139 + + + Biotechniques. 2000 Sep;29(3):548-50, 552-4, 556 passim + 10997270 + + + + + Gene Expression Regulation, Bacterial + genetics + physiology + + + Mass Spectrometry + + + Metabolic Networks and Pathways + genetics + physiology + + + Microarray Analysis + + + Models, Biological + + + Nitrogen Fixation + genetics + physiology + + + Phaseolus + microbiology + + + Proteomics + methods + + + Rhizobium etli + genetics + metabolism + physiology + + + Root Nodules, Plant + microbiology + + + Systems Biology + methods + + + PMC3164627 + + + + + 2011 + 03 + 10 + + + 2011 + 07 + 29 + + + 2011 + 8 + 2 + 6 + 0 + + + 2011 + 8 + 2 + 6 + 0 + + + 2011 + 12 + 20 + 6 + 0 + + + epublish + + 21801415 + 1752-0509-5-120 + 10.1186/1752-0509-5-120 + PMC3164627 + + + + + + + + 21696634 + + 2011 + 08 + 10 + + + 2011 + 10 + 17 + + + 2015 + 02 + 04 + +
+ + 1752-0509 + + 5 + + 2011 + Jun + 22 + + + BMC systems biology + BMC Syst Biol + + Proteomic patterns of cervical cancer cell lines, a network perspective. + + 96 + + 10.1186/1752-0509-5-96 + + Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis. + With this study, we sought to achieve a systemic perspective of the common proteomic profile of six cervical cancer cell lines, both positive and negative for HPV, and which differ from the profile corresponding to the non-tumourgenic cell line, HaCaT. Our objectives were to identify common cellular events participating in cancer maintenance, as well as the establishment of a pipeline to work with proteomic-derived results. We analyzed by means of 2D SDS-PAGE and MALDI-TOF mass spectrometry the protein extracts of six cervical cancer cell lines, from which we identified a consensus of 66 proteins. We call this group of proteins, the "central core of cervical cancer". Starting from this core set of proteins, we acquired a PPI network that pointed, through topological analysis, to some proteins that may well be playing a central role in the neoplastic process, such as 14-3-3ζ. In silico overrepresentation analysis of transcription factors pointed to the overexpression of c-Myc, Max and E2F1 as key transcription factors involved in orchestrating the neoplastic phenotype. + Our findings show that there is a "central core of cervical cancer" protein expression pattern, and suggest that 14-3-3ζ is key to determine if the cell proliferates or dies. In addition, our bioinformatics analysis suggests that the neoplastic phenotype is governed by a non-canonical regulatory pathway. + + + + Higareda-Almaraz + Juan Carlos + JC + + Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Apdo, Postal 565-A, Cuernavaca, Morelos, CP 62210, México. + + + + Enríquez-Gasca + María del Rocío + Mdel R + + + Hernández-Ortiz + Magdalena + M + + + Resendis-Antonio + Osbaldo + O + + + Encarnación-Guevara + Sergio + S + + + eng + + Journal Article + Research Support, Non-U.S. Gov't + + + 2011 + 06 + 22 + +
+ + England + BMC Syst Biol + 101301827 + 1752-0509 + + + + 0 + 14-3-3 Proteins + + + IM + + + Nucleic Acids Res. 2007 Jan;35(Database issue):D137-40 + 17202159 + + + Int J Cancer. 2007 Jun 1;120(11):2331-8 + 17304502 + + + Nat Methods. 2007 Aug;4(8):651-7 + 17558387 + + + Oncogene. 2007 Sep 13;26(42):6194-202 + 17404573 + + + Nucleic Acids Res. 2008 Jan;36(Database issue):D190-5 + 18045787 + + + Cancer Res. 2008 Jan 15;68(2):498-505 + 18199545 + + + Oncogene. 2008 Feb 21;27(9):1315-9 + 17704798 + + + Cancer Res. 2008 Mar 15;68(6):1760-7 + 18339856 + + + Anticancer Res. 2008 Jan-Feb;28(1A):327-34 + 18383865 + + + Oncogene. 2008 Jul 10;27(30):4172-9 + 18345030 + + + Trends Biochem Sci. 2008 Aug;33(8):359-62 + 18603432 + + + J Cell Biochem. 2008 Oct 1;105(2):370-80 + 18636554 + + + Oncol Rep. 2008 Oct;20(4):751-60 + 18813814 + + + Nature. 2008 Sep 25;455(7212):552-6 + 18794899 + + + Nature. 2008 Sep 25;455(7212):479-80 + 18818647 + + + Genes Dev. 2008 Oct 15;22(20):2755-66 + 18923074 + + + J Virol. 2008 Dec;82(23):11568-76 + 18818322 + + + Nucleic Acids Res. 2009 Jan;37(Database issue):D623-8 + 18940869 + + + Trends Cell Biol. 2009 Jan;19(1):16-23 + 19027299 + + + BMC Bioinformatics. 2009;10 Suppl 1:S56 + 19208159 + + + Science. 2009 May 22;324(5930):1029-33 + 19460998 + + + Cancer Cell. 2009 Sep 8;16(3):195-207 + 19732720 + + + Bioinformatics. 2009 Nov 1;25(21):2857-9 + 19729372 + + + Nucleic Acids Res. 2010 Jan;38(Database issue):D532-9 + 19897547 + + + Nucleic Acids Res. 2010 Jan;38(Database issue):D613-9 + 19906737 + + + Nucleic Acids Res. 2010 Jan;38(Database issue):D620-5 + 19920125 + + + Carcinogenesis. 2010 Jan;31(1):2-8 + 19861649 + + + Biochem Biophys Res Commun. 2010 Jan 1;391(1):142-6 + 19900401 + + + BMC Bioinformatics. 2010;11:91 + 20163717 + + + Oncogene. 2010 Mar 11;29(10):1531-42 + 20010876 + + + Virology. 2010 May 25;401(1):70-9 + 20206957 + + + Mol Cells. 2010 Mar;29(3):305-9 + 20082218 + + + J Biol Chem. 2010 Apr 23;285(17):13121-30 + 20181946 + + + Cancer. 2010 Jun 1;116(11):2531-42 + 20310056 + + + Appl Environ Microbiol. 2010 Jul;76(13):4510-20 + 20453139 + + + Nat Rev Cancer. 2010 Aug;10(8):550-60 + 20592731 + + + Cancer Res. 2010 Aug 1;70(15):6216-24 + 20647324 + + + PLoS One. 2010;5(8):e12383 + 20811631 + + + Cell. 2011 Mar 4;144(5):646-74 + 21376230 + + + Nucleic Acids Res. 2000 Jan 1;28(1):27-30 + 10592173 + + + Electrophoresis. 1999 Dec;20(18):3551-67 + 10612281 + + + Cell. 2000 Jan 7;100(1):57-70 + 10647931 + + + Nat Genet. 2000 May;25(1):25-9 + 10802651 + + + Recent Prog Horm Res. 2001;56:127-55 + 11237210 + + + Cancer Lett. 2001 Jul 10;168(1):45-50 + 11368876 + + + Biochim Biophys Acta. 2001 Jun 11;1547(2):313-9 + 11410287 + + + Oncogene. 2001 Oct 1;20(44):6331-8 + 11607836 + + + Nucleic Acids Res. 2002 Jan 1;30(1):303-5 + 11752321 + + + J Biol Chem. 2002 Jan 25;277(4):2923-30 + 11713253 + + + Nat Rev Cancer. 2002 Apr;2(4):315-9 + 12001993 + + + Genome Res. 2002 Jun;12(6):996-1006 + 12045153 + + + Nucleic Acids Res. 2003 Jan 1;31(1):248-50 + 12519993 + + + N Engl J Med. 2003 Feb 6;348(6):518-27 + 12571259 + + + FASEB J. 2003 Apr;17(6):767-9 + 12594176 + + + Mol Cell. 2003 Apr;11(4):905-14 + 12718877 + + + J Biol Chem. 2003 Jun 6;278(23):20915-24 + 12665508 + + + Am J Epidemiol. 2003 Sep 1;158(5):486-94 + 12936904 + + + Genome Res. 2003 Nov;13(11):2498-504 + 14597658 + + + Oncogene. 2003 Dec 8;22(56):9007-21 + 14663479 + + + Nucleic Acids Res. 2004 Jan 1;32(Database issue):D262-6 + 14681408 + + + Nucleic Acids Res. 2004 Jan 1;32(Database issue):D497-501 + 14681466 + + + Cell. 2004 Mar 19;116(6):855-67 + 15035987 + + + Electrophoresis. 2004 May;25(9):1327-33 + 15174055 + + + Cell. 1990 Dec 21;63(6):1129-36 + 2175676 + + + Proc Natl Acad Sci U S A. 1991 Jul 1;88(13):5523-7 + 1648218 + + + Int J Cancer. 1992 Jul 9;51(5):831-4 + 1319412 + + + Cancer Res. 1996 Oct 15;56(20):4620-4 + 8840974 + + + Oncogene. 1999 May 13;18(19):2942-54 + 10378691 + + + Science. 1956 Feb 24;123(3191):309-14 + 13298683 + + + Proteomics. 2005 Apr;5(6):1481-93 + 15838902 + + + Breast Cancer Res. 2005;7(3):R365-73 + 15987432 + + + Nucleic Acids Res. 2006 Jan 1;34(Database issue):D535-9 + 16381927 + + + J Biol Chem. 2006 Feb 10;281(6):3283-9 + 16352593 + + + Bioinformatics. 2006 Sep 15;22(18):2291-7 + 16844706 + + + Cancer Res. 2006 Oct 15;66(20):9895-902 + 17047051 + + + Proc Natl Acad Sci U S A. 2006 Nov 21;103(47):17834-9 + 17093053 + + + Nucleic Acids Res. 2007 Jan;35(Database issue):D561-5 + 17145710 + + + + + 14-3-3 Proteins + chemistry + genetics + metabolism + + + Cell Line, Tumor + + + Databases, Protein + + + Electrophoresis, Polyacrylamide Gel + + + Female + + + Gene Expression Profiling + + + Humans + + + Molecular Weight + + + Protein Interaction Mapping + + + Proteomics + + + Uterine Cervical Neoplasms + metabolism + pathology + + + PMC3152905 + + + + + 2011 + 02 + 04 + + + 2011 + 06 + 22 + + + 2011 + 6 + 24 + 6 + 0 + + + 2011 + 6 + 24 + 6 + 0 + + + 2011 + 10 + 18 + 6 + 0 + + + epublish + + 21696634 + 1752-0509-5-96 + 10.1186/1752-0509-5-96 + PMC3152905 + + + + + + + + 20811631 + + 2010 + 09 + 02 + + + 2010 + 11 + 04 + + + 2014 + 12 + 02 + +
+ + 1932-6203 + + 5 + 8 + + 2010 + Aug + 25 + + + PloS one + PLoS ONE + + Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. + + e12383 + + 10.1371/journal.pone.0012383 + + Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. + This work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. + Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective. + + + + Resendis-Antonio + Osbaldo + O + + Centro de Ciencias Genomicas-Universidad Nacional Autónoma de México, Cuernavaca, México. resendis@ccg.unam.mx + + + + Checa + Alberto + A + + + Encarnación + Sergio + S + + + eng + + Journal Article + Research Support, Non-U.S. Gov't + + + 2010 + 08 + 25 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Cell. 2000 Jan 7;100(1):57-70 + 10647931 + + + Nat Protoc. 2010 Jan;5(1):93-121 + 20057383 + + + J Biol Chem. 2002 Aug 2;277(31):28058-64 + 12006566 + + + Nature. 2004 May 6;429(6987):92-6 + 15129285 + + + J Theor Biol. 2004 Jun 21;228(4):437-47 + 15178193 + + + Biophys J. 2004 Oct;87(4):2172-86 + 15454420 + + + Nat Rev Microbiol. 2004 Nov;2(11):886-97 + 15494745 + + + Appl Environ Microbiol. 1994 Oct;60(10):3724-31 + 7986045 + + + Trends Biochem Sci. 1999 Feb;24(2):68-72 + 10098401 + + + Nat Rev Cancer. 2004 Nov;4(11):891-9 + 15516961 + + + Nat Rev Genet. 2004 Nov;5(11):826-37 + 15520792 + + + Science. 1956 Feb 24;123(3191):309-14 + 13298683 + + + Cancer Res. 2005 Jan 15;65(2):613-21 + 15695406 + + + Cancer Cell. 2005 Oct;8(4):311-21 + 16226706 + + + Nat Rev Cancer. 2005 Nov;5(11):857-66 + 16327764 + + + Science. 2006 May 26;312(5777):1158-9 + 16728625 + + + Cancer Cell. 2006 Jun;9(6):425-34 + 16766262 + + + Science. 2006 Jun 16;312(5780):1650-3 + 16728594 + + + Clin Cancer Res. 2006 Oct 1;12(19):5632-40 + 17020965 + + + Int J Cancer. 2006 Dec 15;119(12):2733-41 + 17019714 + + + Curr Opin Cell Biol. 2006 Dec;18(6):598-608 + 17046224 + + + Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1777-82 + 17267599 + + + Nat Protoc. 2007;2(3):727-38 + 17406635 + + + PLoS Comput Biol. 2007 Oct;3(10):1887-95 + 17922569 + + + Proc Natl Acad Sci U S A. 2007 Dec 4;104(49):19345-50 + 18032601 + + + Nature. 2008 Mar 13;452(7184):230-3 + 18337823 + + + Curr Opin Genet Dev. 2008 Feb;18(1):54-61 + 18387799 + + + Nat Biotechnol. 2008 Jun;26(6):659-67 + 18536691 + + + J Biol Chem. 2008 Aug 15;283(33):22700-8 + 18541534 + + + Cell. 2008 Sep 5;134(5):703-7 + 18775299 + + + Nat Rev Microbiol. 2009 Feb;7(2):129-43 + 19116616 + + + Genes Dev. 2009 Mar 1;23(5):537-48 + 19270154 + + + PLoS One. 2009;4(3):e4967 + 19305506 + + + Science. 2009 May 22;324(5930):1029-33 + 19460998 + + + Science. 2009 Sep 18;325(5947):1544-9 + 19762644 + + + Nat Rev Cancer. 2009 Oct;9(10):691-700 + 19759539 + + + Cancer Res. 2010 Jan 1;70(1):12-3 + 20028868 + + + Carcinogenesis. 2010 Jan;31(1):2-8 + 19861649 + + + Nat Biotechnol. 2002 Mar;20(3):243-9 + 11875424 + + + + + Cell Proliferation + + + Citric Acid Cycle + + + Computational Biology + + + Glycolysis + + + HeLa Cells + + + Humans + + + Models, Biological + + + Neoplasms + metabolism + pathology + + + Oxidative Phosphorylation + + + Pentose Phosphate Pathway + + + Phenotype + + + PMC2928278 + + + + + 2010 + 03 + 17 + + + 2010 + 07 + 29 + + + 2010 + 9 + 3 + 6 + 0 + + + 2010 + 9 + 3 + 6 + 0 + + + 2010 + 11 + 5 + 6 + 0 + + + epublish + + 20811631 + 10.1371/journal.pone.0012383 + PMC2928278 + + + + + + + + 19305506 + + 2009 + 03 + 23 + + + 2009 + 04 + 24 + + + 2016 + 10 + 19 + +
+ + 1932-6203 + + 4 + 3 + + 2009 + + + PloS one + PLoS ONE + + Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking. + + e4967 + + 10.1371/journal.pone.0004967 + + Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network. + This paper presents a statistic framework capable to study how and how fast the metabolites participating in a perturbed metabolic network reach a steady-state. Instead of requiring accurate kinetic information, this approach uses high throughput metabolome technology to define a feasible kinetic library, which constitutes the base for identifying, statistical and dynamical properties during the relaxation. For the sake of illustration we have applied this approach to the human Red blood cell metabolism (hRBC) and its capacity to predict temporal phenomena was evaluated. Remarkable, the main dynamical properties obtained from a detailed kinetic model in hRBC were recovered by our statistical approach. Furthermore, robust properties in time scale and metabolite organization were identify and one concluded that they are a consequence of the combined performance of redundancies and variability in metabolite participation. + In this work we present an approach that integrates high throughput metabolome data to define the dynamic behavior of a slightly perturbed metabolic network where kinetic information is lacking. Having information of metabolite concentrations at steady-state, this method has significant relevance due its potential scope to analyze others genome scale metabolic reconstructions. Thus, I expect this approach will significantly contribute to explore the relationship between dynamic and physiology in other metabolic reconstructions, particularly those whose kinetic information is practically nulls. For instances, I envisage that this approach can be useful in genomic medicine or pharmacogenomics, where the estimation of time scales and the identification of metabolite organization may be crucial to characterize and identify (dis)functional stages. + + + + Resendis-Antonio + Osbaldo + O + + Center for Genomic Sciences-UNAM, Cuernavaca Morelos, Mexico. resendis@ccg.unam.mx + + + + eng + + + R01 GM068837 + GM + NIGMS NIH HHS + United States + + + GM68837 + GM + NIGMS NIH HHS + United States + + + + Journal Article + Research Support, N.I.H., Extramural + Research Support, Non-U.S. Gov't + + + 2009 + 03 + 23 + +
+ + United States + PLoS One + 101285081 + 1932-6203 + + IM + + + Bioinformatics. 2001 Mar;17(3):286-7 + 11294796 + + + Nature. 1997 Jun 26;387(6636):913-7 + 9202124 + + + Biophys J. 2002 Aug;83(2):646-62 + 12124254 + + + Biophys J. 2005 Mar;88(3):1616-25 + 15626710 + + + Nat Biotechnol. 2005 May;23(5):612-6 + 15821729 + + + PLoS Med. 2006 Jul;3(7):e209 + 16683861 + + + Proc Natl Acad Sci U S A. 2006 Aug 8;103(32):11868-73 + 16880395 + + + Nat Chem Biol. 2006 Oct;2(10):529-30 + 16936719 + + + J Nutr. 2007 Jan;137(1 Suppl):259S-266S + 17182837 + + + Trends Microbiol. 2007 Jan;15(1):45-50 + 17113776 + + + Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1777-82 + 17267599 + + + Nat Rev Drug Discov. 2007 Mar;6(3):202-10 + 17318209 + + + J Biol Chem. 2007 Apr 6;282(14):10731-41 + 17289676 + + + Nat Protoc. 2007;2(3):727-38 + 17406635 + + + Science. 2007 Apr 27;316(5824):593-7 + 17379776 + + + In Silico Biol. 2007;7(2 Suppl):S81-90 + 17822394 + + + In Silico Biol. 2007;7(2 Suppl):S37-44 + 17822389 + + + PLoS Comput Biol. 2007 Oct;3(10):1887-95 + 17922569 + + + Mol Syst Biol. 2007;3:146 + 18004279 + + + Theor Biol Med Model. 2005;2:18 + 15882454 + + + Biophys J. 2008 Mar 15;94(6):2065-81 + 18065471 + + + Mol Syst Biol. 2008;4:171 + 18319723 + + + Nat Biotechnol. 2008 Jun;26(6):659-67 + 18536691 + + + PLoS Comput Biol. 2008;4(9):e1000177 + 18787685 + + + Nat Rev Genet. 2008 Oct;9 Suppl 1:S23-7 + 18802418 + + + Curr Opin Biotechnol. 2004 Feb;15(1):50-1 + 15102466 + + + Trends Biotechnol. 2004 May;22(5):245-52 + 15109811 + + + Nature. 2004 May 6;429(6987):92-6 + 15129285 + + + J Theor Biol. 2004 Jun 21;228(4):437-47 + 15178193 + + + Cell. 2004 Sep 17;118(6):675-85 + 15369668 + + + Biophys J. 2004 Oct;87(4):2172-86 + 15454420 + + + Nat Rev Microbiol. 2004 Nov;2(11):886-97 + 15494745 + + + J Biol Chem. 1996 Apr 5;271(14):7927-41 + 8626472 + + + Nat Biotechnol. 2001 Jun;19(6):503-4 + 11385433 + + + + + Algorithms + + + Cluster Analysis + + + Computational Biology + methods + + + Erythrocytes + metabolism + + + Humans + + + Metabolic Networks and Pathways + + + Models, Biological + + + PMC2654918 + + + + + 2008 + 11 + 18 + + + 2009 + 02 + 10 + + + 2009 + 3 + 24 + 9 + 0 + + + 2009 + 3 + 24 + 9 + 0 + + + 2009 + 4 + 25 + 9 + 0 + + + ppublish + + 19305506 + 10.1371/journal.pone.0004967 + PMC2654918 + + + + + + + + 19076632 + + 2008 + 12 + 16 + + + 2009 + 02 + 19 + + + 2016 + 10 + 19 + +
+ + 0168-6445 + + 33 + 1 + + 2009 + Jan + + + FEMS microbiology reviews + FEMS Microbiol. Rev. + + Regulation by transcription factors in bacteria: beyond description. + + 133-51 + + 10.1111/j.1574-6976.2008.00145.x + + Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema. + + + + Balleza + Enrique + E + + Programa de Genómica Computacional, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Morelos, Mexico. + + + + López-Bojorquez + Lucia N + LN + + + Martínez-Antonio + Agustino + A + + + Resendis-Antonio + Osbaldo + O + + + Lozada-Chávez + Irma + I + + + Balderas-Martínez + Yalbi I + YI + + + Encarnación + Sergio + S + + + Collado-Vides + Julio + J + + + eng + + + R01 GM071962 + GM + NIGMS NIH HHS + United States + + + R01 GM071962-05 + GM + NIGMS NIH HHS + United States + + + + Journal Article + Research Support, N.I.H., Extramural + Research Support, Non-U.S. Gov't + Review + +
+ + England + FEMS Microbiol Rev + 8902526 + 0168-6445 + + + + 0 + Bacterial Proteins + + + 0 + Transcription Factors + + + IM + + + Proc Natl Acad Sci U S A. 2008 Feb 12;105(6):1897-900 + 18250330 + + + Mol Biol Evol. 2008 Mar;25(3):559-67 + 18158322 + + + BMC Syst Biol. 2008;2:18 + 18284674 + + + BMC Genomics. 2006;7:24 + 16478536 + + + Mol Cell. 2008 Mar 28;29(6):786-92 + 18374652 + + + Proc Natl Acad Sci U S A. 2008 Apr 1;105(13):5081-6 + 18353986 + + + Nature. 2008 Apr 17;452(7189):840-5 + 18421347 + + + BMC Microbiol. 2008;8:60 + 18405378 + + + J Mol Biol. 2008 Jun 6;379(3):627-43 + 18466918 + + + Proc Natl Acad Sci U S A. 1985 Dec;82(23):7989-93 + 2999789 + + + J Mol Biol. 1987 Nov 20;198(2):311-26 + 2828639 + + + Nucleic Acids Res. 1988 Mar 25;16(5):1879-902 + 3282223 + + + Microbiol Rev. 1991 Sep;55(3):371-94 + 1943993 + + + Cell. 1992 Oct 16;71(2):267-76 + 1423594 + + + Science. 1995 Oct 20;270(5235):467-70 + 7569999 + + + Proc Int Conf Intell Syst Mol Biol. 1994;2:28-36 + 7584402 + + + Nat Genet. 1996 Dec;14(4):457-60 + 8944026 + + + Genes Dev. 2006 Jul 1;20(13):1776-89 + 16818608 + + + J Biol Chem. 1959 Sep;234:2351-8 + 14405566 + + + C R Hebd Seances Acad Sci. 1959 Oct 5;249:1282-4 + 14406327 + + + Biochem Biophys Res Commun. 2004 Dec 17;325(3):983-90 + 15541386 + + + Genome Biol. 2004;5(12):252 + 15575978 + + + Nat Biotechnol. 2005 Jan;23(1):137-44 + 15637633 + + + Trends Genet. 2005 Jan;21(1):16-20 + 15680508 + + + Trends Microbiol. 2005 Feb;13(2):52-6 + 15680762 + + + J Bacteriol. 2005 Mar;187(5):1591-603 + 15716429 + + + Proc Natl Acad Sci U S A. 2005 Mar 22;102(12):4470-5 + 15728384 + + + FEMS Microbiol Rev. 2005 Apr;29(2):231-62 + 15808743 + + + Trends Microbiol. 2005 May;13(5):221-8 + 15866039 + + + Bioinformatics. 2005 May 15;21(10):2563-5 + 15722376 + + + Curr Opin Biotechnol. 2005 Jun;16(3):329-35 + 15961034 + + + Trends Genet. 2005 Jul;21(7):385-9 + 15949864 + + + J Biol. 2005;4(2):6 + 15982408 + + + Pharmacogenomics. 2005 Jun;6(4):373-82 + 16004555 + + + Nature. 2005 Sep 15;437(7057):376-80 + 16056220 + + + Nucleic Acids Res. 2006;34(12):3434-45 + 16840530 + + + Science. 2006 Jul 14;313(5784):236-8 + 16763111 + + + BMC Genomics. 2006;7:147 + 16772031 + + + Nat Biotechnol. 2006 Aug;24(8):1027-32 + 16845378 + + + Nat Struct Mol Biol. 2006 Sep;13(9):806-14 + 16892065 + + + J Theor Biol. 2004 Oct 7;230(3):301-12 + 15302540 + + + Mol Cell Proteomics. 2004 Aug;3(8):780-7 + 15115803 + + + Nature. 1969 Jan 4;221(5175):43-6 + 4882047 + + + J Theor Biol. 1969 Mar;22(3):437-67 + 5803332 + + + J Theor Biol. 1973 Apr;39(1):103-29 + 4741704 + + + Nature. 1974 Dec 13;252(5484):546-9 + 4431516 + + + Proc Natl Acad Sci U S A. 1975 Mar;72(3):784-8 + 1093168 + + + Biochemistry. 1975 Nov 18;14(23):5074-9 + 1103966 + + + Nature. 1981 Apr 30;290(5809):744-9 + 6261152 + + + J Theor Biol. 1984 Nov 21;111(2):273-302 + 6513572 + + + J Theor Biol. 1984 Nov 21;111(2):303-21 + 6513573 + + + J Mol Biol. 1985 Jan 20;181(2):211-30 + 3157005 + + + Nucleic Acids Res. 2003 Mar 15;31(6):1753-64 + 12626717 + + + Curr Opin Microbiol. 2003 Apr;6(2):125-34 + 12732301 + + + Nat Genet. 2003 Jun;34(2):166-76 + 12740579 + + + J Theor Biol. 2003 Jul 7;223(1):1-18 + 12782112 + + + Cell. 2003 May 30;113(5):597-607 + 12787501 + + + Trends Microbiol. 2003 Jun;11(6):248-53 + 12823939 + + + Trends Genet. 2003 Sep;19(9):479-84 + 12957540 + + + Annu Rev Biomed Eng. 2003;5:179-206 + 14527313 + + + Microbiol Mol Biol Rev. 2006 Sep;70(3):830-56 + 16959970 + + + Curr Opin Microbiol. 2006 Oct;9(5):511-9 + 16942903 + + + J Biol Chem. 2006 Oct 6;281(40):29872-85 + 16857666 + + + Proc Natl Acad Sci U S A. 2006 Oct 3;103(40):14724-31 + 17003135 + + + Nat Biotechnol. 2006 Oct;24(10):1235-40 + 17033664 + + + Nucleic Acids Res. 2006;34(16):4642-52 + 16963779 + + + J Mol Biol. 2006 Nov 17;364(1):1-8 + 17007876 + + + J Struct Biol. 2006 Nov;156(2):255-61 + 16697220 + + + J Struct Biol. 2006 Nov;156(2):262-72 + 16879983 + + + J Mol Biol. 2006 Dec 8;364(4):777-98 + 17045294 + + + Microbiol Mol Biol Rev. 2006 Dec;70(4):910-38 + 17158704 + + + Mol Cell. 2006 Dec 8;24(5):747-57 + 17157257 + + + Nucleic Acids Res. 2007 Jan;35(Database issue):D407-12 + 17142223 + + + Nucleic Acids Res. 2007 Jan;35(Database issue):D766-70 + 17182626 + + + Trends Microbiol. 2007 Jan;15(1):45-50 + 17113776 + + + Plant Cell. 2007 Nov;19(11):3327-38 + 18055607 + + + Genome Res. 2008 Jan;18(1):148-60 + 18032729 + + + Mol Microbiol. 2008 Jan;67(2):254-63 + 18047568 + + + Nucleic Acids Res. 2008 Jan;36(Database issue):D866-70 + 17932051 + + + Nucleic Acids Res. 2008 Jan;36(Database issue):D93-6 + 17962296 + + + Nucleic Acids Res. 2008 Jan;36(Database issue):D480-4 + 18077471 + + + Nucleic Acids Res. 2008 Jan;36(Database issue):D120-4 + 18158297 + + + Genome Res. 2002 Oct;12(10):1507-16 + 12368242 + + + Genome Res. 2002 Oct;12(10):1523-32 + 12368244 + + + Bioinformatics. 2002;18 Suppl 2:S231-40 + 12386007 + + + Curr Opin Microbiol. 2003 Oct;6(5):482-9 + 14572541 + + + Genome Res. 2003 Nov;13(11):2435-43 + 14597655 + + + Nat Rev Genet. 2004 Feb;5(2):101-13 + 14735121 + + + Nature. 2004 Jan 29;427(6973):415-8 + 14749823 + + + Proc Natl Acad Sci U S A. 2004 Feb 24;101(8):2299-304 + 14983004 + + + Proc Natl Acad Sci U S A. 2004 Feb 24;101(8):2524-9 + 14983042 + + + Mol Microbiol. 2007 Feb;63(4):1131-44 + 17233826 + + + Mol Microbiol. 2007 Mar;63(5):1296-306 + 17302812 + + + J Mol Biol. 2007 Mar 30;367(3):731-40 + 17276457 + + + J Theor Biol. 2007 Apr 7;245(3):433-48 + 17188715 + + + Science. 2007 Mar 23;315(5819):1716-9 + 17379809 + + + J Mol Biol. 2007 Apr 20;368(1):263-72 + 17321548 + + + Proc Natl Acad Sci U S A. 2007 Mar 27;104(13):5516-20 + 17372223 + + + PLoS Biol. 2007 Jan;5(1):e8 + 17214507 + + + Proc Natl Acad Sci U S A. 1997 Feb 4;94(3):814-9 + 9023339 + + + Nature. 1997 Sep 18;389(6648):251-60 + 9305837 + + + Nucleic Acids Res. 1998 Jan 1;26(1):55-9 + 9399800 + + + Nat Biotechnol. 1996 Dec;14(13):1675-80 + 9634850 + + + Annu Rev Biophys Biomol Struct. 1998;27:199-224 + 9646867 + + + Pac Symp Biocomput. 1998;:77-88 + 9697173 + + + J Theor Biol. 1998 Jul 27;193(2):307-19 + 9714934 + + + Nat Chem Biol. 2006 Feb;2(2):87-94 + 16415859 + + + Genome Res. 2006 Mar;16(3):405-13 + 16449500 + + + Bioinformatics. 2006 Mar 15;22(6):645-50 + 16046493 + + + Science. 2006 Mar 17;311(5767):1600-3 + 16543458 + + + Nature. 2006 Mar 23;440(7083):545-50 + 16554821 + + + BMC Bioinformatics. 2006;7:113 + 16522208 + + + J Mol Biol. 2006 Apr 28;358(2):614-33 + 16530225 + + + Proc Natl Acad Sci U S A. 2006 May 9;103(19):7304-9 + 16648266 + + + Proc Natl Acad Sci U S A. 2006 May 9;103(19):7276-81 + 16670199 + + + EMBO Rep. 2006 Jul;7(7):710-5 + 16799466 + + + BMC Bioinformatics. 2007;8 Suppl 6:S4 + 17903285 + + + Trends Genet. 2007 Oct;23(10):488-93 + 17692992 + + + PLoS Comput Biol. 2007 Sep;3(9):1739-50 + 17845071 + + + PLoS Comput Biol. 2007 Oct;3(10):1887-95 + 17922569 + + + Mol Syst Biol. 2007;3:145 + 18004278 + + + Ann N Y Acad Sci. 2007 Dec;1115:51-72 + 17925348 + + + Nucleic Acids Res. 2007;35(20):6963-72 + 17933780 + + + Cell. 2007 Dec 28;131(7):1354-65 + 18160043 + + + Genome Res. 2008 Jun;18(6):900-10 + 18340041 + + + BMC Bioinformatics. 2008;9:233 + 18471287 + + + Science. 2008 Jun 6;320(5881):1344-9 + 18451266 + + + PLoS One. 2008;3(6):e2456 + 18560561 + + + Nucleic Acids Res. 2008 Jun;36(11):3570-8 + 18453629 + + + J Mol Biol. 2008 Aug 1;381(1):238-47 + 18599074 + + + Genome Res. 2008 Sep;18(9):1509-17 + 18550803 + + + Bioinformatics. 2008 Sep 15;24(18):2044-50 + 18621757 + + + Genome Biol. 2008;9(1):R4 + 18179685 + + + Biophys J. 2004 Mar;86(3):1357-72 + 14990466 + + + Nat Rev Microbiol. 2004 Jan;2(1):57-65 + 15035009 + + + PLoS Biol. 2003 Oct;1(1):E15 + 14551912 + + + Curr Opin Microbiol. 2004 Apr;7(2):198-202 + 15063859 + + + Cell. 2004 Apr 16;117(2):185-98 + 15084257 + + + Nat Rev Microbiol. 2004 May;2(5):391-400 + 15100692 + + + Annu Rev Microbiol. 1998;52:231-86 + 9891799 + + + Biosystems. 1999 Apr;50(1):49-59 + 10235650 + + + J Bacteriol. 1999 Oct;181(20):6361-70 + 10515926 + + + J Mol Biol. 1961 Jun;3:318-56 + 13718526 + + + J Mol Biol. 1964 Mar;8:365-70 + 14168690 + + + J Mol Biol. 1965 Jan;11:90-6 + 14255764 + + + Microbiology. 2004 Nov;150(Pt 11):3783-95 + 15528664 + + + IET Syst Biol. 2007 Mar;1(2):61-77 + 17441550 + + + Breast Dis. 2006-2007;26:27-54 + 17473364 + + + Nat Rev Genet. 2007 Jun;8(6):450-61 + 17510665 + + + Nature. 2007 May 24;447(7143):396-8 + 17522671 + + + Science. 2007 May 25;316(5828):1191-4 + 17525339 + + + Metab Eng. 2007 May;9(3):258-67 + 17292651 + + + J Bacteriol. 2007 Jun;189(12):4384-90 + 17416654 + + + Science. 2007 Jun 8;316(5830):1441-2 + 17556576 + + + BMC Genomics. 2007;8:143 + 17547756 + + + FEBS Lett. 2007 Jul 24;581(18):3499-506 + 17617412 + + + Science. 2007 Jul 27;317(5837):526-9 + 17569828 + + + Math Biosci. 2007 Aug;208(2):454-68 + 17300817 + + + Chem Rev. 2007 Aug;107(8):3467-97 + 17636889 + + + PLoS Comput Biol. 2006 Dec 15;2(12):e163 + 17173478 + + + Proc Natl Acad Sci U S A. 2007 Aug 28;104(35):13948-53 + 17709750 + + + J Mol Biol. 2007 Sep 28;372(4):1108-22 + 17706247 + + + Genes Dev. 2007 Sep 15;21(18):2271-6 + 17875664 + + + Science. 2005 Sep 23;309(5743):2010-3 + 16179466 + + + Nature. 2005 Sep 29;437(7059):699-706 + 16170311 + + + Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50 + 16199517 + + + J Cell Sci. 2005 Nov 1;118(Pt 21):4947-57 + 16254242 + + + Genes Dev. 2005 Nov 1;19(21):2619-30 + 16264194 + + + Bioinformatics. 2005 Nov 15;21(22):4187-9 + 16109747 + + + Proc Natl Acad Sci U S A. 2005 Dec 6;102(49):17693-8 + 16301522 + + + Microbiol Mol Biol Rev. 2005 Dec;69(4):527-43 + 16339734 + + + Trends Microbiol. 2006 Jan;14(1):22-7 + 16311037 + + + Science. 2002 Oct 25;298(5594):799-804 + 12399584 + + + Science. 2002 Oct 25;298(5594):824-7 + 12399590 + + + Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15112-7 + 12415116 + + + Nat Genet. 2002 Dec;32 Suppl:496-501 + 12454644 + + + Proc Natl Acad Sci U S A. 2002 Dec 10;99 Suppl 4:16470-6 + 12202745 + + + Genome Biol. 2003;4(1):203 + 12540296 + + + Trends Genet. 2003 Feb;19(2):75-9 + 12547514 + + + Nucleic Acids Res. 2003 Feb 15;31(4):1234-44 + 12582243 + + + Nat Genet. 2004 May;36(5):492-6 + 15107850 + + + Nature. 2004 May 6;429(6987):92-6 + 15129285 + + + Genome Res. 2004 Jun;14(6):1107-18 + 15173116 + + + Microbiology. 2004 Jun;150(Pt 6):1609-27 + 15184548 + + + Genes Dev. 2004 Jul 15;18(14):1766-79 + 15256503 + + + Nature. 2000 Jan 20;403(6767):335-8 + 10659856 + + + Nature. 2000 Jan 20;403(6767):339-42 + 10659857 + + + Bull Math Biol. 2000 Mar;62(2):247-92 + 10824430 + + + Nature. 2000 Jun 1;405(6786):520-1 + 10850696 + + + Nucleic Acids Res. 2000 Sep 15;28(18):3497-503 + 10982868 + + + Science. 2000 Dec 22;290(5500):2306-9 + 11125145 + + + Genome Biol. 2001;2(4):RESEARCH0013 + 11305941 + + + J Biol Chem. 2001 Mar 16;276(11):8165-72 + 11062240 + + + J Bacteriol. 2001 Jul;183(13):4004-11 + 11395465 + + + Infect Immun. 2001 Dec;69(12):7197-204 + 11705888 + + + Bioinformatics. 2001 Dec;17(12):1183-97 + 11751227 + + + Genome Res. 2002 Feb;12(2):298-308 + 11827949 + + + J Comput Biol. 2002;9(1):67-103 + 11911796 + + + Nat Genet. 2002 May;31(1):69-73 + 11967532 + + + Nat Genet. 2002 May;31(1):60-3 + 11967534 + + + Nat Genet. 2002 May;31(1):64-8 + 11967538 + + + Microbiology. 2002 May;148(Pt 5):1561-9 + 11988531 + + + Science. 2002 May 24;296(5572):1466-70 + 12029133 + + + Mol Microbiol. 2002 Jun;44(6):1611-24 + 12067348 + + + Adv Microb Physiol. 2002;46:47-110 + 12073657 + + + Bioessays. 2002 Jun;24(6):512-8 + 12111734 + + + Trends Genet. 2002 Aug;18(8):395-8 + 12142007 + + + Nat Genet. 2002 Aug;31(4):370-7 + 12134151 + + + Science. 2002 Aug 16;297(5584):1183-6 + 12183631 + + + Science. 2002 Aug 30;297(5586):1562-6 + 12202833 + + + + + Bacteria + genetics + metabolism + + + Bacterial Proteins + genetics + metabolism + + + Evolution, Molecular + + + Gene Expression Regulation, Bacterial + + + Transcription Factors + genetics + metabolism + + + Transcription, Genetic + + + 228 + PMC2704942 + + + + + 2008 + 12 + 17 + 9 + 0 + + + 2008 + 12 + 17 + 9 + 0 + + + 2009 + 2 + 20 + 9 + 0 + + + ppublish + + 19076632 + FMR145 + 10.1111/j.1574-6976.2008.00145.x + PMC2704942 + + + + + + + + 17922569 + + 2007 + 10 + 30 + + + 2007 + 12 + 13 + + + 2014 + 09 + 04 + +
+ + 1553-7358 + + 3 + 10 + + 2007 + Oct + + + PLoS computational biology + PLoS Comput. Biol. + + Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli. + + 1887-95 + + + Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation. + + + + Resendis-Antonio + Osbaldo + O + + Bioengineering Department, University of California San Diego, La Jolla, California, USA. + + + + Reed + Jennifer L + JL + + + Encarnación + Sergio + S + + + Collado-Vides + Julio + J + + + Palsson + Bernhard Ø + BØ + + + eng + + Journal Article + Research Support, Non-U.S. Gov't + + + 2007 + 08 + 17 + +
+ + United States + PLoS Comput Biol + 101238922 + 1553-734X + + IM + + + Microbiology. 2000 Jul;146 ( Pt 7):1627-37 + 10878127 + + + Curr Opin Biotechnol. 2006 Oct;17(5):488-92 + 16934450 + + + Cell Mol Life Sci. 2001 Jan;58(1):61-71 + 11229817 + + + J Bacteriol. 2001 Feb;183(3):854-64 + 11208782 + + + J Bacteriol. 2001 Apr;183(8):2595-604 + 11274120 + + + Mol Plant Microbe Interact. 2001 Aug;14(8):1016-25 + 11497462 + + + J Bacteriol. 2001 Dec;183(24):6999-7006 + 11717256 + + + Biotechnol Bioeng. 2002 Jan 5;77(1):27-36 + 11745171 + + + Environ Microbiol. 2002 Mar;4(3):133-40 + 12000313 + + + Microbiol Mol Biol Rev. 2002 Jun;66(2):203-22 + 12040124 + + + Mol Plant Microbe Interact. 2002 Jun;15(6):598-607 + 12059108 + + + Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15112-7 + 12415116 + + + Nature. 2003 Apr 17;422(6933):722-6 + 12700763 + + + Metab Eng. 2003 Oct;5(4):264-76 + 14642354 + + + Curr Opin Biotechnol. 2004 Feb;15(1):50-1 + 15102466 + + + Science. 2004 Jun 11;304(5677):1627-9 + 15192217 + + + Mol Plant Microbe Interact. 2004 Jul;17(7):720-8 + 15242166 + + + Nat Rev Microbiol. 2004 Aug;2(8):621-31 + 15263897 + + + Nat Rev Microbiol. 2004 Nov;2(11):886-97 + 15494745 + + + J Biol Chem. 1974 Jul 10;249(13):4057-66 + 4859329 + + + Proc Biol Sci. 1992 Aug 22;249(1325):143-8 + 1360674 + + + Appl Environ Microbiol. 1994 Oct;60(10):3724-31 + 7986045 + + + Antonie Van Leeuwenhoek. 1994;66(1-3):129-50 + 7747928 + + + Mol Plant Microbe Interact. 1995 Jul-Aug;8(4):584-92 + 8589413 + + + J Bacteriol. 1996 Mar;178(6):1646-54 + 8626293 + + + J Bacteriol. 1996 Oct;178(20):5960-70 + 8830693 + + + J Bacteriol. 1997 Dec;179(23):7403-9 + 9393705 + + + FEMS Microbiol Lett. 1997 Dec 15;157(2):301-6 + 9435112 + + + FEMS Microbiol Rev. 1998 Jun;22(2):105-23 + 9729766 + + + Mol Plant Microbe Interact. 2005 Jan;18(1):67-74 + 15672820 + + + FEMS Microbiol Lett. 2005 May 15;246(2):159-65 + 15899401 + + + Proteomics. 2005 Nov;5(16):4170-84 + 16254929 + + + Nucleic Acids Res. 2006 Jan 1;34(Database issue):D354-7 + 16381885 + + + Nat Rev Genet. 2006 Feb;7(2):130-41 + 16418748 + + + Nat Rev Mol Cell Biol. 2006 Mar;7(3):198-210 + 16496022 + + + Proc Natl Acad Sci U S A. 2006 Mar 7;103(10):3834-9 + 16505379 + + + Trends Microbiol. 2006 Apr;14(4):161-8 + 16520035 + + + Proteomics. 2006 May;6(10):3008-28 + 16688787 + + + Appl Microbiol Biotechnol. 2006 Jul;71(4):377-86 + 16703322 + + + Nat Biotechnol. 2001 Feb;19(2):125-30 + 11175725 + + + + + Gene Expression Regulation, Bacterial + physiology + + + Genome, Bacterial + physiology + + + Metabolic Networks and Pathways + + + Models, Biological + + + Nitrogen Fixation + genetics + + + Rhizobium etli + genetics + metabolism + + + Symbiosis + + + PMC2000972 + + + + + 2007 + 02 + 20 + + + 2007 + 08 + 17 + + + 2007 + 10 + 10 + 9 + 0 + + + 2007 + 12 + 14 + 9 + 0 + + + 2007 + 10 + 10 + 9 + 0 + + + ppublish + + 17922569 + 07-PLCB-RA-0091 + 10.1371/journal.pcbi.0030192 + PMC2000972 + + + + + + + + 15680508 + + 2005 + 01 + 31 + + + 2005 + 04 + 12 + + + 2007 + 11 + 14 + +
+ + 0168-9525 + + 21 + 1 + + 2005 + Jan + + + Trends in genetics : TIG + Trends Genet. + + Modular analysis of the transcriptional regulatory network of E. coli. + + 16-20 + + + The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules. + + + + Resendis-Antonio + Osbaldo + O + + Program of Computational Genomics, Nitrogen Fixation Research Center, Universidad Nacional Autónoma de México, Ave Universidad s/n, Col Chamilpa, Cuernavaca, Morelos 62100 México. + + + + Freyre-González + Julio A + JA + + + Menchaca-Méndez + Ricardo + R + + + Gutiérrez-Ríos + Rosa M + RM + + + Martínez-Antonio + Agustino + A + + + Avila-Sánchez + Cristhian + C + + + Collado-Vides + Julio + J + + + eng + + + GM 62205-03 + GM + NIGMS NIH HHS + United States + + + + Journal Article + Research Support, Non-U.S. Gov't + Research Support, U.S. Gov't, P.H.S. + +
+ + England + Trends Genet + 8507085 + 0168-9525 + + IM + + + Escherichia coli + genetics + + + Gene Expression Regulation, Bacterial + + + Models, Genetic + + + Phylogeny + + + Transcription, Genetic + + + + + + + 2005 + 2 + 1 + 9 + 0 + + + 2005 + 4 + 13 + 9 + 0 + + + 2005 + 2 + 1 + 9 + 0 + + + ppublish + + 15680508 + S0168-9525(04)00312-9 + 10.1016/j.tig.2004.11.010 + + + + diff --git a/categories/index.xml b/categories/index.xml deleted file mode 100644 index 5b4b346..0000000 --- a/categories/index.xml +++ /dev/null @@ -1 +0,0 @@ -Categories on Webpage of the Resendis Labhttps://resendislab.github.io/categories/Recent content in Categories on Webpage of the Resendis LabHugo -- gohugo.ioen-usTue, 06 Dec 2016 09:19:57 -0600sciencehttps://resendislab.github.io/categories/science/Tue, 06 Dec 2016 09:19:57 -0600https://resendislab.github.io/categories/science/tutorialhttps://resendislab.github.io/categories/tutorial/Tue, 06 Dec 2016 09:19:57 -0600https://resendislab.github.io/categories/tutorial/ \ No newline at end of file diff --git a/categories/science/index.xml b/categories/science/index.xml deleted file mode 100644 index 4458144..0000000 --- a/categories/science/index.xml +++ /dev/null @@ -1,3 +0,0 @@ -science on Webpage of the Resendis Labhttps://resendislab.github.io/categories/science/Recent content in science on Webpage of the Resendis LabHugo -- gohugo.ioen-usTue, 06 Dec 2016 09:19:57 -0600Hola!!!https://resendislab.github.io/posts/test/Tue, 06 Dec 2016 09:19:57 -0600https://resendislab.github.io/posts/test/<h2 id="this-is-an-example-post">This is an example post</h2> -<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> -<p>This is my text now grrrr :)</p> \ No newline at end of file diff --git a/categories/tutorial/index.xml b/categories/tutorial/index.xml deleted file mode 100644 index 21d8d6b..0000000 --- a/categories/tutorial/index.xml +++ /dev/null @@ -1,3 +0,0 @@ -tutorial on Webpage of the Resendis Labhttps://resendislab.github.io/categories/tutorial/Recent content in tutorial on Webpage of the Resendis LabHugo -- gohugo.ioen-usTue, 06 Dec 2016 09:19:57 -0600Hola!!!https://resendislab.github.io/posts/test/Tue, 06 Dec 2016 09:19:57 -0600https://resendislab.github.io/posts/test/<h2 id="this-is-an-example-post">This is an example post</h2> -<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> -<p>This is my text now grrrr :)</p> \ No newline at end of file diff --git a/config.toml b/config.toml new file mode 100644 index 0000000..68a67e2 --- /dev/null +++ b/config.toml @@ -0,0 +1,11 @@ +baseurl = "https://resendislab.github.io" +languageCode = "en-us" +title = "Webpage of the Resendis Lab" +relativeURLs = true + +[params] + mdlversion = "1.3.0" + mdltheme = "indigo-green" + +[markup.goldmark.renderer] + unsafe= true diff --git a/.nojekyll b/content/about/.Rhistory similarity index 100% rename from .nojekyll rename to content/about/.Rhistory diff --git a/content/about/contact.md b/content/about/contact.md new file mode 100644 index 0000000..8427809 --- /dev/null +++ b/content/about/contact.md @@ -0,0 +1,18 @@ +--- +title: "Contact" +date: "1900-01-01T00:00:00" +--- + +# Directions + +
+ +**Osbaldo Resendis-Antonio, PhD**
+Laboratory in Systems Biology and Human Diseases
+Associated Professor
+[Instituto Nacional de Medicina Genomica – INMEGEN](https://google.com/maps/place/inmegen)
+Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX
+Phone: +52 55 5350 1900 - Ext.1198

+ + + diff --git a/content/about/we.md b/content/about/we.md new file mode 100644 index 0000000..7183dcf --- /dev/null +++ b/content/about/we.md @@ -0,0 +1,25 @@ ++++ +date = "2016-12-05T14:48:16-06:00" +title = "Who we are" ++++ + +Welcome to the webpage of the Human Systems Biology group in the National +Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is +interdisciplinary and have the objective to develop a systems biology framework +to analyze mainly human diseases and metabolic phenotype in microorganisms +through the use of computational models and high-throughput technologies. + +![group photo](/media/group.jpg) + +Currently, our laboratory focuses on the analysis of metabolic alterations in +cancer cells by the implementation of genome scale metabolic reconstructions and +assess the predictions in terms of experimental data at different scales. We +have developed some approaches for modeling cancer metabolism and currently we +are developing computational schemes with capacities to integrate metabolome and +RNA-seq data for elucidating metabolic mechanism in cancer cell lines and +tissues. + +Lastly, our laboratory is leading efforts to test the utility of computational +schemes to explore themes related with cancer studies, such as the influence of +microbiome in cancer, the study of the biological networks regulating the +epithelial messenchymal transition and tumor heterogeneity in cancer. diff --git a/content/events/biological_physics.md b/content/events/biological_physics.md new file mode 100644 index 0000000..d108e2b --- /dev/null +++ b/content/events/biological_physics.md @@ -0,0 +1,36 @@ ++++ +place = "Centro de Ciencias de la Complejidad" +maps = "https://goo.gl/maps/3RAyTdDrTBH2" +title = "Biological Physics Mexico City 2017" +end = "2017-05-19" +date = "2017-05-17" +webpage = "https://sites.google.com/site/biologicalphysicsmexico2017/" + ++++ + +Frontiers at the interface of Physics, Math and Biology. +======================================================== + +This conference (the second in a series) is intended as an international, +multidisciplinary scientific forum to discuss the latest developments in +biological physics (including proteins, peptides and enzymes, among many other +topics). + +The conference is expected to boost a new paradigm of interdisciplinary +approaches converging into specific problems in biological physics. Hence, the +conference audience is broad: We aim to attract the attention of biologists as +well as biochemists, organic chemists, engineers, computational scientists, +physicists, and mathematicians. The venue is highly convenient since there are +four major Research Universities in Mexico City's metropolitan area, with +extensive undergraduate and graduate programs in physics, biology, medicine, +engineering and mathematics. + +The program includes: + +- Talks by national and international experts +- Poster session for undergraduate/graduate students +- Round-Table session on new trends in biological physics +- A dedicated issue of conference proceedings. + +Due to the generosity of the sponsoring institutions, no fees will be charged +to those selected to participate in this conference. diff --git a/content/events/biophysmex2019.md b/content/events/biophysmex2019.md new file mode 100644 index 0000000..d953d8d --- /dev/null +++ b/content/events/biophysmex2019.md @@ -0,0 +1,25 @@ ++++ +place = "Centro de Ciencias de la Complejidad" +maps = "https://goo.gl/maps/3RAyTdDrTBH2" +title = "Biological Physics Mexico City 2019" +end = "2019-09-04" +date = "2019-09-06" +webpage = "https://sites.google.com/view/biophysmex2019" + ++++ + +Frontier Science at the Intersection of Physics, Math and Biology +======================================================== + +The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments. + +The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians. The venue is highly convenient since there are four major Research Universities in Mexico City's metropolitan area, with extensive undergraduate and graduate programs in physics, biology, medicine, engineering and mathematics. + +The program includes: + +- Talks by national and international experts; +- Poster session for undergraduate / graduate students +- Round-Table session on new trends in biological physics. +- A dedicated issue of conference proceedings. + + diff --git a/content/events/is3b.md b/content/events/is3b.md new file mode 100644 index 0000000..a13a5ff --- /dev/null +++ b/content/events/is3b.md @@ -0,0 +1,19 @@ ++++ +place = "INMEGEN" +maps = "https://goo.gl/maps/1CUc2NA46Bu" +date = "2016-08-02" +end = "2016-08-04" +title = "2nd International Summer Symposium on Systems Biology" +webpage = "https://is3b.inmegen.gob.mx" ++++ + +With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM & INMEGEN. + +The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine. The aims of the meeting are: + +1. Discuss current research in Systems Biology and its applications for understanding human diseases +2. Create an ambiance that enables scientific collaborations among experimental and theoretical groups working on human diseases. + +To this extent we invite national and international researchers and students working in the aforementioned fields during all stages of their academic career with the possibility to present their work as a poster or short talk to a highly qualified research community. + +The registration deadline has been extended until June 15th, 2016! diff --git a/content/events/is3b_2014.md b/content/events/is3b_2014.md new file mode 100644 index 0000000..f550f23 --- /dev/null +++ b/content/events/is3b_2014.md @@ -0,0 +1,35 @@ ++++ +date = "2014-08-04" +title = "1st International Summer Symposium on Systems Biology" +end = "2014-08-06" +place = "INMEGEN" +maps = "https://goo.gl/maps/1CUc2NA46Bu" +webpage = "http://www.inmegen.gob.mx/is3b/" ++++ + +Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems +Biology: From networks to phenotypes in human diseases”. The meeting took place at the National +Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the +first of a series of meetings to encourage the development of the systems biology in Mexico and the +development of this area to tackle basic and applied research in medical and biomedical fields. +This effort was supported by the Laboratory of Human Systems Biology-INMEGEN and Fundación +Televisa to create a scientific ambiance for discussing some methods and strategies to develop the +bases of a systemic and personalized medicine in a national and international perspective. + +The purposes of the meeting were: + +- Discuss some of the frontier research in Systems Biology and its applications for understanding +human diseases. + +- Create an ambiance to establish collaborations among groups that will promote different +computational frameworks for modeling human diseases. + +- Design strategies to encourage growth in this area in biomedical, medicine and genomic sciences at +the undergraduate and graduate levels since these are areas with potential for dealing with health +problems in Mexico. + +To reach these goals, the meeting brought together some national and international qualified experts +from different research groups, providing an excellent occasion for academic exchange between local +and foreign colleagues in a pleasant and collaborative environment. The program included plenary +lectures, poster sessions, a discussion panel and a series of short presentations geared towards +postdoctoral researchers and advanced graduate students. diff --git a/content/events/is3b_2019.md b/content/events/is3b_2019.md new file mode 100644 index 0000000..b2aea5b --- /dev/null +++ b/content/events/is3b_2019.md @@ -0,0 +1,31 @@ ++++ +date = "2019-08-05" +title = "3st International Summer Symposium on Systems Biology" +end = "2019-08-06" +place = "INMEGEN" +maps = "https://goo.gl/maps/1CUc2NA46Bu" +webpage = "http://is3b.inmegen.gob.mx/index.html" ++++ + +Those are the proceedings for the 3rd edition of the “International Summer Symposium on Systems +Biology”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields. +This effort was supported by the Laboratory of Human Systems Biology-INMEGEN to create a scientific ambiance for discussing some methods and strategies to develop the +bases of a systemic and personalized medicine in a national and international perspective. + +The purposes of the meeting were: + +- Discuss some of the frontier research in Systems Biology and its applications for understanding +human diseases. + +- Create an ambiance to establish collaborations among groups that will promote different +computational frameworks for modeling human diseases. + +- Design strategies to encourage growth in this area in biomedical, medicine and genomic sciences at +the undergraduate and graduate levels since these are areas with potential for dealing with health +problems in Mexico. + +To reach these goals, the meeting brought together some national and international qualified experts +from different research groups, providing an excellent occasion for academic exchange between local +and foreign colleagues in a pleasant and collaborative environment. The program included plenary +lectures, poster sessions, a discussion panel and a series of short presentations geared towards +postdoctoral researchers and advanced graduate students. diff --git a/content/members/.Rhistory b/content/members/.Rhistory new file mode 100644 index 0000000..e69de29 diff --git a/content/members/dummy.md b/content/members/dummy.md new file mode 100644 index 0000000..a254df0 --- /dev/null +++ b/content/members/dummy.md @@ -0,0 +1,5 @@ ++++ + ++++ + +This is a dummy page. It's content will not be rendered. diff --git a/content/positions/postdocs.md b/content/positions/postdocs.md new file mode 100644 index 0000000..98a2222 --- /dev/null +++ b/content/positions/postdocs.md @@ -0,0 +1,7 @@ ++++ +date = 2017-06-29T12:52:56-05:00 +author = "Osbaldo Resendis Antonio" +title = "Postdoctoral position" ++++ + +We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to [oresendis [at] inmegen.gob.mx](mailto:oresendis [at] inmegen.gob.mx). diff --git a/content/positions/students.md b/content/positions/students.md new file mode 100644 index 0000000..5861ebe --- /dev/null +++ b/content/positions/students.md @@ -0,0 +1,32 @@ ++++ +date = 2017-06-29T12:53:05-05:00 +author = "Osbaldo Resendis Antonio" +title = "Graduate student positions" ++++ + +We extend an invitation to undergrads and grad students with interest to +continue his/her academic education through a Master's or Doctoral degree in one +of these academic programs: biological (http://pcbiol.posgrado.unam.mx), +biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical +(http://www.pdcb.unam.mx/) Sciences at UNAM. We encourage candidates with an +academic background in biology, biology physics, biophysics, genome sciences, +applied mathematics and computational sciences. The students incorporated to one +of these programs will be guided to develop a systems biology description in one +of these areas: + +1. Identification of metabolic alterations in cancer cell lines +or animal tissues +2. Tissue-specific genome-scale metabolic reconstruction in +human system +3. computational modeling of metabolism in microbiota and its +association with diabetes +4. Development of computational methods to integrate and interpret high-throughput technologies such as RNAseq, proteome, and metabolome +5. development of systems Biology schemes for the Study of Neuropsychiatric Diseases +6. Development of computational analysis in Personalized (precision) medicine. + +All projects are immersed on an interdisciplinary ambiance and promote an +integrative description of the statistical analysis of high-throughput data in +genomic sciences, development of computational/mathematical modeling of +biological networks; and the physiological knowledge, these points required +for understanding the principles that support the metabolic phenotype in human +diseases. For any further questions do not hesitate to contact us via [E-mail](mailto:oresendis [at] inmegen.gob.mx). \ No newline at end of file diff --git a/content/posts/test.md b/content/posts/test.md new file mode 100644 index 0000000..67da8d3 --- /dev/null +++ b/content/posts/test.md @@ -0,0 +1,27 @@ ++++ +categories = [ + "science", + "tutorial", +] +author = "Christian" +title = "Hola!!!" +date = "2016-12-06T09:19:57-06:00" +image = "" + ++++ + +## This is an example post + +Please substitute all text below "+++" with your own! + +This is my text now grrrr :) + + +$$ +\int_a^b e^{2\pi\cdot x} dx +$$ + +```R +library(data.table) +df <- data.table(x=1:10) +``` diff --git a/content/projects/GEM_spheroids.md b/content/projects/GEM_spheroids.md new file mode 100644 index 0000000..35308da --- /dev/null +++ b/content/projects/GEM_spheroids.md @@ -0,0 +1,20 @@ ++++ +people = [ + "Jorge Enrique Arellano Villavicencio" +] +title = "Computational modeling of metabolic dynamics in the intratumoral microenvironment." +date = "2023-06-27" + ++++ + +Currently, oncology research has been focused on investigating cancer metabolism due to its +remarkable capacity to adapt to changes in its microenvironment, enabling it to efficiently respond +to gradients of oxygen and nutrients. In 3D spheroid cultures of MCF-7 cells, three distinct cell +subpopulations with varying metabolic characteristics have been identified, indicating that each +subpopulation fulfills specific activities within the tumor, promoting its progression and survival. + +This project proposes the utilization of genome-scale metabolic reconstructions (GEMS) to model +the growth of each subpopulation. Additionally, by employing community modeling tools, it aims +to simulate spheroid growth and characterize the dynamics of metabolites among the three +communities. This research will pave the way for understanding the cooperativity between cells +and their response to different types of stress, such as hypoxia and reduced carbon sources. diff --git a/content/projects/T2D.md b/content/projects/T2D.md new file mode 100644 index 0000000..dccae97 --- /dev/null +++ b/content/projects/T2D.md @@ -0,0 +1,7 @@ ++++ +date = "2022-12-15" +people = ["Estrella Martínez"] +title = "Alteration of gut microbiota induced by metformin and linagliptin/metformin treatment prevents type 2 diabetes." ++++ + +Lifestyle modifications, metformin and dipeptidyl peptidase type 4 inhibitors (DPP4i) reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The efficacy of such interventions may be enhanced by the gut microbiota (GM), which plays a role in mediating glucose-lowering effects through the increased abundance of short-chain fatty acid (SCFA)-producing bacteria. We determined the effect of combined linagliptin+metformin vs metformin monotherapy on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes without a previous treatment and compared it between metformin monotherapy and the combination of linagliptin+metformin. A double-blind, randomized parallel clinical trial was conducted in 167 Mexican adults with prediabetes for 12 months. We analyzed the effects of the two treatments on GM using the machine learning algorithm (random forest) and mediation analysis with two structural equation models (SEM) to determine the relationship between body composition, IS, Pβf, and bacterial genera. These treatments modify GM composition, by increasing the abundance of SCFA-producing bacteria [Metformin (Fusicatenibacter and Blautia) and Linagliptin/metformin (Roseburia, Bifidobacterium and [Eubacterium] hallii group)]. \ No newline at end of file diff --git a/content/projects/T2DMicom.md b/content/projects/T2DMicom.md new file mode 100644 index 0000000..d9d61c0 --- /dev/null +++ b/content/projects/T2DMicom.md @@ -0,0 +1,7 @@ ++++ +date = "2022-12-15" +people = ["Daniel Neri"] +title = "Gut microbiota and type 2 diabetes" ++++ + +A direct link between the gut microbiota (GM) and the progression of type 2 diabetes mellitus (T2D) in individuals has been described. We propose using supervised Machine Learning (ML) methods to identify predictive taxa for patients with prediabetes (pre-T2D) and T2D. diff --git a/content/projects/covidMicom.md b/content/projects/covidMicom.md new file mode 100644 index 0000000..47fa6e6 --- /dev/null +++ b/content/projects/covidMicom.md @@ -0,0 +1,7 @@ ++++ +date = "2022-12-15" +people = ["David Girón Villalobos"] +title = "Computational modeling of the gut microbiota metabolism in COVID-19 patients" ++++ + +I study the gut microbiota to find how microbes participate in the development of COVID-19. To do that, I use MICOM, a community metabolic computational model, that can predict metabolic interactions within the microbiota and the host. \ No newline at end of file diff --git a/content/projects/diabe_metabolism.md b/content/projects/diabe_metabolism.md new file mode 100644 index 0000000..d44c3d2 --- /dev/null +++ b/content/projects/diabe_metabolism.md @@ -0,0 +1,10 @@ ++++ +people = [ + "Jean Paul Sanchez" +] +title = "Microbiome metabolism and diabetes" +date = "2019-12-06" + ++++ + +Alterations in the microbiome has been associated with diabetes progression. diff --git a/content/projects/diabetes_metformin.md b/content/projects/diabetes_metformin.md new file mode 100644 index 0000000..d6ad6d6 --- /dev/null +++ b/content/projects/diabetes_metformin.md @@ -0,0 +1,10 @@ ++++ +people = [ + "Laura Elena Hernández Juárez" +] +title = "On Type 2 diabetes, and their relation with the gut microbiome metabolism." +date = "2023-06-27" + ++++ + +Type 2 diabetes mellitus (T2D) is a widespread disease worldwide, the etiology may be associated with gut microbiota influenced by different diet patterns. Metformin is a T2D treatment, and it is known that can alter the gut microbiota composition, but a few is known about the relation between this composition and the physio-pathological variables, therefore in this project the microbiota composition is analyzed, as well as the computational modeling of metabolism in microbiota to infer the growth rates of selected bacteria and the metabolic interactions into gut microbiota on patients with T2D under metformin and linagliptin treatment \ No newline at end of file diff --git a/content/projects/diabetes_micom.md b/content/projects/diabetes_micom.md new file mode 100644 index 0000000..eb4b112 --- /dev/null +++ b/content/projects/diabetes_micom.md @@ -0,0 +1,7 @@ ++++ +date = "2023-06-27" +people = ["Juan José Oropeza Valdez"] +title = "Machine Learning-Based Exploration of Gut Microbiota's Impact on Type 2 Diabetes" ++++ + +Employing machine learning algorithms to investigate the role of the gut microbiota in the development and management of Type 2 diabetes (T2DM). By analyzing microbiome data from individuals with multiple diabetes treatments, my aim is to identify specific microbial compositions associated with the disease and develop predictive models that assess an individual's risk of developing T2DM based on their microbiota profile. Also using a systems biology approach (MICOM) using the gut microbiota data to identify the metabolic changes in the community associated with T2DM. \ No newline at end of file diff --git a/content/projects/ecologicalGut.md b/content/projects/ecologicalGut.md new file mode 100644 index 0000000..4593a9f --- /dev/null +++ b/content/projects/ecologicalGut.md @@ -0,0 +1,7 @@ ++++ +date = "2022-12-15" +people = ["Crístian Mendoza Ortiz"] +title = "Ecological study on gut microbiota" ++++ + +We analyze the dynamics of the metabolism of the gut microbiota in longitudinal databases through a hybrid model between generalized Lotka-Volterra and flux balance analysis (FBA) \ No newline at end of file diff --git a/content/projects/emt.md b/content/projects/emt.md new file mode 100644 index 0000000..aeb2cfc --- /dev/null +++ b/content/projects/emt.md @@ -0,0 +1,12 @@ ++++ +title = "In silico study of metabolic reprogramming during epithelial-mesenchymal transition" +people = [ + "Meztli Matadamas" +] +date = "2019-12-06" + ++++ + +An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes. + +The project combine high-throughput data to understand metabolic changes before and after EMT in lung cancer cell lines. In particular to find main fluxes used during EMT. To that extent we employ methods from bioinformatics and Systems Biology. Our goal is to found specific targets which could stop or reverse EMT in cancer cells. diff --git a/content/projects/hepato.md b/content/projects/hepato.md new file mode 100644 index 0000000..b961eb6 --- /dev/null +++ b/content/projects/hepato.md @@ -0,0 +1,12 @@ ++++ +date = "2018-12-06" +title = "Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model" +people = [ + "Erika Hernandez", +] + ++++ + +Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. +Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions. +This data would not only helped us identify reactions important to maintain the cancerous state but also help us survey the regulatory mechanism this is achieved. diff --git a/content/projects/inmunology_cancer.md b/content/projects/inmunology_cancer.md new file mode 100644 index 0000000..f384681 --- /dev/null +++ b/content/projects/inmunology_cancer.md @@ -0,0 +1,10 @@ ++++ +people = [ + "Ugo Avila Ponce de Leon" +] +title = "Immunology and cancer: Boolean Modeling of regulatory networks" +date = "2018-12-06" + ++++ + +Macrophages are cells of the innate immune system endowed with the capacity to orchestrate the immune response in human tissues. Due to their plasticity biological property, they polarize to several subtypes based on the actions of the tumor microenvironment. These cells have plasticity, because once they are committed to a subtype fate, they can polarize to another subtype by simply modifying the microenvironment. We integrated experimental data for the construction of a network that will explain the plasticity and the importance of the microenvironment in shaping the polarization of macrophages. The mathematical model was used to describe the genetic control points of macrophage polarization and plasticity, and it can function as groundwork and guidance for an immunotherapeutic approach to modulate the proliferation of cancer cells. diff --git a/content/projects/leukemia.md b/content/projects/leukemia.md new file mode 100644 index 0000000..88b1a61 --- /dev/null +++ b/content/projects/leukemia.md @@ -0,0 +1,7 @@ ++++ +date = "2022-12-15" +people = ["Brenda Loaiza"] +title = " Research on Children Leukemia" ++++ + +Leukemia is the most common cancer in children worldwide, highest incidences and worse prognostics are for low and middle-income countries where less than 30% are cured. In Mexico 4,000 to 6,000 new cases are registered each year. Epidemiological studies have shown the contribution of environmental factors to the development of Leukemia, but also clinical factors such as late and imprecise diagnosis of the disease, limited access, and /or adherence to treatment, and tolerance and toxicity of antineoplastic drugs. To confront this problem, a National Strategic Program (Pronace) was proposed for the integral study of Children Leukemia. I'm responsible for Collecting all kinds of data from most laboratories of Mexico, through the organization of a DataBase in SQL language to be publicly available, and for integral study including artificial intelligence analysis for creating models that identify risk variables, better prognosis of patients, and a deep understanding of the etiology of Acute Lymphoblastic Leukemia in mexican children. \ No newline at end of file diff --git a/content/projects/metabolic_Macrophages.md b/content/projects/metabolic_Macrophages.md new file mode 100644 index 0000000..63e21c7 --- /dev/null +++ b/content/projects/metabolic_Macrophages.md @@ -0,0 +1,9 @@ ++++ +date = "2023-01-01" +people = ["Perla Itzel Alvarado Luis"] +title = "Metabolic changes in macrophage polarization through in silico approaches" ++++ + +Macrophages, crucial components of the innate immune system, have the remarkable ability to polarize and adopt various phenotypes in response to fluctuations in their microenvironment. Considered as "double-edged swords”, these cells serve a wide array of physiological roles; however, their dysfunction can contribute to the development of various diseases, such as cancer, tuberculosis, and atherosclerosis. Furthermore, macrophage polarization is critically supported by metabolic shifts, and there is an exciting potential for regulating macrophage functions in different contexts by manipulating their metabolism. + +The objective of this project is to use a systems biology approach to analyze metabolomic data from polarized macrophages in order to unravel the underlying mechanisms of metabolic reprogramming during macrophage polarization. Through this study, we aim to identify the specific metabolic factors that contribute to the transition between different phenotypes and ultimately, their potential use in inducing repolarization towards a desired phenotype. \ No newline at end of file diff --git a/content/projects/prolif.md b/content/projects/prolif.md new file mode 100644 index 0000000..4eaff6d --- /dev/null +++ b/content/projects/prolif.md @@ -0,0 +1,14 @@ ++++ +people = [ + "Christian Diener" +] +title = "Metabolic heterogeneity in cancer and its applications in Personalized Medicine" +date = "2018-12-06" + ++++ + +Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation. + +In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism. In particular we are asking the question whether distinct genomic aberrations such as mutations or changes in transcription can be related to respective changes in cellular metabolism. To that extent we employ methods from Systems Biology as well as from Data Science and Machine Learning in order to connect genetic information to specific metabolic phenotypes. + +Our aim is to use the knowledge we gain in the context of personalized medicine, particularly the use of genotyping for the prediction of the best course of treatment for a specific patient. diff --git a/content/projects/scPhenix.md b/content/projects/scPhenix.md new file mode 100644 index 0000000..a327b18 --- /dev/null +++ b/content/projects/scPhenix.md @@ -0,0 +1,7 @@ ++++ +date = "2022-12-15" +people = ["Crístian Padrón "] +title = "Manifold learning approaches for high dimensional biological data" ++++ + +Modern high–throughput biological data yield detailed characterizations of the genomic, transcriptomic, and proteomic states of samples. This kind of data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discovery reliability. Data sparsity makes it difficult to obtain a well-data structure and distorts the distribution of variables. Currently, there is a raised need to develop new algorithms with improved capacities to reduce noise and recover missing information. For that reason, we are developing new machine learning methods to better understand noisy and high-dimensional biological data. For example, microbiome and scRNA-seq data imputation. Also, we are developing multi-omic data integration methods to find key variables involved in complex biological systems which is in itself difficult to handle and the problem of the high-dimensionality is accentuated in multi-omics data. diff --git a/content/projects/singlecell_thelma.md b/content/projects/singlecell_thelma.md new file mode 100644 index 0000000..43962e9 --- /dev/null +++ b/content/projects/singlecell_thelma.md @@ -0,0 +1,14 @@ ++++ +people = [ + "Thelma Escobedo" +] +title = "Systems biology and bioinformatics of single cell RNAseq data." +date = "2018-12-06" + ++++ + +Research in personalized therapy has taken relevance because treatment failures due to intratumoral heterogenety which refers to celular diversity or subpopulations +forming within the tumor. Currently, given complex molecular processes of cancer there has been greater use of omic technologies and computational analysis. With the purpuse to +contribute in this line, we have opened a new line of research to describe the progress of expression profiles in tumor cell lines through bioinformatic analysis of single cell +RNAseq data. Likewise, this work will contribute to infer the principles that guide the population heterogeneity mechanisms for the design of new optimized strategies for the +treatment of cancer. diff --git a/content/projects/spheroids.md b/content/projects/spheroids.md new file mode 100644 index 0000000..5c50640 --- /dev/null +++ b/content/projects/spheroids.md @@ -0,0 +1,13 @@ ++++ +people = [ + "Erick Muciño" +] +title = "The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development" +date = "2018-12-06" + ++++ + +Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. Hence, generate models that allow the integration of miRNAs regulation in cancer metabolism will allow us to analyze in a systematically and systemic manner the relations and potential mechanism underlying between miRNAs and central pathways of metabolism. + + +It is imperative to know the mechanism governing the pathogenesis and progression of cancer to design therapies with greater impact on diagnosis and disease progression. So, this project aims to suggest mechanism that trigger the metabolic change in a breast cancer cell line (MCF-7), integrating miRNAs network. To this end, we propose to develop a scheme of systems biology, which allow us to make an integrative analysis of regulatory networks of miRNAs and metabolism in MCF-7. This approach will allow us to develop models capable of identifying potential therapeutic targets with greater impact, biomarkers that allow early detection of cancer and penetrate in global mechanism in clinical cases. diff --git a/content/pubs/Boolean_modeling.md b/content/pubs/Boolean_modeling.md new file mode 100644 index 0000000..c46e8ad --- /dev/null +++ b/content/pubs/Boolean_modeling.md @@ -0,0 +1,11 @@ ++++ +authors = ["Ugo Avila-Ponce de León", "Aarón Vázquez-Jiménez", "Meztli Matadamas-Guzmán", "Osbaldo Resendis-Antonio"] +title = "Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment" +journal = "Frontiers in Immunology" +what = "article" +doi = "10.3389/fimmu.2022.1012730" +pubmed = "" +date = "2012-12-05" ++++ + +Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage. The same behavior happens when the M2-associated transcription factors are disturbed (STAT3 or STAT6); cycle attractors will prevent a hyper-regulation scenario implicated in providing a suitable environment for tumor growth. Therefore, here we propose that cyclic macrophage phenotypes can serve as a reservoir for balancing the phenotypes when a specific phenotype-based transcription factor is perturbed in the regulatory network of macrophages. We consider that cyclic attractors should not be simply ignored, but it is necessary to carefully evaluate their biological importance. In this work, we suggest one conjecture: the cyclic attractors can serve as a reservoir to balance the inflammatory/regulatory response of the network under external perturbations. \ No newline at end of file diff --git a/content/pubs/Comorbidities_covid.md b/content/pubs/Comorbidities_covid.md new file mode 100644 index 0000000..d0c813a --- /dev/null +++ b/content/pubs/Comorbidities_covid.md @@ -0,0 +1,20 @@ ++++ +authors = ["Rubí Romo-Rodríguez", "Karla Gutiérrez-de Anda", "Jebea A López-Blanco", "Gabriela Zamora-Herrera", "Paulina Cortés-Hernández", "Gerardo Santos-López", "Luis Márquez-Domínguez", "Armando Vilchis-Ordoñez", "Dalia Ramírez-Ramírez", "Juan Carlos Balandrán", "Israel Parra-Ortega", "Osbaldo Resendis-Antonio", "Lenin Domínguez-Ramírez", "Constantino López-Macías", "Laura C Bonifaz", "Lourdes A Arriaga-Pizano", "Arturo Cérbulo-Vázquez", "Eduardo Ferat-Osorio", "Antonieta Chavez-González", "Samuel Treviño", "Eduardo Brambila", "Miguel Ángel Ramos-Sánchez", "Ricardo Toledo-Tapia", "Fabiola Domínguez", "Jorge Bayrán-Flores", "Alejandro Cruz-Oseguera", "Julio Roberto Reyes-Leyva", "Socorro Méndez-Martínez", "Jorge Ayón-Aguilar", "Aurora Treviño-García", "Eduardo Monjaraz", "Rosana Pelayo"] +title = "Chronic Comorbidities in Middle Aged Patients Contribute to Ineffective Emergency Hematopoiesis in Covid-19 Fatal Outcomes" +journal = "Archives of Medical Research" +what = "article" +doi = "10.1016/j.arcmed.2023.03.003" +pubmed = "https://pubmed.ncbi.nlm.nih.gov/36990888/" +date = "2023-01-01" ++++ + +Background and Aims +Mexico is among the countries with the highest estimated excess mortality rates due to the COVID–19 pandemic, with more than half of reported deaths occurring in adults younger than 65 years old. Although this behavior is presumably influenced by the young demographics and the high prevalence of metabolic diseases, the underlying mechanisms have not been determined. +Methods +The age–stratified case fatality rate (CFR) was estimated in a prospective cohort with 245 hospitalized COVID–19 cases, followed through time, for the period October 2020–September 2021. Cellular and inflammatory parameters were exhaustively investigated in blood samples by laboratory test, multiparametric flow cytometry and multiplex immunoassays. +Results +The CFR was 35.51%, with 55.2% of deaths recorded in middle–aged adults. On admission, hematological cell differentiation, physiological stress and inflammation parameters, showed distinctive profiles of potential prognostic value in patients under 65 at 7 days follow–up. Pre–existing metabolic conditions were identified as risk factors of poor outcomes. Chronic kidney disease (CKD), as single comorbidity or in combination with diabetes, had the highest risk for COVID–19 fatality. Of note, fatal outcomes in middle–aged patients were marked from admission by an inflammatory landscape and emergency myeloid hematopoiesis at the expense of functional lymphoid innate cells for antiviral immunosurveillance, including NK and dendritic cell subsets. +Conclusions +Comorbidities increased the development of imbalanced myeloid phenotype, rendering middle–aged individuals unable to effectively control SARS–CoV–2. A predictive signature of high–risk outcomes at day 7 of disease evolution as a tool for their early stratification in vulnerable populations is proposed. +Keywords: Chronic comorbidities; Emergency hematopoiesis; COVID–19; Middle adulthood; Inflammation; Chronic kidney disease + diff --git a/content/pubs/Comparative_subcellular.md b/content/pubs/Comparative_subcellular.md new file mode 100644 index 0000000..1d6e21b --- /dev/null +++ b/content/pubs/Comparative_subcellular.md @@ -0,0 +1,11 @@ ++++ +authors = ["Dafne Guerrero-Escalera", "Brisa Rodope Alarcón-Sánchez", "Jaime Arellanes-Robledo", "Armando Cruz-Rangel", "Luis del Pozo-Yauner", "Victoria Chagoya de Sánchez", "Osbaldo Resendis-Antonio", "Saul Villa-Treviño", "Julia Esperanza Torres-Mena", "Julio Isael Pérez-Carreón"] +title = "Comparative subcellular localization of NRF2 and KEAP1 during the hepatocellular carcinoma development in vivo" +journal = "Biochimica et Biophysica Acta (BBA)-Molecular Cell Research" +what = "article" +doi = "10.1016/j.bbamcr.2022.119222" +pubmed = "" +date = "2022-05-01" ++++ + +The activation of Nuclear Factor, Erythroid 2 Like 2 – Kelch Like ECH Associated Protein 1 (NRF2-KEAP1) signaling pathway plays a critical dual role by either protecting or promoting the carcinogenesis process. However, its activation or nuclear translocation during hepatocellular carcinoma (HCC) progression has not been addressed yet. This study characterizes the subcellular localization of both NRF2 and KEAP1 during diethylnitrosamine-induced hepatocarcinogenesis in the rat. NRF2-KEAP1 pathway was continuously activated along with the increased expression of its target genes, namely Nqo1, Hmox1, Gclc, and Ptgr1. Similarly, the nuclear translocation of NRF2, MAF, and KEAP1 increased in HCC cells from weeks 12 to 22 during HCC progression. Likewise, colocalization of NRF2 with KEAP1 was higher in the cell nuclei of HCC neoplastic nodules than in surrounding cells. Moreover, immunofluorescence analyses revealed that the interaction of KEAP1 with filamentous Actin was disrupted in HCC cells. This disruption may be contributing to the release and nuclear translocation of NRF2 since the cortical actin cytoskeleton serves as anchoring of KEAP1. In conclusion, this evidence indicates that NRF2 is progressively activated and promotes the progression of experimental HCC. \ No newline at end of file diff --git a/content/pubs/Dysbiosis_diabetes.md b/content/pubs/Dysbiosis_diabetes.md new file mode 100644 index 0000000..7e1f699 --- /dev/null +++ b/content/pubs/Dysbiosis_diabetes.md @@ -0,0 +1,17 @@ ++++ +authors = ["Daniel Neri-Rosario", "Yoscelina E. Martínez-López", "Diego A. Esquivel-Hernández", "Jean Paul Sánchez-Castañeda", "Crístian Padron-Manrique", "Aarón Vázquez-Jiménez", "David Giron-Villalobos", "Osbaldo Resendis-Antonio"] +title = "Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort" +journal = "Front. Endocrinol." +what = "article" +doi = "10.3389/fendo.2023.1170459" +pubmed = "" +date = "2023-06-27" ++++ + +Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual’s gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D. + +Methods: To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls. + +Results: We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects. + +Discussion: These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease. \ No newline at end of file diff --git a/content/pubs/Fontiers_oncology_2020_Erick.md b/content/pubs/Fontiers_oncology_2020_Erick.md new file mode 100644 index 0000000..8aaf611 --- /dev/null +++ b/content/pubs/Fontiers_oncology_2020_Erick.md @@ -0,0 +1,32 @@ ++++ +authors = ["Erick Andrés Muciño-Olmos", "Aarón Vázquez-Jiménez", "Diana Elena López-Esparza", "Vilma Maldonado", "Mahara Valverde", "Osbaldo Resendis-Antonio"] +title = "MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression" +journal= "Frontiers in Oncology" +what = "article" +doi="10.3389/fonc.2020.582396" +pubmed = "33425736" +date = "2020-12-04" ++++ + +During tumor progression, cancer cells ire their metabolism to face their bioenergetic +demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that +inhibit the translation and stability of crucial mRNAs, some of them causing direct +metabolic alterations in cancer. In this study, we investigated the relationship between +miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned +regulation is diversified depending on the tumor stage. To do so, we implemented a +paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The +cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture +conditions. For MCTS, we selected two-time points during their development to +recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA +expression patterns associated with metabolism. As a result, we identified a set of new +direct putative regulatory interactions between miRNAs and metabolic mRNAs +representative for proliferative and quiescent stages. Notably, our study allows us to +suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, +we found that the overexpression of several miRNAs could directly overturn the +expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this +set of miRNAs downregulates their expression in the quiescent stage, the same set is +upregulated in proliferative stages. This last finding suggests an additional metabolic +switch of the above mentioned metabolic pathways between the quiescent and proliferative +stages. Our results contribute to a better understanding of how miRNAs modulate the +metabolic landscape in breast cancer MCTS, which eventually will help to design new +strategies to mitigate cancer phenotype. diff --git a/content/pubs/Frontiers_immunology_2021.md b/content/pubs/Frontiers_immunology_2021.md new file mode 100644 index 0000000..2c01d57 --- /dev/null +++ b/content/pubs/Frontiers_immunology_2021.md @@ -0,0 +1,11 @@ ++++ +authors = ["Aarón Vázquez-Jiménez","Ugo Avila-Ponce De León","Meztli Matadamas-Guzman","Erick Andrés Muciño-Olmos","Estrella Martínez-López", "Thelma Escobedo-Tapia", "Osbaldo Resendis-Antonio"] +title = "On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers" +journal= "Frontiers in Immunology" +what = "article" +doi="10.3389/fimmu.2021.705646" +pubmed = "34603282" +date = "2021-09-16" ++++ + +COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis. diff --git a/content/pubs/Frontiers_immunology_2021_UGO.md b/content/pubs/Frontiers_immunology_2021_UGO.md new file mode 100644 index 0000000..e5b85ca --- /dev/null +++ b/content/pubs/Frontiers_immunology_2021_UGO.md @@ -0,0 +1,11 @@ ++++ +authors = ["Ugo Avila-Ponce De León","Aarón Vázquez-Jiménez","Meztli Matadamas-Guzman","Rosana Pelayo","Osbaldo Resendis-Antonio"] +title = "Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysis" +journal= "Frontiers in Immunology" +what = "article" +doi="10.3389/fimmu.2021.642842" +pubmed = "34177892" +date = "2021-06-10" ++++ + +The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells. Here, we used a Boolean modeling approach to understand how the tumor microenvironment shapes macrophage behavior to enhance pro-tumoral functions. Our network reconstruction integrates experimental data and public information that let us study the polarization from monocytes to M1, M2a, M2b, M2c, and M2d subphenotypes. To analyze the dynamics of our model, we modeled macrophage polarization in different conditions and perturbations. Notably, our study identified new hybrid cell populations, undescribed before. Based on the in vivo macrophage behavior, we explained the hybrid macrophages’ role in the tumor microenvironment. The in silico model allowed us to postulate transcriptional factors that maintain the balance between macrophages with anti- and pro-tumoral functions. In our pursuit to maintain the balance of macrophage phenotypes to eliminate malignant tumor cells, we emulated a theoretical genetically modified macrophage by modifying the activation of NFκB and a loss of function in HIF1-α and discussed their phenotype implications. Overall, our theoretical approach is as a guide to design new experiments for unraveling the principles of the dual host-protective or -harmful antagonistic roles of transitional macrophages in tumor immunoediting and cancer cell fate decisions. diff --git a/content/pubs/Frontiers_onlogy2020.md b/content/pubs/Frontiers_onlogy2020.md new file mode 100644 index 0000000..009f0b4 --- /dev/null +++ b/content/pubs/Frontiers_onlogy2020.md @@ -0,0 +1,11 @@ ++++ +authors = ["Meztli Matadamas-Guzman", "Cecilia Zazueta", "Emilio Rojas", "Osbaldo Resendis-Antonio"] +title = "Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgrounds" +journal = "Frontiers in Onology" +what = "article" +doi = "10.3389/fonc.2020.01309" +pubmed = "32850411" +date = "2020-08-02" ++++ + +Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds. Here we used a mathematical modeling approach to determine which are the metabolic reactions altered after induction of EMT, based on metabolomic and transcriptional data of three non-small cell lung cancer (NSCLC) cell lines. The model suggested that the most affected pathways were the Krebs cycle, amino acid metabolism, and glutathione metabolism. However, glutathione metabolism had many alterations either on the metabolic reactions or at the transcriptional level in the three cell lines. We identified Glutamate-cysteine ligase (GCL), a key enzyme of glutathione synthesis, as an important common feature that is dysregulated after EMT. Analyzing survival data of men with lung cancer, we observed that patients with mutations in GCL catalytic subunit (GCLC) or Glutathione peroxidase 1 (GPX1) genes survived less time than people without mutations on these genes. Besides, patients with low expression of ANPEP, GPX3 and GLS genes also survived less time than those with high expression. Hence, we propose that glutathione metabolism and glutathione itself could be good targets to delay or potentially prevent EMT induction in NSCLC cell lines. diff --git a/content/pubs/Machine_COVID.md b/content/pubs/Machine_COVID.md new file mode 100644 index 0000000..e6c85b2 --- /dev/null +++ b/content/pubs/Machine_COVID.md @@ -0,0 +1,12 @@ ++++ +authors = ["Ugo Avila-Ponce de León", "Aarón Vázquez-Jiménez", "Alejandra Cervera", "Galilea Resendis-González", "Daniel Neri-Rosado", "Osbaldo Resendis-Antonio"] +title = "Machine Learning and COVID-19: Lessons from SARS-CoV-2" +journal = "Adv Exp Med Bio" +what = "book" +doi = "" +pubmed = "" +date = "2023-01-01" +isbn="978-3-031-28011-5" ++++ + +Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behaviour and high-throughput technologies, associated with COVID-19 evolution. \ No newline at end of file diff --git a/content/pubs/Macrophage_Boolean.md b/content/pubs/Macrophage_Boolean.md new file mode 100644 index 0000000..7e1abc5 --- /dev/null +++ b/content/pubs/Macrophage_Boolean.md @@ -0,0 +1,11 @@ ++++ +authors = ["Ugo Avila-Ponce de León", "Osbaldo Resendis-Antonio"] +title = "Macrophage Boolean networks in the time of SARS-CoV-2" +journal = "Frontiers in Immunology" +what = "article" +doi = "10.3389/fimmu.2022.997434" +pubmed = "" +date = "2022-10-17" ++++ + +The post-pandemic period of the current coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has lasted longer than expected despite the huge impact of the world-wide vaccination campaign in the past years. Since the pandemic began, endless mathematical models have been published to describe the viral outbreak at a population level. However, the molecular mechanism that drives the pathogenesis of the virus in the human microenvironment has been scarce. The existing mechanistic models deal to answer how SARS-CoV-2 invades the lung microenvironment and shapes the composition of the immune system in favor of virus duplication. From the clinical point of view, almost all patients that develop severe COVID-19 lead from a life-threatening acute respiratory distress syndrome (ARDS), which is associated with a hyper-inflammatory microenvironment and injury in the alveolar and lung epithelium. One of the cells associated with this syndrome is an uncontrolled hyper-activated macrophage-associated syndrome (MAS), which promotes a systemic inflammatory response that exacerbates the progress of the cytokine storm in the host. Furthermore, single-cell high-throughput technologies applied in COVID-19 infections have shown that macrophages and monocytes are more abundant over other immune cells in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC). In addition, the cytokines produced by monocytes and macrophages modulate the response of the immune system in patients with COVID-19. For instance, there is evidence supporting that monocytes in severe patients have a lower expression of the human leukocyte antigen HLA-DRB1, which represses the activation of the immune response through the low production of foreign peptides. Besides, monocytes have a lower expression of interferon-stimulated genes in severe COVID-19 patients, resulting in the delay of the interferon response against SARS-CoV-2. Furthermore, macrophages in severe COVID-19 patients are associated with overexpression of pro-inflammatory genes when compared with moderate COVID-19 patients. Altogether, these and other findings highlight the remarkable role that monocytes and macrophage polarization have in the progression of the disease. diff --git a/content/pubs/Mathematics_2021.md b/content/pubs/Mathematics_2021.md new file mode 100644 index 0000000..ff76c4c --- /dev/null +++ b/content/pubs/Mathematics_2021.md @@ -0,0 +1,11 @@ ++++ +authors = ["Aarón Vázquez-Jiménez","Osbaldo Resendis-Antonio"] +title = "Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seq" +journal= "Mathematics" +what = "article" +doi="10.3390/math9192515" +pubmed = " " +date = "2021-10-07" ++++ + +The single-cell RNA-seq allows exploring the transcriptome for one cell at a time. By doing so, cellular regulation is pictured. One limitation is the dropout events phenomenon, where a gene is observed at a low or moderate expression level in one cell but not detected in another. Dropouts obscure legitimate biological heterogeneity leading to the description of a small fraction of the meaningful relations. We used a stochastic approach to model the Reverse Transcription Polymerase Chain Reaction (RT-PCR) kinetic, in which we contemplated the temperature profile, RT-PCR duration, and reaction rates. By studying the underlying biochemical processes of RT-PCR, using a computational and analytical framework, we show a minimal amount of RNA to avoid dropout events. We further use this fact to characterize the limits in the dispersion reduction. Dispersion asymptotically decreases as the RNA initial value increases. Despite always being a basal dispersion, their decreasing speed is modulated mainly by the degradation rates, particularly for the RNA. We concluded that the critical step into the RT-PCR is the RT phase due to the fragile nature of the RNA. We propose that limiting RNA degradation might ensure that the portraited transcriptional landscape is unbiased by technical error. diff --git a/content/pubs/Mycrobiota_diabetes.md b/content/pubs/Mycrobiota_diabetes.md new file mode 100644 index 0000000..2b8e06a --- /dev/null +++ b/content/pubs/Mycrobiota_diabetes.md @@ -0,0 +1,17 @@ ++++ +authors = ["Diego A. Esquivel-Hernández", "Yoscelina Estrella Martínez-López", "Jean Paul Sánchez-Castañeda", "Daniel Neri-Rosario", "Crístian Padrón-Manrique", "David Giron-Villalobos", "Crístian Mendoza-Ortíz", "Osbaldo Resendis-Antonio"] +title = "A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort" +journal = "Front. Endocrinol" +what = "article" +doi = "10.3389/fendo.2023.1128767" +pubmed = "https://pubmed.ncbi.nlm.nih.gov/37124757/" +date = "2023-04-12" ++++ + +Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. + +Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). + +Results: By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. + +Discussion: Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine. \ No newline at end of file diff --git a/content/pubs/PM15680508.md b/content/pubs/PM15680508.md new file mode 100644 index 0000000..c588bde --- /dev/null +++ b/content/pubs/PM15680508.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Julio A Freyre-González", "Ricardo Menchaca-Méndez", "Rosa M Gutiérrez-Ríos", "Agustino Martínez-Antonio", "Cristhian Avila-Sánchez", "Julio Collado-Vides"] +title = "Modular analysis of the transcriptional regulatory network of E. coli." +journal = "Trends in genetics : TIG" +what = "article" +doi = "10.1016/j.tig.2004.11.010" +pubmed = "15680508" +date = "2005-02-01" +keywords = [] ++++ + +The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules. \ No newline at end of file diff --git a/content/pubs/PM17188715.md b/content/pubs/PM17188715.md new file mode 100644 index 0000000..9da1e68 --- /dev/null +++ b/content/pubs/PM17188715.md @@ -0,0 +1,12 @@ ++++ +authors = ["Maximino Aldana", "Enrique Balleza", "Stuart Kauffman", "Osbaldo Resendiz"] +title = "Robustness and evolvability in genetic regulatory networks." +journal = "Journal of theoretical biology" +what = "article" +doi = "10.1016/j.jtbi.2006.10.027" +pubmed = "17188715" +date = "2006-12-26" +keywords = [] ++++ + +Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. Here we address this problem by studying the robustness and evolvability of the attractor landscape of genetic regulatory network models under the process of gene duplication followed by divergence. We show that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear. The above is true in a variety of network topologies and even for the case of extreme divergence in which the duplicate gene bears almost no relation with its parent. Our results indicate that networks operating close to the so-called "critical regime" exhibit the maximum robustness and evolvability simultaneously. \ No newline at end of file diff --git a/content/pubs/PM17559662.md b/content/pubs/PM17559662.md new file mode 100644 index 0000000..aefec28 --- /dev/null +++ b/content/pubs/PM17559662.md @@ -0,0 +1,12 @@ ++++ +authors = ["Rosa María Gutierrez-Ríos", "Julio A Freyre-Gonzalez", "Osbaldo Resendis", "Julio Collado-Vides", "Milton Saier", "Guillermo Gosset"] +title = "Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli." +journal = "BMC microbiology" +what = "article" +doi = "10.1186/1471-2180-7-53" +pubmed = "17559662" +date = "2007-06-15" +keywords = [] ++++ + +Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses. \ No newline at end of file diff --git a/content/pubs/PM17922569.md b/content/pubs/PM17922569.md new file mode 100644 index 0000000..29d3d65 --- /dev/null +++ b/content/pubs/PM17922569.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Jennifer L Reed", "Sergio Encarnación", "Julio Collado-Vides", "Bernhard Ø Palsson"] +title = "Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli." +journal = "PLoS computational biology" +what = "article" +doi = "10.1371/journal.pcbi.0030192" +pubmed = "17922569" +date = "2007-10-10" +keywords = [] ++++ + +Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation. \ No newline at end of file diff --git a/content/pubs/PM19076632.md b/content/pubs/PM19076632.md new file mode 100644 index 0000000..b1b1396 --- /dev/null +++ b/content/pubs/PM19076632.md @@ -0,0 +1,12 @@ ++++ +authors = ["Enrique Balleza", "Lucia N López-Bojorquez", "Agustino Martínez-Antonio", "Osbaldo Resendis-Antonio", "Irma Lozada-Chávez", "Yalbi I Balderas-Martínez", "Sergio Encarnación", "Julio Collado-Vides"] +title = "Regulation by transcription factors in bacteria: beyond description." +journal = "FEMS microbiology reviews" +what = "article" +doi = "10.1111/j.1574-6976.2008.00145.x" +pubmed = "19076632" +date = "2008-12-17" +keywords = [] ++++ + +Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema. \ No newline at end of file diff --git a/content/pubs/PM19305506.md b/content/pubs/PM19305506.md new file mode 100644 index 0000000..d655696 --- /dev/null +++ b/content/pubs/PM19305506.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio"] +title = "Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking." +journal = "PloS one" +what = "article" +doi = "10.1371/journal.pone.0004967" +pubmed = "19305506" +date = "2009-03-24" +keywords = [] ++++ + +Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network. \ No newline at end of file diff --git a/content/pubs/PM20811631.md b/content/pubs/PM20811631.md new file mode 100644 index 0000000..7e6e2c8 --- /dev/null +++ b/content/pubs/PM20811631.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Alberto Checa", "Sergio Encarnación"] +title = "Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect." +journal = "PloS one" +what = "article" +doi = "10.1371/journal.pone.0012383" +pubmed = "20811631" +date = "2010-09-03" +keywords = [] ++++ + +Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. \ No newline at end of file diff --git a/content/pubs/PM21696634.md b/content/pubs/PM21696634.md new file mode 100644 index 0000000..41ca37e --- /dev/null +++ b/content/pubs/PM21696634.md @@ -0,0 +1,12 @@ ++++ +authors = ["Juan Carlos Higareda-Almaraz", "María del Rocío Enríquez-Gasca", "Magdalena Hernández-Ortiz", "Osbaldo Resendis-Antonio", "Sergio Encarnación-Guevara"] +title = "Proteomic patterns of cervical cancer cell lines, a network perspective." +journal = "BMC systems biology" +what = "article" +doi = "10.1186/1752-0509-5-96" +pubmed = "21696634" +date = "2011-06-24" +keywords = [] ++++ + +Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis. \ No newline at end of file diff --git a/content/pubs/PM21801415.md b/content/pubs/PM21801415.md new file mode 100644 index 0000000..7e7d12c --- /dev/null +++ b/content/pubs/PM21801415.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Magdalena Hernández", "Emmanuel Salazar", "Sandra Contreras", "Gabriel Martínez Batallar", "Yolanda Mora", "Sergio Encarnación"] +title = "Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling." +journal = "BMC systems biology" +what = "article" +doi = "10.1186/1752-0509-5-120" +pubmed = "21801415" +date = "2011-08-02" +keywords = [] ++++ + +Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process. \ No newline at end of file diff --git a/content/pubs/PM23071431.md b/content/pubs/PM23071431.md new file mode 100644 index 0000000..0184f8d --- /dev/null +++ b/content/pubs/PM23071431.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Magdalena Hernández", "Yolanda Mora", "Sergio Encarnación"] +title = "Functional modules, structural topology, and optimal activity in metabolic networks." +journal = "PLoS computational biology" +what = "article" +doi = "10.1371/journal.pcbi.1002720" +pubmed = "23071431" +date = "2012-10-17" +keywords = [] ++++ + +Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions. \ No newline at end of file diff --git a/content/pubs/PM23316163.md b/content/pubs/PM23316163.md new file mode 100644 index 0000000..95d0cee --- /dev/null +++ b/content/pubs/PM23316163.md @@ -0,0 +1,12 @@ ++++ +authors = ["Claudia E Hernández Patiño", "Gustavo Jaime-Muñoz", "Osbaldo Resendis-Antonio"] +title = "Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells." +journal = "Frontiers in physiology" +what = "article" +doi = "10.3389/fphys.2012.00481" +pubmed = "23316163" +date = "2013-01-15" +keywords = ["cancer metabolic phenotype", "computational modeling of metabolism", "constraint-based modeling", "genome scale metabolic reconstruction", "high throughput biology"] ++++ + +One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues. \ No newline at end of file diff --git a/content/pubs/PM24747697.md b/content/pubs/PM24747697.md new file mode 100644 index 0000000..837bf20 --- /dev/null +++ b/content/pubs/PM24747697.md @@ -0,0 +1,12 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Carolina González-Torres", "Gustavo Jaime-Muñoz", "Claudia Erika Hernandez-Patiño", "Carlos Felipe Salgado-Muñoz"] +title = "Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer." +journal = "Seminars in cancer biology" +what = "article" +doi = "10.1016/j.semcancer.2014.04.003" +pubmed = "24747697" +date = "2014-04-22" +keywords = ["Cancer metabolism", "Mathematical models", "P4 medicine", "Systems biology: Constraint-based modeling"] ++++ + +Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century. \ No newline at end of file diff --git a/content/pubs/PM27335086.md b/content/pubs/PM27335086.md new file mode 100644 index 0000000..435cfdc --- /dev/null +++ b/content/pubs/PM27335086.md @@ -0,0 +1,12 @@ ++++ +authors = ["Christian Diener", "Felipe Muñoz-Gonzalez", "Sergio Encarnación", "Osbaldo Resendis-Antonio"] +title = "The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome." +journal = "Scientific reports" +what = "article" +doi = "10.1038/srep28415" +pubmed = "27335086" +date = "2016-06-24" +keywords = [] ++++ + +During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway. \ No newline at end of file diff --git a/content/pubs/PM27616995.md b/content/pubs/PM27616995.md new file mode 100644 index 0000000..c6ff486 --- /dev/null +++ b/content/pubs/PM27616995.md @@ -0,0 +1,11 @@ ++++ +authors = ["Mahdi Jalili", "Ali Salehzadeh-Yazdi", "Shailendra Gupta", "Olaf Wolkenhauer", "Marjan Yaghmaie", "Osbaldo Resendis-Antonio", "Kamran Alimoghaddam"] +title = "Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks." +journal = "Frontiers in physiology" +what = "article" +doi = "10.3389/fphys.2016.00375" +pubmed = "27616995" +date = "2016-09-13" +keywords = ["biological centrality", "biological network", "centrality", "essentiality", "topological network analysis"] ++++ + diff --git a/content/pubs/PM28018236.md b/content/pubs/PM28018236.md new file mode 100644 index 0000000..ecdde7b --- /dev/null +++ b/content/pubs/PM28018236.md @@ -0,0 +1,12 @@ ++++ +authors = ["Alejandra V Contreras", "Benjamin Cocom-Chan", "Georgina Hernandez-Montes", "Tobias Portillo-Bobadilla", "Osbaldo Resendis-Antonio"] +title = "Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine." +journal = "Frontiers in physiology" +what = "article" +doi = "10.3389/fphys.2016.00606" +pubmed = "28018236" +date = "2016-12-27" +keywords = ["cancer metabolism", "metabolome", "microbiome", "next generation sequencing (NGS)", "precision medicine", "systems integration"] ++++ + +It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology. \ No newline at end of file diff --git a/content/pubs/PM28082911.md b/content/pubs/PM28082911.md new file mode 100644 index 0000000..f07f204 --- /dev/null +++ b/content/pubs/PM28082911.md @@ -0,0 +1,12 @@ ++++ +authors = ["Christian Diener", "Osbaldo Resendis-Antonio"] +title = "Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies." +journal = "Frontiers in physiology" +what = "article" +doi = "10.3389/fphys.2016.00644" +pubmed = "28082911" +date = "2017-01-14" +keywords = ["NCI60", "TCGA", "flux balance analysis", "personalized medicine", "proliferation", "systems biology"] ++++ + +Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients. \ No newline at end of file diff --git a/content/pubs/PM28410570.md b/content/pubs/PM28410570.md new file mode 100644 index 0000000..39f3527 --- /dev/null +++ b/content/pubs/PM28410570.md @@ -0,0 +1,12 @@ ++++ +authors = ["Cecilio Valadez-Cano", "Roberto Olivares-Hernández", "Osbaldo Resendis-Antonio", "Alexander DeLuna", "Luis Delaye"] +title = "Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora." +journal = "BMC evolutionary biology" +what = "article" +doi = "10.1186/s12862-017-0947-6" +pubmed = "28410570" +date = "2017-04-16" +keywords = ["Adaptation", "Endosymbiont", "Metabolic evolution", "Metabolic integration"] ++++ + +Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore. \ No newline at end of file diff --git a/content/pubs/PM28536537.md b/content/pubs/PM28536537.md new file mode 100644 index 0000000..6e15fc3 --- /dev/null +++ b/content/pubs/PM28536537.md @@ -0,0 +1,12 @@ ++++ +authors = ["Nora A Gutierrez Najera", "Osbaldo Resendis-Antonio", "Humberto Nicolini"] +title = "\"Gestaltomics\": Systems Biology Schemes for the Study of Neuropsychiatric Diseases." +journal = "Frontiers in physiology" +what = "article" +doi = "10.3389/fphys.2017.00286" +pubmed = "28536537" +date = "2017-05-26" +keywords = ["diagnosis", "lung cancer", "omics", "psychiatry", "systems biology"] ++++ + +The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the "psychiatric phenotype" is to provide an improved vision of the shape of the phenotype as it is visualized by "Gestalt" psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term "Gestaltomics" as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed. \ No newline at end of file diff --git a/content/pubs/PM29704665.md b/content/pubs/PM29704665.md new file mode 100644 index 0000000..23b3bf7 --- /dev/null +++ b/content/pubs/PM29704665.md @@ -0,0 +1,12 @@ ++++ +authors = ["Vanessa L Hale", "Patricio Jeraldo", "Michael Mundy", "Janet Yao", "Gary Keeney", "Nancy Scott", "E Heidi Cheek", "Jennifer Davidson", "Megan Green", "Christine Martinez", "John Lehman", "Chandra Pettry", "Erica Reed", "Kelly Lyke", "Bryan A White", "Christian Diener", "Osbaldo Resendis-Antonio", "Jaime Gransee", "Tumpa Dutta", "Xuan-Mai Petterson", "Lisa Boardman", "David Larson", "Heidi Nelson", "Nicholas Chia"] +title = "Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer." +journal = "Methods (San Diego, Calif.)" +what = "article" +doi = "10.1016/j.ymeth.2018.04.024" +pubmed = "29704665" +date = "2018-04-29" +keywords = [] ++++ + +Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue. \ No newline at end of file diff --git a/content/pubs/PM30376889.md b/content/pubs/PM30376889.md new file mode 100644 index 0000000..6b14ed5 --- /dev/null +++ b/content/pubs/PM30376889.md @@ -0,0 +1,12 @@ ++++ +authors = ["Vanessa L. Hale", "Patricio Jeraldo", "Jun Chen", "Michael Mundy", "Janet Yao", "Sambhawa Priya", "Gary Keeney", "Kelly Lyke", "Jason Ridlon", "Bryan A. White", "Amy J. French", "Stephen N. Thibodeau", "Christian Diener", "Osbaldo Resendis-Antonio", "Jaime Gransee", "Tumpa Dutta", "Xuan-Mai Petterson", "Jaeyun Sung", "Ran Blekhman", "Lisa Boardman", "David Larson", "Heidi Nelson", "Nicholas Chia"] +title = "Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers." +journal = "Genome Medicine" +what = "article" +doi = "10.1186/s13073-018-0586-6" +pubmed = "30376889" +date = "2018-10-31" +keywords = [] ++++ + +Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC.Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers. diff --git a/content/pubs/Physiological_Networks.md b/content/pubs/Physiological_Networks.md new file mode 100644 index 0000000..9d2a933 --- /dev/null +++ b/content/pubs/Physiological_Networks.md @@ -0,0 +1,11 @@ ++++ +authors = ["Antonio Barajas-Martínez", "Roopa Mehta", "Elizabeth Ibarra-Coronado", "Ruben Fossion", "Vania J Martínez Garcés", "Monserrat Ramírez Arellano", "Ibar A González Alvarez", "Yamilet Viana Moncada Bautista", "Omar Y Bello-Chavolla", "Natalia Ramírez Pedraza", "Bethsabel Rodríguez Encinas", "Carolina Isabel Pérez Carrión", "María Isabel Jasso Ávila", "Jorge Carlos Valladares-García", "Pablo Esteban Vanegas-Cedillo", "Diana Hernández Juárez", "Arsenio Vargas-Vázquez", "Neftali Eduardo Antonio-Villa", "Paloma Almeda-Valdes", "Osbaldo Resendis-Antonio", "Marcia Hiriart", "Alejandro Frank", "Carlos A Aguilar-Salinas", "Ana Leonor Rivera"] +title = "Physiological Network Is Disrupted in Severe COVID-19" +journal = "Frontiers in physiology" +what = "article" +doi = "10.3389/fphys.2022.848172" +pubmed = "" +date = "2022-03-10" ++++ + +The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism’s homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else. The goal of this study is to determine the differences in the relationships that link physiological variables that characterize the elderly and men, and those that characterize fatal outcomes in young men. To accomplish this, we examined a database of patients with moderate to severe COVID-19 (471 men and 277 women) registered at the “Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán” in March 2020. The sample was stratified by outcome, age, and sex. Physiological networks were built using 67 physiological variables (vital signs, anthropometric, hematic, biochemical, and tomographic variables) recorded upon hospital admission. Individual variables and system behavior were examined by descriptive statistics, differences between groups, principal component analysis, and network analysis. We show how topological network properties, particularly clustering coefficient, become disrupted in disease. Finally, anthropometric, metabolic, inflammatory, and pulmonary cluster interaction characterize the deceased young male group. \ No newline at end of file diff --git a/content/pubs/Postprandial_Glycemic.md b/content/pubs/Postprandial_Glycemic.md new file mode 100644 index 0000000..925d1d7 --- /dev/null +++ b/content/pubs/Postprandial_Glycemic.md @@ -0,0 +1,11 @@ ++++ +authors = ["Rocio Guizar-Heredia", "Lilia G. Noriega", "Ana Leonor Rivera", "Osbaldo Resendis-Antonio", "Martha Guevara-Cruz", "Nimbe Torres", "Armando R. Tovar"] +title = "A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota" +journal = "Archives of Medical Research" +what = "article" +doi = "10.1016/j.arcmed.2023.02.007." +pubmed = "https://pubmed.ncbi.nlm.nih.gov/36990891/" +date = "2023-01-01" ++++ + +A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases. \ No newline at end of file diff --git a/content/pubs/Spermiogenesis.md b/content/pubs/Spermiogenesis.md new file mode 100644 index 0000000..e152377 --- /dev/null +++ b/content/pubs/Spermiogenesis.md @@ -0,0 +1,11 @@ ++++ +authors = ["Ulises Torres-Flores", "Fernanda Díaz-Espinosa", "Taydé López-Santaella", "Rosa Rebollar-Vega", "Aarón Vázquez-Jiménez", "Ian J. Taylor", "Rosario Ortiz-Hernández", "Olga M. Echeverría", "Gerardo H. Vázquez-Nin", "María Concepción Gutierrez-Ruiz", "Inti Alberto De la Rosa-Velázquez", "Osbaldo Resendis-Antonio", "Abrahan Hernández-Hernandez"] +title = "Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencing" +journal = "Front. Cell Dev. Biol." +what = "article" +doi = "10.3389/fcell.2023.1119514" +pubmed = "https://pubmed.ncbi.nlm.nih.gov/37065848/" +date = "2023-03-30" ++++ + +CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm. In the early stages of spermatogenesis, transcriptional alterations are mild. As germ cells go through the specialization stage or spermiogenesis, transcriptional profiles become more altered. We found morphology defects in spermatids that support the alterations in their transcriptional profiles. Altogether, our study sheds light on the contribution of CTCF to the phenotype of male gametes and provides a fundamental description of its role at different stages of spermiogenesis. \ No newline at end of file diff --git a/content/pubs/Type2_diabetes.md b/content/pubs/Type2_diabetes.md new file mode 100644 index 0000000..cf54776 --- /dev/null +++ b/content/pubs/Type2_diabetes.md @@ -0,0 +1,11 @@ ++++ +authors = ["Yoscelina Estrella Martínez-López", "Diego A Esquivel-Hernández", "Jean Paul Sánchez-Castañeda", "Daniel Neri-Rosario", "Rodolfo Guardado-Mendoza", "Osbaldo Resendis-Antonio"] +title = "Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era" +journal = "Gut Microbes" +what = "article" +doi = "10.1080/19490976.2022.2111952" +pubmed = "" +date = "2022-08-15" ++++ + +The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way. diff --git a/content/pubs/Uncoding_macrophages.md b/content/pubs/Uncoding_macrophages.md new file mode 100644 index 0000000..59acce9 --- /dev/null +++ b/content/pubs/Uncoding_macrophages.md @@ -0,0 +1,11 @@ ++++ +authors = ["Ugo Avila-Ponce de León", "Aarón Vázquez-Jiménez", "Pablo Padilla-Longoria", "Osbaldo Resendis-Antonio"] +title = "Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach" +journal = "Front Immunol" +what = "article" +doi = "10.3389/fimmu.2023.1150890" +pubmed = "https://pubmed.ncbi.nlm.nih.gov/37283734/" +date = "2023-05-22" ++++ + +The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment. \ No newline at end of file diff --git a/content/pubs/cancer_a_complex_disease.md b/content/pubs/cancer_a_complex_disease.md new file mode 100644 index 0000000..4b958ef --- /dev/null +++ b/content/pubs/cancer_a_complex_disease.md @@ -0,0 +1,18 @@ ++++ +authors = ["Elena R. Alvarez-Buylla","Juan Carlos Balandran","Jose Luis Caldu-Primo","Jose Davila-Velderrain","Jennifer Enciso","Enrique Hernandez-Lemus","Lucia S. Lopez Castillo","Juan Carlos Martinez-Garcia","Nancy R. Mejia-Dominguez","Leticia R. Paiva","Rosana Pelayo","Osbaldo Resendis-Antonio","Octavio Valadez-Blanco"] +title = "Cancer: a complex disease" +journal = "Copit-Arxives" +what = "book" +doi = "" +pubmed = "" +date = "2018-12-01T00:00:00" +isbn="978-1-938128-15-8" ++++ + +![](/media/cancer_a_complex_disease_ebook.png) + +*This is an EBook can +be [downloaded for free](http://scifunam.fisica.unam.mx/mir/copit/TS0017EN/TS0017EN.pdf).* + +The study of complex systems and their related phenomena has become a major research venue in the recent years and it is commonly regarded as an important part of the scientific revolution developing through the 21st century. The science of complexity is concerned with the laws of operation and evolution of systems formed by many locally interacting elements that produce collective order at spatiotemporal scales larger than that of the single constitutive elements. This new thinking, that explores formally the emergence of spontaneous higher order and feedback hierarchies, has been particularly successful in the biological sciences. One particular life-threatening disease in humans, overwhelmingly common in the modern world is cancer. It is regarded as a collection of phenomena involving anomalous cell growth caused by an underlying genetic instability with the potential to spread to other parts of the human body. In the present book, a group of well recognized specialists discuss new ideas about the disease. These authors coming from solid backgrounds in physics, mathematics, medicine, molecular and cell biology, genetics and anthropology have dedicated their time to write an authoritative free-available text published under the open access philosophy that hopefully would be in the front-line struggle against cancer, a complex disease. + diff --git a/content/pubs/ecology_gut.md b/content/pubs/ecology_gut.md new file mode 100644 index 0000000..4d3afb2 --- /dev/null +++ b/content/pubs/ecology_gut.md @@ -0,0 +1,19 @@ ++++ +authors = ["Diego Armando Esquivel-Hernández", "Yoscelina Estrella Martínez-López", "Jean Paul Sánchez-Castañeda", "Daniel Neri-Rosario", "Cristian Padrón-Manrique", "David Girón-Villalobos", "Cristian Mendoza-Ortíz", "Osbaldo Resendis-Antonio"] +title = "prePrint: A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohort" +journal = "Research Square" +what = "article" +doi = "10.21203/rs.3.rs-1848436/v1" +pubmed = "" +date = "2022-07-20" ++++ + +Background + +The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. +Results + +Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. +Conclusions + +Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine. diff --git a/content/pubs/editorial_frontiers.md b/content/pubs/editorial_frontiers.md new file mode 100644 index 0000000..214a527 --- /dev/null +++ b/content/pubs/editorial_frontiers.md @@ -0,0 +1,14 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Christian Diener"] +title = "Editorial: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer" +journal = "Frontiers In Physiology" +what = "article" +doi = "10.3389/fphys.2017.00537" +pubmed = "" +date = "2017-07-28T12:00:00" ++++ + +This is the Editorial for our Frontiers Research Topic "Systems Biology and +the challenge of deciphering the metabolic mechanisms underlying cancer". + +The corresponding E-Book will be available soon. diff --git a/content/pubs/encyclopedia.md b/content/pubs/encyclopedia.md new file mode 100644 index 0000000..ea41147 --- /dev/null +++ b/content/pubs/encyclopedia.md @@ -0,0 +1,13 @@ ++++ +authors = ["Werner Dubitzky", "Olaf Wolkenhauer", "Kwang-Hyun Cho", "Hiroki Yokota"] +title = "Encyclopedia of Systems Biology" +journal = "" +what = "book" +doi = "" +pubmed = "" +date = "2013-06-01T00:00:00" ++++ + +![](/media/encyclopedia.jpg) + +The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology. diff --git a/content/pubs/frontiers_ebook.md b/content/pubs/frontiers_ebook.md new file mode 100644 index 0000000..23080be --- /dev/null +++ b/content/pubs/frontiers_ebook.md @@ -0,0 +1,39 @@ ++++ +authors = ["Osbaldo Resendis-Antonio", "Christian Diener"] +title = "Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer " +journal = "Frontiers in Physiology" +what = "book" +doi = "" +pubmed = "" +date = "2017-11-27T00:00:00" ++++ + +![](/media/frontiers_ebook.jpg) + +*This is an EBook compendium of the respective Frontiers Research Topic and can +be [downloaded for free](https://www.frontiersin.org/books/Systems_Biology_and_the_Challenge_of_Deciphering_the_MetabolicMechanisms_Underlying_Cancer/1387#nogo).* + +Since the discovery of the Warburg effect in the 1920s cancer has been tightly +associated with the genetic and metabolic state of the cell. One of the +hallmarks of cancer is the alteration of the cellular metabolism in order to +promote proliferation and undermine cellular defense mechanisms such as +apoptosis or detection by the immune system. However, the strategies by which +this is achieved in different cancers and sometimes even in different patients +of the same cancer is very heterogeneous, which hinders the design of general +treatment options. Recently, there has been an ongoing effort to study this +phenomenon on a genomic scale in order to understand the causality underlying +the disease. Hence, current “omics” technologies have contributed to identify +and monitor different biological pieces at different biological levels, such as +genes, proteins or metabolites. These technological capacities have provided us +with vast amounts of clinical data where a single patient may often give rise +to various tissue samples, each of them being characterized in detail by +genomescale data on the sequence, expression, proteome and metabolome level. +Data with such detail poses the imminent problem of extracting meaningful +interpretations and translating them into specific treatment options. To this +purpose, Systems Biology provides a set of promising computational tools in +order to decipher the mechanisms driving a healthy cell’s metabolism into a +cancerous one. However, this enterprise requires bridging the gap between large +data resources, mathematical analysis and modeling specifically designed to +work with the available data. This is by no means trivial and requires high +levels of communication and adaptation between the experimental and theoretical +side of research. diff --git a/content/pubs/mbPhenix.md b/content/pubs/mbPhenix.md new file mode 100644 index 0000000..a940e49 --- /dev/null +++ b/content/pubs/mbPhenix.md @@ -0,0 +1,15 @@ ++++ +authors = ["Cristian Padron-Manrique", "Aaron Vazquez-Jimenez", "Diego A Esquivel-Hernandez", "Yoscelina Estrella Martinez-Lopez", "Daniel Neri-Rosario", "Jean Paul Sanchez", "David Giron-Villalobos", "Osbaldo Resendis-Antonio"] +title = "mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data" +journal = "Bioinformatics" +what = "article" +doi = "10.1093/bioinformatics/btad706" +pubmed = "" +date = "2023-12-01" ++++ +Motivation +Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. +Results +We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails. +Availability and implementation +The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub). diff --git a/content/pubs/memote.md b/content/pubs/memote.md new file mode 100644 index 0000000..7514a7e --- /dev/null +++ b/content/pubs/memote.md @@ -0,0 +1,13 @@ ++++ +authors = ["Christian Lieven", "Moritz E. Beber, Brett G. Olivier", "Frank T. Bergmann", "Meric Ataman", "Parizad Babaei", "Jennifer A. Bartell", "Lars M. Blank", "Siddharth Chauhan", "Kevin Correia", "Christian Diener", "Andreas Dräger", "Birgitta E. Ebert", "Janaka N. Edirisinghe", "Jose P. Faria", "Adam Feist", "Georgios Fengos", "Ronan M. T. Fleming", "Beatriz García-Jiménez", "Vassily Hatzimanikatis", "Wout van Helvoirt", "Christopher S. Henry", "Henning Hermjakob", "Markus J. Herrgård", "Hyun Uk Kim", "Zachary King", "Jasper J. Koehorst", "Steffen Klamt", "Edda Klipp", "Meiyappan Lakshmanan", "Nicolas Le Novère", "Dong-Yup Lee", "Sang Yup Lee", "Sunjae Lee", "Nathan E. Lewis", "Hongwu Ma", "Daniel Machado", "Radhakrishnan Mahadevan", "Paulo Maia", "Adil Mardinoglu", "Gregory L. Medlock", "Jonathan M. Monk", "Jens Nielsen", "Lars Keld Nielsen", "Juan Nogales, Intawat Nookaew", "Osbaldo Resendis-Antonio", "Bernhard O. Palsson", "Jason A. Papin", "Kiran R. Patil", "Mark Poolman", "Nathan D. Price", "Anne Richelle", "Isabel Rocha", "Benjamin J. Sanchez", "Peter J. Schaap", "Rahuman S. Malik Sheriff", "Saeed Shoaie", "Nikolaus Sonnenschein", "Bas Teusink", "Paulo Vilaça", "Jon Olav Vik, Judith A. Wodke", "Joana C. Xavier", "Qianqian Yuan", "Maksim Zakhartsev", "Cheng Zhang"] +title = "Memote: A community driven effort towards a standardized genome-scale metabolic model test suite" +journal = "Nature Biotechnology" +what = "article" +doi = "10.1101/350991" +pubmed = "32123384" +date = "2020-03-02" ++++ + +Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. +Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection. +Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models. diff --git a/content/pubs/micom.md b/content/pubs/micom.md new file mode 100644 index 0000000..0722f5f --- /dev/null +++ b/content/pubs/micom.md @@ -0,0 +1,11 @@ ++++ +authors = ["Christian Diener", "Osbaldo Resendis-Antonio"] +title = "Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota." +journal = "Msystems" +what = "article" +doi = " 10.1128/mSystems.00606-19" +pubmed = "31964767" +date = "2020-02-04" ++++ + +Alterations in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn's disease and diabetes. However, establishing the causality between the microbial composition and disease remains a challenge. We introduce a strategy based on metabolic models of complete microbial gut communities in order to derive the particular metabolic consequences of the microbial composition for the diabetic gut in a balanced cohort of 186 individuals. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that allows growth for the majority of observed taxa in a sample. We also integrated various additional constraints such as diet and the measured abundances of microbial species to derive the resulting metabolic alterations for individual metagenomic samples. In particular, we show that growth rates vary greatly across samples and that there exists a network of bacteria implicated in health and disease that mutually influence each others growth rates. Studying individual exchange fluxes between the microbiota and the gut lumen we observed that consumption of metabolites by the microbiota follows a niche structure whereas production of short chain fatty acids by the microbiota was highly sample-specific and was altered in type 2 diabetes and restored after metformin treatment in samples from danish individuals. Additionally, we found that production of butyrate could not be easily influenced by single-target interventions. diff --git a/content/pubs/quantitative_modeling.md b/content/pubs/quantitative_modeling.md new file mode 100644 index 0000000..c827569 --- /dev/null +++ b/content/pubs/quantitative_modeling.md @@ -0,0 +1,20 @@ ++++ +authors = ["Luis Olivarez-Quiroz", "Osbaldo Resendis-Antonio"] +title = "Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues" +journal = "" +what = "book" +doi = "" +pubmed = "" +date = "2018-03-01" ++++ + +![](/media/quantitative_models.jpg) + +This book presents cutting-edge research on the use of physical and +mathematical formalisms to model and quantitatively analyze biological +phenomena ranging from microscopic to macroscopic systems. The systems +discussed in this compilation cover protein folding pathways, gene regulation +in prostate cancer, quorum sensing in bacteria to mathematical and physical +descriptions to analyze anomalous diffusion in patchy environments and the +physical mechanisms that drive active motion in large sets of particles, both +fundamental descriptions that can be applied to different phenomena in biology. diff --git a/content/pubs/scientificReports2020.md b/content/pubs/scientificReports2020.md new file mode 100644 index 0000000..63f0201 --- /dev/null +++ b/content/pubs/scientificReports2020.md @@ -0,0 +1,11 @@ ++++ +authors = ["Erick Andrés Muciño-Olmos", "Aarón Vázquez-Jiménez", "Ugo Avila-Ponce de León", "Meztli Matadamas-Guzman", "Vilma Maldonado", "Tayde López-Santaella", "Abrahan Hernández-Hernández", "Osbaldo Resendis-Antonio"] +title = "Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq" +journal = "Scientific Reports" +what = "article" +doi = "10.1038/s41598-020-69026-7" +pubmed = "32728097" +date = "2020-07-02" ++++ + +Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors. diff --git a/content/pubs/symbiotic_endophytes.md b/content/pubs/symbiotic_endophytes.md new file mode 100644 index 0000000..08705e6 --- /dev/null +++ b/content/pubs/symbiotic_endophytes.md @@ -0,0 +1,14 @@ ++++ +authors = ["Ricardo Aroca"] +title = "Symbiotic Endophytes" +journal = "" +what = "book" +doi = "" +pubmed = "" +date = "2013-01-01T00:00:00" ++++ + +![](/media/endophytes.jpg) + +This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. +Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields. diff --git a/content/software/corda.md b/content/software/corda.md new file mode 100644 index 0000000..e17e1a7 --- /dev/null +++ b/content/software/corda.md @@ -0,0 +1,21 @@ ++++ +date = 2017-05-01 +authors = ["Christian Diener", "Osbaldo Resendis Antonio"] +title = "CORDA for Python" +repo = "https://github.com/resendislab/corda" +docs = "https://resendislab.github.io/corda" +logo = "" ++++ + + +This is a Python implementation based on the papers of Schultz et. al. with +some added optimizations. It is based on the publications of Schultz et. al. +\[[1](http://journals.plos.org/ploscompbiol/article/authors?id=10.1371%2Fjournal.pcbi.1004808), [2](http://dx.doi.org/10.1142/9789813207813_0045)\]. + + +CORDA, short for Cost Optimization Reaction Dependency Assessment is a +method for the reconstruction of metabolic networks from a given +reference model (a database of all known reactions) and a confidence +mapping for reactions. It allows you to reconstruct metabolic models for +tissues, patients or specific experimental conditions from a set of +transcription or proteome measurements. diff --git a/content/software/dycone.md b/content/software/dycone.md new file mode 100644 index 0000000..efa734e --- /dev/null +++ b/content/software/dycone.md @@ -0,0 +1,13 @@ ++++ +date = 2016-06-01 +authors = ["Christian Diener", "Osbaldo Resendis Antonio"] +title = "dycone" +repo = "https://github.com/cdiener/dycone" +docs = "" +logo = "https://github.com/cdiener/dycone/raw/master/stuff/logo.png" ++++ + +Dycone ("dynamic cone") allows you infer enzymatic regulation from metabolome +measurements. It employs formalisms based on flux and k-cone analysis to connect +metabolome data to distinct regulations of enzyme activity. Most of the +analysis methods can be applied to genome-scale data. diff --git a/content/software/micom.md b/content/software/micom.md new file mode 100644 index 0000000..f81e94c --- /dev/null +++ b/content/software/micom.md @@ -0,0 +1,19 @@ ++++ +date = 2017-10-01 +authors = ["Christian Diener", "Osbaldo Resendis Antonio"] +title = "micom" +repo = "https://github.com/resendislab/micom" +docs = "https://resendislab.github.io/micom" +logo = "https://github.com/resendislab/micom/raw/master/micom.png" ++++ + +micom is a Python package for metabolic modeling of microbial communities +developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio +at the National Institute of Genomic Medicine Mexico. + +micom allows you to construct a community model from a list on input COBRA +models and manages exchange fluxes between individuals and individuals with the +environment. It explicitly accounts for different abundances of individuals in +the community and can thus incorporate data from 16S rRNA sequencing +experiments. It allows optimization with a variety of algorithms modeling the +trade-off between egoistic growth rate maximization and cooperative objectives. diff --git a/content/software/microbiome.md b/content/software/microbiome.md new file mode 100644 index 0000000..fae7368 --- /dev/null +++ b/content/software/microbiome.md @@ -0,0 +1,12 @@ ++++ +date = 2017-08-01 +authors = ["Christian Diener", "Osbaldo Resendis Antonio"] +title = "Our microbiome pipeline" +repo = "https://github.com/resendislab/microbiome" +docs = "https://resendislab.github.io/microbiome" +logo = "" ++++ + +This repository contains the standardized analysis pipeline for 16S and metagenome +data. It serves as a testing ground for what will be required to analyze around +500 samples. diff --git a/data/members.yml b/data/members.yml new file mode 100644 index 0000000..d86a25b --- /dev/null +++ b/data/members.yml @@ -0,0 +1,273 @@ +current: +- name: "Osbaldo Resendis Antonio" + function: "Principal Investigator" + image: "osbaldo.jpg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Aarón Vázquez Jiménez" + function: "Research Associate" + image: "avazquez.jpg" + mail: "avazquez (at) inmegen.gob.mx" + website: "https://www.linkedin.com/in/aar%C3%B3n-v%C3%A1zquez-jim%C3%A9nez-798473159/" + twitter: "inmegen" +- name: "Edgar Mixcoha" + function: "PostDoc" + image: "Edgar.png" + mail: "edgarmixcoha (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Juan José Oropeza Valdez" + function: "PostDoc" + image: "JJ_1.jpg" + mail: "lmcuazjjov (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Crístian Julio Cesar Padrón Manrique" + function: "Ph.D. student" + image: "Cesar.jpg" + mail: "cristianjuliocesar.agualimpia (at) hotmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Jorge E. Arellano" + function: "Ph.D. student" + image: "Jorge.jpg" + mail: "jorge.arellano.bioexp (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "Jorgeabioexp" +- name: "Victor Sanz" + function: "Ph.D. student" + image: "Victor.jpg" + mail: "victor_sanz (at) ciencias.unam.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Perla Alvarado" + function: "M.Sc. student" + image: "Perla.jpg" + mail: "alvarado.lp.01 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Yael Alex Colin" + function: "M.Sc. student" + image: "Yael.jpg" + mail: "yael.a.1406 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Gabriela Montserrat Torres Fernández" + function: "M.Sc. student" + image: "Gabriela.jpg" + mail: "gabrielatofdz(at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Mayavi Peña" + function: "Bsc. student" + image: "Mayavi.jpg" + mail: "mayavii58 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Gonzalo Aguilar" + function: "Logo and webpage designer" + image: "placeholder.svg" + mail: "contacto.ga (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Francisco Giovani Mercado Valle" + function: "M.Sc student" + image: "Jiovani.jpg" + mail: "giovani.mevf (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" + +former: +- name: "Crístian Mendoza Ortiz" + function: "M.Sc. student" + image: "Cristian.jpg" + mail: "cristian.mendoza1996 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Laura Elena Hernández Juárez" + function: "PostDoc" + image: "LE_1.jpg" + mail: "lauraelena.hernandezjuarez (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "David Girón Villalobos" + function: "M.Sc. student" + image: "David.jpeg" + mail: "davidgironvillalobos (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Brenda Loaiza" + function: "PostDoc" + image: "Brenda.jpg" + mail: "cherisloaiza (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Ugo Avila Ponce de León" + function: "Ph.D. student" + image: "ugo.jpg" + mail: "ugo.avila.ponce (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Adrián Santiago Rivera" + function: "Bsc. student" + image: "Adrian.jpg" + mail: "santiagoriveraadriantlc1518 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Jean Paul Sanchez" + function: "M.Sc student" + image: "JP.jpg" + mail: "jeanpaul.sac (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Daniel Neri Rosario" + function: "M.Sc student" + image: "Daniel.jpeg" + mail: "danielneri533 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Estrella Martinez" + function: "Ph.D. student" + image: "Estrella.jpg" + mail: "ruxlia (at) hotmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Thelma Escobedo" + function: "M.Sc student" + image: "Thelma.jpg" + mail: "thelma_et (at) outlook.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Vanessa Salvador Rincón" + function: "M.Sc student" + image: "vanessa.jpg" + mail: "vaneerincon94 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Diego A. Esquivel Hernández" + function: "PostDoc" + image: "Diego.jpg" + mail: "diegoibt27 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Meztli Matadamas" + function: "Ph.D. student" + image: "Mez.jpg" + mail: "meztlimatadamas (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Erick Muciño" + function: "Ph.D. student" + image: "erick.jpg" + mail: "erick.mucol09 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "Mucino_EA" +- name: "Vanessa Salvador Rincón" + function: "M.Sc student" + image: "vanessa.jpg" + mail: "vaneerincon94 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Felipe Muñoz Gonzalez" + function: "Ph.D. student" + image: "felipe.jpg" + mail: "fmunoz (at) lcg.unam.mx" + website: "https://www.linkedin.com/in/felipe-mu%C3%B1oz-393057ab/" + twitter: "pipeMGZ" +- name: "Carlos Ramírez Ramos" + function: "Visiting Researcher" + image: "carlos.jpg" + mail: "cramireza (at) ciencias.unam.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "Jorgeabioexp" +- name: "Fernanda Díaz" + function: "Visiting Researcher" + image: "fernanda.jpg" + mail: "diaz.fernanda12 (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Luis Santiago Mille" + function: "Visiting student" + image: "placeholder.svg" + mail: "lmille (at) bwh.harvard.edu" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Alfredo Rodriguez Arteaga" + function: "M.Sc student (visiting student)" + image: "placeholder.svg" + mail: "alfredo.r.arteaga (at) ciencias.unam.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Christian Diener" + function: "PostDoc" + image: "chris.jpg" + mail: "cdiener (at) inmegen.gob.mx" + website: "https://cdiener.com" + twitter: "thaasophobia" +- name: "Akram Mendez" + function: "PostDoc" + image: "akram.jpg" + mail: "akramsharim (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "akrams" +- name: "Ana Manzano" + function: "visiting student" + image: "placeholder.svg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Carlos Salgado Muñoz" + function: "Ph.D. student" + image: "charly.jpg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Benjamin Cocom Chan" + function: "M.Sc. student" + image: "benjamin.jpg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Ricardo Ramirez" + function: "Visiting Ph.D. student" + image: "placeholder.svg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Cristhian Avila" + function: "PreDoc" + image: "cris.jpg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Erika Hernandez Patiño" + function: "M.Sc. student" + image: "erika.jpg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "cerikah" +- name: "Ixchetl Rojas Benito" + function: "B.Sc. student" + image: "ixchetl.jpg" + mail: "ixchetl14 (at) ciencias.unam.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Layra Cruz" + function: "B.Sc. student" + image: "layra.jpg" + mail: "layraanael (at) gmail.com" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" +- name: "Paola Hernandez" + function: "PosDoc" + image: "paola.jpg" + mail: "paola_hernandez (at) students.kgi.edu" + website: "https://www.linkedin.com/in/aphernandez/" + twitter: "_aphp" +- name: "Lucía Lopez" + function: "B.Sc. student" + image: "lucy.jpg" + mail: "oresendis (at) inmegen.gob.mx" + website: "https://resendislab.inmegen.gob.mx" + twitter: "inmegen" diff --git a/docs/404.html b/docs/404.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/about/index.html b/docs/about/index.html new file mode 100644 index 0000000..2d7d5d9 --- /dev/null +++ b/docs/about/index.html @@ -0,0 +1,101 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ + +
+ +
+
+
+

Who we are

+
+
+

Welcome to the webpage of the Human Systems Biology group in the National +Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is +interdisciplinary and have the objective to develop a systems biology framework +to analyze mainly human diseases and metabolic phenotype in microorganisms +through the use of computational models and high-throughput technologies.

+ +

group photo

+ +

Currently, our laboratory focuses on the analysis of metabolic alterations in +cancer cells by the implementation of genome scale metabolic reconstructions and +assess the predictions in terms of experimental data at different scales. We +have developed some approaches for modeling cancer metabolism and currently we +are developing computational schemes with capacities to integrate metabolome and +RNA-seq data for elucidating metabolic mechanism in cancer cell lines and +tissues.

+ +

Lastly, our laboratory is leading efforts to test the utility of computational +schemes to explore themes related with cancer studies, such as the influence of +microbiome in cancer, the study of the biological networks regulating the +epithelial messenchymal transition and tumor heterogeneity in cancer.

+ +
+ +
+
+
+

Contact

+
+
+ + +

Directions

+ +


+Osbaldo Resendis-Antonio, PhD
+Laboratory in Systems Biology and Human Diseases
+Associated Professor
+Instituto Nacional de Medicina Genomica – INMEGEN
+Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX
+Phone: +52 55 5350 1900 - Ext.1198

+ + + +
+
+
+
+ + + + + + diff --git a/docs/about/index.xml b/docs/about/index.xml new file mode 100644 index 0000000..b60eb63 --- /dev/null +++ b/docs/about/index.xml @@ -0,0 +1,41 @@ + + + + Abouts on Webpage of the Resendis Lab + https://resendislab.github.io/about/ + Recent content in Abouts on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Mon, 05 Dec 2016 14:48:16 -0600 + + + + + + Who we are + https://resendislab.github.io/about/we/ + Mon, 05 Dec 2016 14:48:16 -0600 + + https://resendislab.github.io/about/we/ + Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. +Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales. + + + + Contact + https://resendislab.github.io/about/contact/ + Mon, 01 Jan 1900 00:00:00 +0000 + + https://resendislab.github.io/about/contact/ + Directions +Osbaldo Resendis-Antonio, PhD +Laboratory in Systems Biology and Human Diseases +Associated Professor +Instituto Nacional de Medicina Genomica – INMEGEN +Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX +Phone: +52 55 5350 1900 - Ext.1198 + + + + + \ No newline at end of file diff --git a/docs/about/we/index.html b/docs/about/we/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/categories/index.xml b/docs/categories/index.xml new file mode 100644 index 0000000..6bc1da1 --- /dev/null +++ b/docs/categories/index.xml @@ -0,0 +1,32 @@ + + + + Categories on Webpage of the Resendis Lab + https://resendislab.github.io/categories/ + Recent content in Categories on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + + + + + + Science + https://resendislab.github.io/categories/science/ + Tue, 06 Dec 2016 09:19:57 -0600 + + https://resendislab.github.io/categories/science/ + + + + + Tutorial + https://resendislab.github.io/categories/tutorial/ + Tue, 06 Dec 2016 09:19:57 -0600 + + https://resendislab.github.io/categories/tutorial/ + + + + + \ No newline at end of file diff --git a/docs/categories/science/index.html b/docs/categories/science/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/categories/science/index.xml b/docs/categories/science/index.xml new file mode 100644 index 0000000..3559ae9 --- /dev/null +++ b/docs/categories/science/index.xml @@ -0,0 +1,28 @@ + + + + Science on Webpage of the Resendis Lab + https://resendislab.github.io/categories/science/ + Recent content in Science on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Tue, 06 Dec 2016 09:19:57 -0600 + + + + + + Hola!!! + https://resendislab.github.io/posts/test/ + Tue, 06 Dec 2016 09:19:57 -0600 + + https://resendislab.github.io/posts/test/ + <h2 id="this-is-an-example-post">This is an example post</h2> + +<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> + +<p>This is my text now grrrr :)</p> + + + + \ No newline at end of file diff --git a/docs/categories/tutorial/index.html b/docs/categories/tutorial/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/categories/tutorial/index.xml b/docs/categories/tutorial/index.xml new file mode 100644 index 0000000..706ec2b --- /dev/null +++ b/docs/categories/tutorial/index.xml @@ -0,0 +1,28 @@ + + + + Tutorial on Webpage of the Resendis Lab + https://resendislab.github.io/categories/tutorial/ + Recent content in Tutorial on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Tue, 06 Dec 2016 09:19:57 -0600 + + + + + + Hola!!! + https://resendislab.github.io/posts/test/ + Tue, 06 Dec 2016 09:19:57 -0600 + + https://resendislab.github.io/posts/test/ + <h2 id="this-is-an-example-post">This is an example post</h2> + +<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> + +<p>This is my text now grrrr :)</p> + + + + \ No newline at end of file diff --git a/css/prism.css b/docs/css/prism.css similarity index 100% rename from css/prism.css rename to docs/css/prism.css diff --git a/css/styles.css b/docs/css/styles.css similarity index 100% rename from css/styles.css rename to docs/css/styles.css diff --git a/docs/events/index.html b/docs/events/index.html new file mode 100644 index 0000000..a3b115c --- /dev/null +++ b/docs/events/index.html @@ -0,0 +1,282 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+
+
+ + 2019 + + 2017 + + 2016 + + 2014 + +
+ + + +
+
+ +
+
+
+

Biological Physics Mexico City 2019

+

04-09-2019 to 06-09-1019

+
+ +

Frontier Science at the Intersection of Physics, Math and Biology

+ +

The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments.

+ +

The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians. The venue is highly convenient since there are four major Research Universities in Mexico City's metropolitan area, with extensive undergraduate and graduate programs in physics, biology, medicine, engineering and mathematics.

+ +

The program includes:

+ +
    +
  • Talks by national and international experts
    • +
  • Poster session for undergraduate/graduate students
    • +
  • Round-Table session on new trends in biological physics
    • +
  • A dedicated issue of conference proceedings.
    • +
+ +

Due to the generosity of the sponsoring institutions, no fees will be charged +to those selected to participate in this conference.

+ +
+
+map Map + link Webpage +
+
+
+ + +
+ +
+
+
+

3rd International Summer Symposium on Systems Biology

+

05-08-2019 to 06-08-1019

+
+ +

3rd International Summer Symposium on Systems Biology

+ +

Those are the proceedings for the 3rd edition of the “International Summer Symposium on Systems +Biology”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields.

+ +

+This effort was supported by the Laboratory of Human Systems Biology-INMEGEN to create a scientific ambiance for discussing some methods and strategies to develop the +bases of a systemic and personalized medicine in a national and international perspective.

+ + +

The purposes of the meeting are:

+ +
    +
  • Discuss some of the frontier research in Systems Biology and its applications for understanding +human diseases.
    • +
  • Create an ambiance to establish collaborations among groups that will promote different +computational frameworks for modeling human diseases.
    • +
  • Design strategies to encourage growth in this area in biomedical, medicine and genomic sciences at +the undergraduate and graduate levels since these are areas with potential for dealing with health +problems in Mexico.
    • +
+ +

To reach these goals, the meeting brought together some national and international qualified experts +from different research groups, providing an excellent occasion for academic exchange between local +and foreign colleagues in a pleasant and collaborative environment. The program included plenary +lectures, poster sessions, a discussion panel and a series of short presentations geared towards +postdoctoral researchers and advanced graduate students.

+ +
+
+map Map + link Webpage +
+
+
+ +
+ + + + + +
+
+ +
+
+
+

Biological Physics Mexico City 2017

+

17-05-2017 to 19-05-2017

+
+ +

Frontiers at the interface of Physics, Math and Biology.

+ +

This conference (the second in a series) is intended as an international, +multidisciplinary scientific forum to discuss the latest developments in +biological physics (including proteins, peptides and enzymes, among many other +topics).

+ +

The conference is expected to boost a new paradigm of interdisciplinary +approaches converging into specific problems in biological physics. Hence, the +conference audience is broad: We aim to attract the attention of biologists as +well as biochemists, organic chemists, engineers, computational scientists, +physicists, and mathematicians. The venue is highly convenient since there are +four major Research Universities in Mexico City’s metropolitan area, with +extensive undergraduate and graduate programs in physics, biology, medicine, +engineering and mathematics.

+ +

The program includes:

+ +
    +
  • Talks by national and international experts
    • +
  • Poster session for undergraduate/graduate students
    • +
  • Round-Table session on new trends in biological physics
    • +
  • A dedicated issue of conference proceedings.
    • +
+ + +
+
+map Map + link Webpage +
+
+
+ +
+ +
+
+ +
+
+
+

2nd International Summer Symposium on Systems Biology

+

02-08-2016 to 04-08-2016

+

With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM & INMEGEN.

+ +

The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine. The aims of the meeting are:

+ +
    +
  1. Discuss current research in Systems Biology and its applications for understanding human diseases
    2. +
  2. Create an ambiance that enables scientific collaborations among experimental and theoretical groups working on human diseases.
    3. +
+ +

To this extent we invite national and international researchers and students working in the aforementioned fields during all stages of their academic career with the possibility to present their work as a poster or short talk to a highly qualified research community.

+ +

The registration deadline has been extended until June 15th, 2016!

+ +
+
+map Map + link Webpage +
+
+
+ +
+ +
+
+ +
+
+
+

1st International Summer Symposium on Systems Biology

+

04-08-2014 to 06-08-2014

+

Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems +Biology: From networks to phenotypes in human diseases”. The meeting took place at the National +Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the +first of a series of meetings to encourage the development of the systems biology in Mexico and the +development of this area to tackle basic and applied research in medical and biomedical fields. +This effort was supported by the Laboratory of Human Systems Biology-INMEGEN and Fundación +Televisa to create a scientific ambiance for discussing some methods and strategies to develop the +bases of a systemic and personalized medicine in a national and international perspective.

+ +

The purposes of the meeting were:

+ +
    +

  • Discuss some of the frontier research in Systems Biology and its applications for understanding +human diseases.

    • + +

  • Create an ambiance to establish collaborations among groups that will promote different +computational frameworks for modeling human diseases.

    • + +

  • Design strategies to encourage growth in this area in biomedical, medicine and genomic sciences at +the undergraduate and graduate levels since these are areas with potential for dealing with health +problems in Mexico.

    • +
+ +

To reach these goals, the meeting brought together some national and international qualified experts +from different research groups, providing an excellent occasion for academic exchange between local +and foreign colleagues in a pleasant and collaborative environment. The program included plenary +lectures, poster sessions, a discussion panel and a series of short presentations geared towards +postdoctoral researchers and advanced graduate students.

+ +
+
+map Map + link Webpage +
+
+
+ +
+ +
+
+
+ + + + + + diff --git a/docs/events/index.xml b/docs/events/index.xml new file mode 100644 index 0000000..e52a3e7 --- /dev/null +++ b/docs/events/index.xml @@ -0,0 +1,44 @@ + + + + Events on Webpage of the Resendis Lab + https://resendislab.github.io/events/ + Recent content in Events on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Wed, 17 May 2017 00:00:00 +0000 + + + + + + Biological Physics Mexico City 2017 + https://resendislab.github.io/events/biological_physics/ + Wed, 17 May 2017 00:00:00 +0000 + + https://resendislab.github.io/events/biological_physics/ + Frontiers at the interface of Physics, Math and Biology. This conference (the second in a series) is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics (including proteins, peptides and enzymes, among many other topics). +The conference is expected to boost a new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians. + + + + 2nd International Summer Symposium on Systems Biology + https://resendislab.github.io/events/is3b/ + Tue, 02 Aug 2016 00:00:00 +0000 + + https://resendislab.github.io/events/is3b/ + With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM &amp; INMEGEN. +The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine. + + + + 1st International Summer Symposium on Systems Biology + https://resendislab.github.io/events/is3b_2014/ + Mon, 04 Aug 2014 00:00:00 +0000 + + https://resendislab.github.io/events/is3b_2014/ + Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems Biology: From networks to phenotypes in human diseases”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields. + + + + \ No newline at end of file diff --git a/docs/events/is3b/index.html b/docs/events/is3b/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..670f312 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,99 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+

RESENDIS ANTONIO LAB

+

Blending Biology and Computation to understand human diseases.

+
+

+ +
+
+
+

Who we are

+

Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. +Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales.

+
+
+ Read more... +
+
+ + + +
+
+
+

Latest News

+
+
+

Biological Physics Mexico City 2017

+

from 17-05-2017 to 19-05-2017


+

Frontiers at the interface of Physics, Math and Biology. This conference (the second in a series) is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics (including proteins, peptides and enzymes, among many other topics). +The conference is expected to boost a new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians.

+
+
+ Read more... +
+
+ + +
+
+
+

Latest Publication

+
+
+

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.

+

Frontiers In Physiology 2017

+

Nora A Gutierrez Najera, Osbaldo Resendis-Antonio and Humberto Nicolini

+

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part.

+
+
+ See all... +
+
+ +
+ + +
+
+ + + + + + diff --git a/docs/index.xml b/docs/index.xml new file mode 100644 index 0000000..72893c0 --- /dev/null +++ b/docs/index.xml @@ -0,0 +1,327 @@ + + + + Webpage of the Resendis Lab + https://resendislab.github.io/ + Recent content on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Thu, 29 Jun 2017 12:53:05 -0500 + + + + + + Graduate student positions + https://resendislab.github.io/positions/students/ + Thu, 29 Jun 2017 12:53:05 -0500 + + https://resendislab.github.io/positions/students/ + We extend an invitation to undergrads and grad students with interest to continue his/her academic education through a Master&rsquo;s or Doctoral degree in one of these academic programs: biological (http://pcbiol.posgrado.unam.mx), biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical (http://www.pdcb.unam.mx/)) Sciences at UNAM. We encourage candidates with an academic background in biology, biology physics, biophysics, genome sciences, applied mathematics and computational sciences. The students incorporated to one of these programs will be guided to develop a systems biology description in one of these areas: + + + + Postdoctoral position + https://resendislab.github.io/positions/postdocs/ + Thu, 29 Jun 2017 12:52:56 -0500 + + https://resendislab.github.io/positions/postdocs/ + We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to oresendis [at] inmegen. + + + + "Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases. + https://resendislab.github.io/pubs/pm28536537/ + Fri, 26 May 2017 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28536537/ + The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the &ldquo;psychiatric phenotype&rdquo; is to provide an improved vision of the shape of the phenotype as it is visualized by &ldquo;Gestalt&rdquo; psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. + + + + Biological Physics Mexico City 2017 + https://resendislab.github.io/events/biological_physics/ + Wed, 17 May 2017 00:00:00 +0000 + + https://resendislab.github.io/events/biological_physics/ + Frontiers at the interface of Physics, Math and Biology. This conference (the second in a series) is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics (including proteins, peptides and enzymes, among many other topics). +The conference is expected to boost a new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians. + + + + Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora. + https://resendislab.github.io/pubs/pm28410570/ + Sun, 16 Apr 2017 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28410570/ + Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. + + + + Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies. + https://resendislab.github.io/pubs/pm28082911/ + Sat, 14 Jan 2017 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28082911/ + Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. + + + + Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine. + https://resendislab.github.io/pubs/pm28018236/ + Tue, 27 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28018236/ + It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. + + + + Hola!!! + https://resendislab.github.io/posts/test/ + Tue, 06 Dec 2016 09:19:57 -0600 + + https://resendislab.github.io/posts/test/ + <h2 id="this-is-an-example-post">This is an example post</h2> + +<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> + +<p>This is my text now grrrr :)</p> + + + + In silico study of metabolic reprogramming during epithelial-mesenchymal transition + https://resendislab.github.io/projects/emt/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/emt/ + An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes. + + + + Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model + https://resendislab.github.io/projects/hepato/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/hepato/ + Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions. + + + + Metabolic heterogeneity in cancer and its applications in Personalized Medicine + https://resendislab.github.io/projects/prolif/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/prolif/ + Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation. +In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism. + + + + The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development + https://resendislab.github.io/projects/spheroids/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/spheroids/ + Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. + + + + Who we are + https://resendislab.github.io/about/we/ + Mon, 05 Dec 2016 14:48:16 -0600 + + https://resendislab.github.io/about/we/ + Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. +Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales. + + + + Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks. + https://resendislab.github.io/pubs/pm27616995/ + Tue, 13 Sep 2016 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm27616995/ + + + + + 2nd International Summer Symposium on Systems Biology + https://resendislab.github.io/events/is3b/ + Tue, 02 Aug 2016 00:00:00 +0000 + + https://resendislab.github.io/events/is3b/ + With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM &amp; INMEGEN. +The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine. + + + + The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome. + https://resendislab.github.io/pubs/pm27335086/ + Fri, 24 Jun 2016 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm27335086/ + During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. + + + + 1st International Summer Symposium on Systems Biology + https://resendislab.github.io/events/is3b_2014/ + Mon, 04 Aug 2014 00:00:00 +0000 + + https://resendislab.github.io/events/is3b_2014/ + Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems Biology: From networks to phenotypes in human diseases”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields. + + + + Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer. + https://resendislab.github.io/pubs/pm24747697/ + Tue, 22 Apr 2014 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm24747697/ + Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. + + + + Encyclopedia of Systems Biology + https://resendislab.github.io/pubs/encyclopedia/ + Sat, 01 Jun 2013 00:00:00 +0000 + + https://resendislab.github.io/pubs/encyclopedia/ + The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. + + + + Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells. + https://resendislab.github.io/pubs/pm23316163/ + Tue, 15 Jan 2013 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm23316163/ + One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. + + + + Symbiotic Endophytes + https://resendislab.github.io/pubs/symbiotic_endophytes/ + Tue, 01 Jan 2013 00:00:00 +0000 + + https://resendislab.github.io/pubs/symbiotic_endophytes/ + This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields. + + + + Functional modules, structural topology, and optimal activity in metabolic networks. + https://resendislab.github.io/pubs/pm23071431/ + Wed, 17 Oct 2012 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm23071431/ + Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. + + + + Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling. + https://resendislab.github.io/pubs/pm21801415/ + Tue, 02 Aug 2011 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm21801415/ + Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. + + + + Proteomic patterns of cervical cancer cell lines, a network perspective. + https://resendislab.github.io/pubs/pm21696634/ + Fri, 24 Jun 2011 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm21696634/ + Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis. + + + + Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. + https://resendislab.github.io/pubs/pm20811631/ + Fri, 03 Sep 2010 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm20811631/ + Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. + + + + Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking. + https://resendislab.github.io/pubs/pm19305506/ + Tue, 24 Mar 2009 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm19305506/ + Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network. + + + + Regulation by transcription factors in bacteria: beyond description. + https://resendislab.github.io/pubs/pm19076632/ + Wed, 17 Dec 2008 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm19076632/ + Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. + + + + Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli. + https://resendislab.github.io/pubs/pm17922569/ + Wed, 10 Oct 2007 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm17922569/ + Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. + + + + Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli. + https://resendislab.github.io/pubs/pm17559662/ + Fri, 15 Jun 2007 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm17559662/ + Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses. + + + + Robustness and evolvability in genetic regulatory networks. + https://resendislab.github.io/pubs/pm17188715/ + Tue, 26 Dec 2006 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm17188715/ + Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. + + + + Modular analysis of the transcriptional regulatory network of E. coli. + https://resendislab.github.io/pubs/pm15680508/ + Tue, 01 Feb 2005 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm15680508/ + The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. + + + + Contact + https://resendislab.github.io/about/contact/ + Mon, 01 Jan 1900 00:00:00 +0000 + + https://resendislab.github.io/about/contact/ + Directions +Osbaldo Resendis-Antonio, PhD +Laboratory in Systems Biology and Human Diseases +Associated Professor +Instituto Nacional de Medicina Genomica – INMEGEN +Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX +Phone: +52 55 5350 1900 - Ext.1198 + + + + + + https://resendislab.github.io/members/dummy/ + Mon, 01 Jan 0001 00:00:00 +0000 + + https://resendislab.github.io/members/dummy/ + This is a dummy page. It&rsquo;s content will not be rendered. + + + + \ No newline at end of file diff --git a/js/TweenMax.min.js b/docs/js/TweenMax.min.js similarity index 100% rename from js/TweenMax.min.js rename to docs/js/TweenMax.min.js diff --git a/js/neuro.js b/docs/js/neuro.js similarity index 100% rename from js/neuro.js rename to docs/js/neuro.js diff --git a/js/prism.js b/docs/js/prism.js similarity index 100% rename from js/prism.js rename to docs/js/prism.js diff --git a/media/background.png b/docs/media/background.png similarity index 100% rename from media/background.png rename to docs/media/background.png diff --git a/media/background2.png b/docs/media/background2.png similarity index 100% rename from media/background2.png rename to docs/media/background2.png diff --git a/media/benjamin.jpg b/docs/media/benjamin.jpg similarity index 100% rename from media/benjamin.jpg rename to docs/media/benjamin.jpg diff --git a/media/charly.jpg b/docs/media/charly.jpg similarity index 100% rename from media/charly.jpg rename to docs/media/charly.jpg diff --git a/media/chris.jpg b/docs/media/chris.jpg similarity index 100% rename from media/chris.jpg rename to docs/media/chris.jpg diff --git a/media/cris.jpg b/docs/media/cris.jpg similarity index 100% rename from media/cris.jpg rename to docs/media/cris.jpg diff --git a/media/encyclopedia.jpg b/docs/media/encyclopedia.jpg similarity index 100% rename from media/encyclopedia.jpg rename to docs/media/encyclopedia.jpg diff --git a/media/endophytes.jpg b/docs/media/endophytes.jpg similarity index 100% rename from media/endophytes.jpg rename to docs/media/endophytes.jpg diff --git a/media/erick.jpg b/docs/media/erick.jpg similarity index 100% rename from media/erick.jpg rename to docs/media/erick.jpg diff --git a/media/erika.jpg b/docs/media/erika.jpg similarity index 100% rename from media/erika.jpg rename to docs/media/erika.jpg diff --git a/media/favicon.png b/docs/media/favicon.png similarity index 100% rename from media/favicon.png rename to docs/media/favicon.png diff --git a/media/felipe.jpg b/docs/media/felipe.jpg similarity index 100% rename from media/felipe.jpg rename to docs/media/felipe.jpg diff --git a/media/group_2014.jpg b/docs/media/group.jpg similarity index 100% rename from media/group_2014.jpg rename to docs/media/group.jpg diff --git a/media/group.png b/docs/media/group.png similarity index 100% rename from media/group.png rename to docs/media/group.png diff --git a/media/logo.png b/docs/media/logo.png similarity index 100% rename from media/logo.png rename to docs/media/logo.png diff --git a/media/lucy.jpg b/docs/media/lucy.jpg similarity index 100% rename from media/lucy.jpg rename to docs/media/lucy.jpg diff --git a/media/meztli.jpg b/docs/media/meztli.jpg similarity index 100% rename from media/meztli.jpg rename to docs/media/meztli.jpg diff --git a/media/neuro.png b/docs/media/neuro.png similarity index 100% rename from media/neuro.png rename to docs/media/neuro.png diff --git a/media/osbaldo.jpg b/docs/media/osbaldo.jpg similarity index 100% rename from media/osbaldo.jpg rename to docs/media/osbaldo.jpg diff --git a/media/paola.jpg b/docs/media/paola.jpg similarity index 100% rename from media/paola.jpg rename to docs/media/paola.jpg diff --git a/media/placeholder.svg b/docs/media/placeholder.svg similarity index 100% rename from media/placeholder.svg rename to docs/media/placeholder.svg diff --git a/media/twitter.svg b/docs/media/twitter.svg similarity index 100% rename from media/twitter.svg rename to docs/media/twitter.svg diff --git a/docs/media/ugo.jpg b/docs/media/ugo.jpg new file mode 100644 index 0000000..1fab017 Binary files /dev/null and b/docs/media/ugo.jpg differ diff --git a/docs/members/dummy/index.html b/docs/members/dummy/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/members/index.html b/docs/members/index.html new file mode 100644 index 0000000..b1834ed --- /dev/null +++ b/docs/members/index.html @@ -0,0 +1,288 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+
+
+ Current + Former +
+
+
+ +
+
+
+
+ +

Osbaldo Resendis Antonio

+

Principal Investigator

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Erika Hernandez Patiño

+

M.Sc. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Felipe Muñoz Gonzalez

+

Ph.D. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Paola Hernandez

+

Ph.D. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Meztli Matadamas

+

Ph.D. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Christian Diener

+

PostDoc

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Erick Muciño

+

Ph.D. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Lucía Lopez

+

M.Sc. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+
+ +

Ugo Avila

+

M.Sc. student

+
+
+
+mail outline +person pin + + +
+
+
+ +
+
+
+ +
+
+
+
+ +

Carlos Salgado Muñoz

+

Ph.D. student

+
+
+
+mail outline +person pin + +
+
+
+ +
+
+
+
+ +

Benjamin Cocom Chan

+

M.Sc. student

+
+
+
+mail outline +person pin + +
+
+
+ +
+
+
+
+ +

Ricardo Ramirez

+

Ph.D. student

+
+
+
+mail outline +person pin + +
+
+
+ +
+
+
+
+ +

Cristhian Avila

+

PreDoc

+
+
+
+mail outline +person pin + +
+
+
+ +
+
+
+
+ + + + + + diff --git a/docs/members/index.xml b/docs/members/index.xml new file mode 100644 index 0000000..22b9dac --- /dev/null +++ b/docs/members/index.xml @@ -0,0 +1,23 @@ + + + + Members on Webpage of the Resendis Lab + https://resendislab.github.io/members/ + Recent content in Members on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + + + + + + + https://resendislab.github.io/members/dummy/ + Mon, 01 Jan 0001 00:00:00 +0000 + + https://resendislab.github.io/members/dummy/ + This is a dummy page. It&rsquo;s content will not be rendered. + + + + \ No newline at end of file diff --git a/docs/positions/index.html b/docs/positions/index.html new file mode 100644 index 0000000..583ec3d --- /dev/null +++ b/docs/positions/index.html @@ -0,0 +1,101 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+
+
+
+

Work with us

+
+
+
+ +
+
+
+

Graduate student positions

+

We extend an invitation to undergrads and grad students with interest to +continue his/her academic education through a Master’s or Doctoral degree in one +of these academic programs: biological (http://pcbiol.posgrado.unam.mx), +biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical +(http://www.pdcb.unam.mx/)) Sciences at UNAM. We encourage candidates with an +academic background in biology, biology physics, biophysics, genome sciences, +applied mathematics and computational sciences. The students incorporated to one +of these programs will be guided to develop a systems biology description in one +of these areas:

+ +
    +
  1. Identification of metabolic alterations in cancer cell lines +or animal tissues
    2. +
  2. Tissue-specific genome-scale metabolic reconstruction in +human system
    3. +
  3. computational modeling of metabolism in microbiota and its +association with diabetes
    4. +
  4. Development of computational methods to integrate and interpret high-throughput technologies such as RNAseq, proteome, and metabolome
    5. +
  5. development of systems Biology schemes for the Study of Neuropsychiatric Diseases
    6. +
  6. Development of computational analysis in Personalized (precision) medicine. +
    7. +
+ +

All projects are immersed on an interdisciplinary ambiance and promote an +integrative description of the statistical analysis of high-throughput data in +genomic sciences, development of computational/mathematical modeling of +biological networks; and the physiological knowledge, these points required +for understanding the principles that support the metabolic phenotype in human +diseases. For any further questions do not hesitate to contact us via E-mail.

+ +
+
+
+ +
+
+
+

Postdoctoral position

+

We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to oresendis [at] inmegen.gob.mx.

+ +
+
+
+ +
+
+
+ + + + + + diff --git a/docs/positions/index.xml b/docs/positions/index.xml new file mode 100644 index 0000000..861afa2 --- /dev/null +++ b/docs/positions/index.xml @@ -0,0 +1,33 @@ + + + + Positions on Webpage of the Resendis Lab + https://resendislab.github.io/positions/ + Recent content in Positions on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Thu, 29 Jun 2017 12:53:05 -0500 + + + + + + Graduate student positions + https://resendislab.github.io/positions/students/ + Thu, 29 Jun 2017 12:53:05 -0500 + + https://resendislab.github.io/positions/students/ + We extend an invitation to undergrads and grad students with interest to continue his/her academic education through a Master&rsquo;s or Doctoral degree in one of these academic programs: biological (http://pcbiol.posgrado.unam.mx), biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical (http://www.pdcb.unam.mx/)) Sciences at UNAM. We encourage candidates with an academic background in biology, biology physics, biophysics, genome sciences, applied mathematics and computational sciences. The students incorporated to one of these programs will be guided to develop a systems biology description in one of these areas: + + + + Postdoctoral position + https://resendislab.github.io/positions/postdocs/ + Thu, 29 Jun 2017 12:52:56 -0500 + + https://resendislab.github.io/positions/postdocs/ + We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to oresendis [at] inmegen. + + + + \ No newline at end of file diff --git a/docs/posts/index.html b/docs/posts/index.html new file mode 100644 index 0000000..c492318 --- /dev/null +++ b/docs/posts/index.html @@ -0,0 +1,74 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+
+
+
+

Blog

+
+
+
+ +
+
+
+

Hola!!!

+
+
+

+ 06-12-2016 + 1m read

+

by Christian

+

This is an example post

+ +

Please substitute all text below “+++” with your own!

+ +

This is my text now grrrr :)

+
+
+ Read more... +
+ +
+
+
+ + + + + + diff --git a/docs/posts/index.xml b/docs/posts/index.xml new file mode 100644 index 0000000..1972544 --- /dev/null +++ b/docs/posts/index.xml @@ -0,0 +1,28 @@ + + + + Posts on Webpage of the Resendis Lab + https://resendislab.github.io/posts/ + Recent content in Posts on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Tue, 06 Dec 2016 09:19:57 -0600 + + + + + + Hola!!! + https://resendislab.github.io/posts/test/ + Tue, 06 Dec 2016 09:19:57 -0600 + + https://resendislab.github.io/posts/test/ + <h2 id="this-is-an-example-post">This is an example post</h2> + +<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> + +<p>This is my text now grrrr :)</p> + + + + \ No newline at end of file diff --git a/docs/posts/test/index.html b/docs/posts/test/index.html new file mode 100644 index 0000000..2ef6155 --- /dev/null +++ b/docs/posts/test/index.html @@ -0,0 +1,96 @@ + + + + + + + + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + + + +
+
+
+
+ + +
+
+ + +
+ +arrow_back +Return to overview... +
+
+
+

Hola!!!

+
+
+

by Christian


+

This is an example post

+ +

Please substitute all text below “+++” with your own!

+ +

This is my text now grrrr :)

+ +

+$$ +\int_a^b e^{2\pi\cdot x} dx +$$

+ +
library(data.table)
+df <- data.table(x=1:10)
+
+
+
+ +
+
+ + + + + + + + + diff --git a/docs/projects/emt/index.html b/docs/projects/emt/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/projects/hepato/index.html b/docs/projects/hepato/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/projects/index.html b/docs/projects/index.html new file mode 100644 index 0000000..99c0b02 --- /dev/null +++ b/docs/projects/index.html @@ -0,0 +1,107 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+
+
+
+

Projects

+
+
+
+ +
+
+
+

In silico study of metabolic reprogramming during epithelial-mesenchymal transition

+

Meztli Matadamas

+

An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes.

+ +

The project combine high-throughput data to understand metabolic changes before and after EMT in lung cancer cell lines. In particular to find main fluxes used during EMT. To that extent we employ methods from bioinformatics and Systems Biology. Our goal is to found specific targets which could stop or reverse EMT in cancer cells.

+ +
+
+
+ +
+
+
+

Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model

+

Erika Hernandez

+

Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. +Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions. +This data would not only helped us identify reactions important to maintain the cancerous state but also help us survey the regulatory mechanism this is achieved.

+ +
+
+
+ +
+
+
+

Metabolic heterogeneity in cancer and its applications in Personalized Medicine

+

Christian Diener

+

Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation.

+ +

In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism. In particular we are asking the question whether distinct genomic aberrations such as mutations or changes in transcription can be related to respective changes in cellular metabolism. To that extent we employ methods from Systems Biology as well as from Data Science and Machine Learning in order to connect genetic information to specific metabolic phenotypes.

+ +

Our aim is to use the knowledge we gain in the context of personalized medicine, particularly the use of genotyping for the prediction of the best course of treatment for a specific patient.

+ +
+
+
+ +
+
+
+

The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development

+

Erick Muciño

+

Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. Hence, generate models that allow the integration of miRNAs regulation in cancer metabolism will allow us to analyze in a systematically and systemic manner the relations and potential mechanism underlying between miRNAs and central pathways of metabolism.

+ +

It is imperative to know the mechanism governing the pathogenesis and progression of cancer to design therapies with greater impact on diagnosis and disease progression. So, this project aims to suggest mechanism that trigger the metabolic change in a breast cancer cell line (MCF-7), integrating miRNAs network. To this end, we propose to develop a scheme of systems biology, which allow us to make an integrative analysis of regulatory networks of miRNAs and metabolism in MCF-7. This approach will allow us to develop models capable of identifying potential therapeutic targets with greater impact, biomarkers that allow early detection of cancer and penetrate in global mechanism in clinical cases.

+ +
+
+
+ +
+
+
+ + + + + + diff --git a/docs/projects/index.xml b/docs/projects/index.xml new file mode 100644 index 0000000..da706d9 --- /dev/null +++ b/docs/projects/index.xml @@ -0,0 +1,52 @@ + + + + Projects on Webpage of the Resendis Lab + https://resendislab.github.io/projects/ + Recent content in Projects on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Tue, 06 Dec 2016 00:00:00 +0000 + + + + + + In silico study of metabolic reprogramming during epithelial-mesenchymal transition + https://resendislab.github.io/projects/emt/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/emt/ + An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes. + + + + Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model + https://resendislab.github.io/projects/hepato/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/hepato/ + Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions. + + + + Metabolic heterogeneity in cancer and its applications in Personalized Medicine + https://resendislab.github.io/projects/prolif/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/prolif/ + Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation. +In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism. + + + + The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development + https://resendislab.github.io/projects/spheroids/ + Tue, 06 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/projects/spheroids/ + Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. + + + + \ No newline at end of file diff --git a/docs/projects/prolif/index.html b/docs/projects/prolif/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/projects/spheroids/index.html b/docs/projects/spheroids/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM15680508/index.html b/docs/pubs/PM15680508/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM17922569/index.html b/docs/pubs/PM17922569/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM19076632/index.html b/docs/pubs/PM19076632/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM19305506/index.html b/docs/pubs/PM19305506/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM20811631/index.html b/docs/pubs/PM20811631/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM21696634/index.html b/docs/pubs/PM21696634/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM21801415/index.html b/docs/pubs/PM21801415/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM23071431/index.html b/docs/pubs/PM23071431/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM23316163/index.html b/docs/pubs/PM23316163/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM24747697/index.html b/docs/pubs/PM24747697/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM27335086/index.html b/docs/pubs/PM27335086/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/PM27616995/index.html b/docs/pubs/PM27616995/index.html new file mode 100644 index 0000000..e69de29 diff --git a/docs/pubs/index.html b/docs/pubs/index.html new file mode 100644 index 0000000..07255d2 --- /dev/null +++ b/docs/pubs/index.html @@ -0,0 +1,394 @@ + + + + + + + + + Webpage of the Resendis Lab + + + + + + + + +
+
+
+
+ + +
+
+ +
+
+
+ Articles + Books +
+
+
+ +
+
+
+

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.

+

Frontiers In Physiology 2017

+

Nora A Gutierrez Najera, Osbaldo Resendis-Antonio and Humberto Nicolini

+

Keywords: diagnosis, lung cancer, omics, psychiatry, systems biology

+

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.

+

BMC Evolutionary Biology 2017

+

Cecilio Valadez-Cano, Roberto Olivares-Hernández, Osbaldo Resendis-Antonio, Alexander DeLuna and Luis Delaye

+

Keywords: Adaptation, Endosymbiont, Metabolic evolution, Metabolic integration

+

Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

+

Frontiers In Physiology 2017

+

Christian Diener and Osbaldo Resendis-Antonio

+

Keywords: NCI60, TCGA, flux balance analysis, personalized medicine, proliferation, systems biology

+

Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine.

+

Frontiers In Physiology 2016

+

Alejandra V Contreras, Benjamin Cocom-Chan, Georgina Hernandez-Montes, Tobias Portillo-Bobadilla and Osbaldo Resendis-Antonio

+

Keywords: cancer metabolism, metabolome, microbiome, next generation sequencing (NGS), precision medicine, systems integration

+

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.

+

Frontiers In Physiology 2016

+

Mahdi Jalili, Ali Salehzadeh-Yazdi, Shailendra Gupta, Olaf Wolkenhauer, Marjan Yaghmaie, Osbaldo Resendis-Antonio and Kamran Alimoghaddam

+

Keywords: biological centrality, biological network, centrality, essentiality, topological network analysis

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome.

+

Scientific Reports 2016

+

Christian Diener, Felipe Muñoz-Gonzalez, Sergio Encarnación and Osbaldo Resendis-Antonio

+

+

During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer.

+

Seminars In Cancer Biology 2014

+

Osbaldo Resendis-Antonio, Carolina González-Torres, Gustavo Jaime-Muñoz, Claudia Erika Hernandez-Patiño and Carlos Felipe Salgado-Muñoz

+

Keywords: Cancer metabolism, Mathematical models, P4 medicine, Systems biology: Constraint-based modeling

+

Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

+

Frontiers In Physiology 2013

+

Claudia E Hernández Patiño, Gustavo Jaime-Muñoz and Osbaldo Resendis-Antonio

+

Keywords: cancer metabolic phenotype, computational modeling of metabolism, constraint-based modeling, genome scale metabolic reconstruction, high throughput biology

+

One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Functional modules, structural topology, and optimal activity in metabolic networks.

+

PLoS Computational Biology 2012

+

Osbaldo Resendis-Antonio, Magdalena Hernández, Yolanda Mora and Sergio Encarnación

+

+

Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.

+

BMC Systems Biology 2011

+

Osbaldo Resendis-Antonio, Magdalena Hernández, Emmanuel Salazar, Sandra Contreras, Gabriel Martínez Batallar, Yolanda Mora and Sergio Encarnación

+

+

Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Proteomic patterns of cervical cancer cell lines, a network perspective.

+

BMC Systems Biology 2011

+

Juan Carlos Higareda-Almaraz, María del Rocío Enríquez-Gasca, Magdalena Hernández-Ortiz, Osbaldo Resendis-Antonio and Sergio Encarnación-Guevara

+

+

Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect.

+

PloS One 2010

+

Osbaldo Resendis-Antonio, Alberto Checa and Sergio Encarnación

+

+

Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.

+

PloS One 2009

+

Osbaldo Resendis-Antonio

+

+

Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Regulation by transcription factors in bacteria: beyond description.

+

FEMS Microbiology Reviews 2008

+

Enrique Balleza, Lucia N López-Bojorquez, Agustino Martínez-Antonio, Osbaldo Resendis-Antonio, Irma Lozada-Chávez, Yalbi I Balderas-Martínez, Sergio Encarnación and Julio Collado-Vides

+

+

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli.

+

PLoS Computational Biology 2007

+

Osbaldo Resendis-Antonio, Jennifer L Reed, Sergio Encarnación, Julio Collado-Vides and Bernhard Ø Palsson

+

+

Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.

+

BMC Microbiology 2007

+

Rosa María Gutierrez-Ríos, Julio A Freyre-Gonzalez, Osbaldo Resendis, Julio Collado-Vides, Milton Saier and Guillermo Gosset

+

+

Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Robustness and evolvability in genetic regulatory networks.

+

Journal Of Theoretical Biology 2006

+

Maximino Aldana, Enrique Balleza, Stuart Kauffman and Osbaldo Resendiz

+

+

Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. Here we address this problem by studying the robustness and evolvability of the attractor landscape of genetic regulatory network models under the process of gene duplication followed by divergence. We show that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear. The above is true in a variety of network topologies and even for the case of extreme divergence in which the duplicate gene bears almost no relation with its parent. Our results indicate that networks operating close to the so-called “critical regime” exhibit the maximum robustness and evolvability simultaneously.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+

Modular analysis of the transcriptional regulatory network of E. coli.

+

Trends In Genetics : TIG 2005

+

Osbaldo Resendis-Antonio, Julio A Freyre-González, Ricardo Menchaca-Méndez, Rosa M Gutiérrez-Ríos, Agustino Martínez-Antonio, Cristhian Avila-Sánchez and Julio Collado-Vides

+

+

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.

+ +
+
+ Full Article + PubMed +
+
+
+ +
+
+
+ +
+
+
+

Encyclopedia of Systems Biology

+

June 2013

+

Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho and Hiroki Yokota

+

+

+ +

The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology.

+ +
+
+
+ +
+
+
+

Symbiotic Endophytes

+

January 2013

+

Ricardo Aroca

+

+

+ +

This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. +Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields.

+ +
+
+
+ +
+
+
+
+ + + + + + diff --git a/docs/pubs/index.xml b/docs/pubs/index.xml new file mode 100644 index 0000000..10b98b4 --- /dev/null +++ b/docs/pubs/index.xml @@ -0,0 +1,195 @@ + + + + Pubs on Webpage of the Resendis Lab + https://resendislab.github.io/pubs/ + Recent content in Pubs on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + Fri, 26 May 2017 00:00:00 +0000 + + + + + + "Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases. + https://resendislab.github.io/pubs/pm28536537/ + Fri, 26 May 2017 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28536537/ + The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the &ldquo;psychiatric phenotype&rdquo; is to provide an improved vision of the shape of the phenotype as it is visualized by &ldquo;Gestalt&rdquo; psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. + + + + Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora. + https://resendislab.github.io/pubs/pm28410570/ + Sun, 16 Apr 2017 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28410570/ + Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. + + + + Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies. + https://resendislab.github.io/pubs/pm28082911/ + Sat, 14 Jan 2017 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28082911/ + Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. + + + + Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine. + https://resendislab.github.io/pubs/pm28018236/ + Tue, 27 Dec 2016 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm28018236/ + It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. + + + + Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks. + https://resendislab.github.io/pubs/pm27616995/ + Tue, 13 Sep 2016 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm27616995/ + + + + + The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome. + https://resendislab.github.io/pubs/pm27335086/ + Fri, 24 Jun 2016 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm27335086/ + During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. + + + + Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer. + https://resendislab.github.io/pubs/pm24747697/ + Tue, 22 Apr 2014 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm24747697/ + Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. + + + + Encyclopedia of Systems Biology + https://resendislab.github.io/pubs/encyclopedia/ + Sat, 01 Jun 2013 00:00:00 +0000 + + https://resendislab.github.io/pubs/encyclopedia/ + The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. + + + + Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells. + https://resendislab.github.io/pubs/pm23316163/ + Tue, 15 Jan 2013 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm23316163/ + One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. + + + + Symbiotic Endophytes + https://resendislab.github.io/pubs/symbiotic_endophytes/ + Tue, 01 Jan 2013 00:00:00 +0000 + + https://resendislab.github.io/pubs/symbiotic_endophytes/ + This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields. + + + + Functional modules, structural topology, and optimal activity in metabolic networks. + https://resendislab.github.io/pubs/pm23071431/ + Wed, 17 Oct 2012 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm23071431/ + Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. + + + + Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling. + https://resendislab.github.io/pubs/pm21801415/ + Tue, 02 Aug 2011 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm21801415/ + Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. + + + + Proteomic patterns of cervical cancer cell lines, a network perspective. + https://resendislab.github.io/pubs/pm21696634/ + Fri, 24 Jun 2011 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm21696634/ + Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis. + + + + Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. + https://resendislab.github.io/pubs/pm20811631/ + Fri, 03 Sep 2010 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm20811631/ + Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. + + + + Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking. + https://resendislab.github.io/pubs/pm19305506/ + Tue, 24 Mar 2009 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm19305506/ + Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network. + + + + Regulation by transcription factors in bacteria: beyond description. + https://resendislab.github.io/pubs/pm19076632/ + Wed, 17 Dec 2008 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm19076632/ + Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. + + + + Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli. + https://resendislab.github.io/pubs/pm17922569/ + Wed, 10 Oct 2007 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm17922569/ + Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. + + + + Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli. + https://resendislab.github.io/pubs/pm17559662/ + Fri, 15 Jun 2007 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm17559662/ + Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses. + + + + Robustness and evolvability in genetic regulatory networks. + https://resendislab.github.io/pubs/pm17188715/ + Tue, 26 Dec 2006 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm17188715/ + Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. + + + + Modular analysis of the transcriptional regulatory network of E. coli. + https://resendislab.github.io/pubs/pm15680508/ + Tue, 01 Feb 2005 00:00:00 +0000 + + https://resendislab.github.io/pubs/pm15680508/ + The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. + + + + \ No newline at end of file diff --git a/docs/sitemap.xml b/docs/sitemap.xml new file mode 100644 index 0000000..bd758a7 --- /dev/null +++ b/docs/sitemap.xml @@ -0,0 +1,238 @@ + + + + + https://resendislab.github.io/positions/students/ + 2017-06-29T12:53:05-05:00 + + + + https://resendislab.github.io/positions/postdocs/ + 2017-06-29T12:52:56-05:00 + + + + https://resendislab.github.io/pubs/pm28536537/ + 2017-05-26T00:00:00+00:00 + + + + https://resendislab.github.io/events/biological_physics/ + 2017-05-17T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm28410570/ + 2017-04-16T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm28082911/ + 2017-01-14T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm28018236/ + 2016-12-27T00:00:00+00:00 + + + + https://resendislab.github.io/posts/test/ + 2016-12-06T09:19:57-06:00 + + + + https://resendislab.github.io/projects/emt/ + 2016-12-06T00:00:00+00:00 + + + + https://resendislab.github.io/projects/hepato/ + 2016-12-06T00:00:00+00:00 + + + + https://resendislab.github.io/projects/prolif/ + 2016-12-06T00:00:00+00:00 + + + + https://resendislab.github.io/projects/spheroids/ + 2016-12-06T00:00:00+00:00 + + + + https://resendislab.github.io/about/we/ + 2016-12-05T14:48:16-06:00 + + + + https://resendislab.github.io/pubs/pm27616995/ + 2016-09-13T00:00:00+00:00 + + + + https://resendislab.github.io/events/is3b/ + 2016-08-02T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm27335086/ + 2016-06-24T00:00:00+00:00 + + + + https://resendislab.github.io/events/is3b_2014/ + 2014-08-04T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm24747697/ + 2014-04-22T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/encyclopedia/ + 2013-06-01T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm23316163/ + 2013-01-15T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/symbiotic_endophytes/ + 2013-01-01T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm23071431/ + 2012-10-17T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm21801415/ + 2011-08-02T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm21696634/ + 2011-06-24T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm20811631/ + 2010-09-03T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm19305506/ + 2009-03-24T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm19076632/ + 2008-12-17T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm17922569/ + 2007-10-10T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm17559662/ + 2007-06-15T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm17188715/ + 2006-12-26T00:00:00+00:00 + + + + https://resendislab.github.io/pubs/pm15680508/ + 2005-02-01T00:00:00+00:00 + + + + https://resendislab.github.io/about/contact/ + 1900-01-01T00:00:00+00:00 + + + + https://resendislab.github.io/members/dummy/ + + + + https://resendislab.github.io/about/ + 2016-12-05T14:48:16-06:00 + 0 + + + + https://resendislab.github.io/categories/ + 0 + + + + https://resendislab.github.io/events/ + 2017-05-17T00:00:00+00:00 + 0 + + + + https://resendislab.github.io/members/ + 0 + + + + https://resendislab.github.io/positions/ + 2017-06-29T12:53:05-05:00 + 0 + + + + https://resendislab.github.io/posts/ + 2016-12-06T09:19:57-06:00 + 0 + + + + https://resendislab.github.io/projects/ + 2016-12-06T00:00:00+00:00 + 0 + + + + https://resendislab.github.io/pubs/ + 2017-05-26T00:00:00+00:00 + 0 + + + + https://resendislab.github.io/categories/science/ + 2016-12-06T09:19:57-06:00 + 0 + + + + https://resendislab.github.io/tags/ + 0 + + + + https://resendislab.github.io/categories/tutorial/ + 2016-12-06T09:19:57-06:00 + 0 + + + + https://resendislab.github.io/ + 2017-06-29T12:53:05-05:00 + 0 + + + \ No newline at end of file diff --git a/docs/tags/index.xml b/docs/tags/index.xml new file mode 100644 index 0000000..cc9d9d9 --- /dev/null +++ b/docs/tags/index.xml @@ -0,0 +1,14 @@ + + + + Tags on Webpage of the Resendis Lab + https://resendislab.github.io/tags/ + Recent content in Tags on Webpage of the Resendis Lab + Hugo -- gohugo.io + en-us + + + + + + \ No newline at end of file diff --git a/events/index.html b/events/index.html deleted file mode 100644 index 9524f4a..0000000 --- a/events/index.html +++ /dev/null @@ -1,54 +0,0 @@ -Webpage of the Resendis Lab
2019 -2017 -2016 -2014

Biological Physics Mexico City 2019

06-09-2019 to 04-09-2019


Frontier Science at the Intersection of Physics, Math and Biology

The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments.

The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians. The venue is highly convenient since there are four major Research Universities in Mexico City’s metropolitan area, with extensive undergraduate and graduate programs in physics, biology, medicine, engineering and mathematics.

The program includes:

  • Talks by national and international experts;
  • Poster session for undergraduate / graduate students
  • Round-Table session on new trends in biological physics.
  • A dedicated issue of conference proceedings.
map Map -link Webpage

3st International Summer Symposium on Systems Biology

05-08-2019 to 06-08-2019


Those are the proceedings for the 3rd edition of the “International Summer Symposium on Systems -Biology”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields. -This effort was supported by the Laboratory of Human Systems Biology-INMEGEN to create a scientific ambiance for discussing some methods and strategies to develop the -bases of a systemic and personalized medicine in a national and international perspective.

The purposes of the meeting were:

  • Discuss some of the frontier research in Systems Biology and its applications for understanding -human diseases.

  • Create an ambiance to establish collaborations among groups that will promote different -computational frameworks for modeling human diseases.

  • Design strategies to encourage growth in this area in biomedical, medicine and genomic sciences at -the undergraduate and graduate levels since these are areas with potential for dealing with health -problems in Mexico.

To reach these goals, the meeting brought together some national and international qualified experts -from different research groups, providing an excellent occasion for academic exchange between local -and foreign colleagues in a pleasant and collaborative environment. The program included plenary -lectures, poster sessions, a discussion panel and a series of short presentations geared towards -postdoctoral researchers and advanced graduate students.

map Map -link Webpage

Biological Physics Mexico City 2017

17-05-2017 to 19-05-2017


Frontiers at the interface of Physics, Math and Biology.

This conference (the second in a series) is intended as an international, -multidisciplinary scientific forum to discuss the latest developments in -biological physics (including proteins, peptides and enzymes, among many other -topics).

The conference is expected to boost a new paradigm of interdisciplinary -approaches converging into specific problems in biological physics. Hence, the -conference audience is broad: We aim to attract the attention of biologists as -well as biochemists, organic chemists, engineers, computational scientists, -physicists, and mathematicians. The venue is highly convenient since there are -four major Research Universities in Mexico City’s metropolitan area, with -extensive undergraduate and graduate programs in physics, biology, medicine, -engineering and mathematics.

The program includes:

  • Talks by national and international experts
  • Poster session for undergraduate/graduate students
  • Round-Table session on new trends in biological physics
  • A dedicated issue of conference proceedings.

Due to the generosity of the sponsoring institutions, no fees will be charged -to those selected to participate in this conference.

map Map -link Webpage

2nd International Summer Symposium on Systems Biology

02-08-2016 to 04-08-2016


With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM & INMEGEN.

The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine. The aims of the meeting are:

  1. Discuss current research in Systems Biology and its applications for understanding human diseases
  2. Create an ambiance that enables scientific collaborations among experimental and theoretical groups working on human diseases.

To this extent we invite national and international researchers and students working in the aforementioned fields during all stages of their academic career with the possibility to present their work as a poster or short talk to a highly qualified research community.

The registration deadline has been extended until June 15th, 2016!

map Map -link Webpage

1st International Summer Symposium on Systems Biology

04-08-2014 to 06-08-2014


Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems -Biology: From networks to phenotypes in human diseases”. The meeting took place at the National -Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the -first of a series of meetings to encourage the development of the systems biology in Mexico and the -development of this area to tackle basic and applied research in medical and biomedical fields. -This effort was supported by the Laboratory of Human Systems Biology-INMEGEN and Fundación -Televisa to create a scientific ambiance for discussing some methods and strategies to develop the -bases of a systemic and personalized medicine in a national and international perspective.

The purposes of the meeting were:

  • Discuss some of the frontier research in Systems Biology and its applications for understanding -human diseases.

  • Create an ambiance to establish collaborations among groups that will promote different -computational frameworks for modeling human diseases.

  • Design strategies to encourage growth in this area in biomedical, medicine and genomic sciences at -the undergraduate and graduate levels since these are areas with potential for dealing with health -problems in Mexico.

To reach these goals, the meeting brought together some national and international qualified experts -from different research groups, providing an excellent occasion for academic exchange between local -and foreign colleagues in a pleasant and collaborative environment. The program included plenary -lectures, poster sessions, a discussion panel and a series of short presentations geared towards -postdoctoral researchers and advanced graduate students.

map Map -link Webpage
\ No newline at end of file diff --git a/events/index.xml b/events/index.xml deleted file mode 100644 index e13fcda..0000000 --- a/events/index.xml +++ /dev/null @@ -1,4 +0,0 @@ -Events on Webpage of the Resendis Labhttps://resendislab.github.io/events/Recent content in Events on Webpage of the Resendis LabHugo -- gohugo.ioen-usFri, 06 Sep 2019 00:00:00 +0000Biological Physics Mexico City 2019https://resendislab.github.io/events/biophysmex2019/Fri, 06 Sep 2019 00:00:00 +0000https://resendislab.github.io/events/biophysmex2019/Frontier Science at the Intersection of Physics, Math and Biology The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments. -The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics.3st International Summer Symposium on Systems Biologyhttps://resendislab.github.io/events/is3b_2019/Mon, 05 Aug 2019 00:00:00 +0000https://resendislab.github.io/events/is3b_2019/Those are the proceedings for the 3rd edition of the “International Summer Symposium on Systems Biology”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields.Biological Physics Mexico City 2017https://resendislab.github.io/events/biological_physics/Wed, 17 May 2017 00:00:00 +0000https://resendislab.github.io/events/biological_physics/Frontiers at the interface of Physics, Math and Biology. This conference (the second in a series) is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics (including proteins, peptides and enzymes, among many other topics). -The conference is expected to boost a new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians.2nd International Summer Symposium on Systems Biologyhttps://resendislab.github.io/events/is3b/Tue, 02 Aug 2016 00:00:00 +0000https://resendislab.github.io/events/is3b/With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM &amp; INMEGEN. -The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine.1st International Summer Symposium on Systems Biologyhttps://resendislab.github.io/events/is3b_2014/Mon, 04 Aug 2014 00:00:00 +0000https://resendislab.github.io/events/is3b_2014/Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems Biology: From networks to phenotypes in human diseases”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields. \ No newline at end of file diff --git a/index.html b/index.html deleted file mode 100644 index 02891bc..0000000 --- a/index.html +++ /dev/null @@ -1,11 +0,0 @@ -Webpage of the Resendis Lab

RESENDIS ANTONIO LAB

Blending Biology and Computation to understand human diseases.



Who we are

Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. -Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales.

Read more...

Latest News

Biological Physics Mexico City 2019

from 06-09-2019 to 04-09-2019


Frontier Science at the Intersection of Physics, Math and Biology The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments. -The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics.

Read more...

Latest Publication

mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data

Bioinformatics 2023

Cristian Padron-Manrique, Aaron Vazquez-Jimenez, Diego A Esquivel-Hernandez, Yoscelina Estrella Martinez-Lopez, Daniel Neri-Rosario, Jean Paul Sanchez, David Giron-Villalobos and Osbaldo Resendis-Antonio

Motivation Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. Results We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data.

See all...
\ No newline at end of file diff --git a/index.xml b/index.xml deleted file mode 100644 index db601fe..0000000 --- a/index.xml +++ /dev/null @@ -1,22 +0,0 @@ -Webpage of the Resendis Labhttps://resendislab.github.io/Recent content on Webpage of the Resendis LabHugo -- gohugo.ioen-usFri, 01 Dec 2023 00:00:00 +0000mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota datahttps://resendislab.github.io/pubs/mbphenix/Fri, 01 Dec 2023 00:00:00 +0000https://resendislab.github.io/pubs/mbphenix/Motivation Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. Results We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data.Computational modeling of metabolic dynamics in the intratumoral microenvironment.https://resendislab.github.io/projects/gem_spheroids/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/projects/gem_spheroids/Currently, oncology research has been focused on investigating cancer metabolism due to its remarkable capacity to adapt to changes in its microenvironment, enabling it to efficiently respond to gradients of oxygen and nutrients. In 3D spheroid cultures of MCF-7 cells, three distinct cell subpopulations with varying metabolic characteristics have been identified, indicating that each subpopulation fulfills specific activities within the tumor, promoting its progression and survival. -This project proposes the utilization of genome-scale metabolic reconstructions (GEMS) to model the growth of each subpopulation.Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohorthttps://resendislab.github.io/pubs/dysbiosis_diabetes/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/pubs/dysbiosis_diabetes/Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual’s gut microbiota profile.Machine Learning-Based Exploration of Gut Microbiota's Impact on Type 2 Diabeteshttps://resendislab.github.io/projects/diabetes_micom/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/projects/diabetes_micom/Employing machine learning algorithms to investigate the role of the gut microbiota in the development and management of Type 2 diabetes (T2DM). By analyzing microbiome data from individuals with multiple diabetes treatments, my aim is to identify specific microbial compositions associated with the disease and develop predictive models that assess an individual&rsquo;s risk of developing T2DM based on their microbiota profile. Also using a systems biology approach (MICOM) using the gut microbiota data to identify the metabolic changes in the community associated with T2DM.On Type 2 diabetes, and their relation with the gut microbiome metabolism.https://resendislab.github.io/projects/diabetes_metformin/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/projects/diabetes_metformin/Type 2 diabetes mellitus (T2D) is a widespread disease worldwide, the etiology may be associated with gut microbiota influenced by different diet patterns. Metformin is a T2D treatment, and it is known that can alter the gut microbiota composition, but a few is known about the relation between this composition and the physio-pathological variables, therefore in this project the microbiota composition is analyzed, as well as the computational modeling of metabolism in microbiota to infer the growth rates of selected bacteria and the metabolic interactions into gut microbiota on patients with T2D under metformin and linagliptin treatmentUncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approachhttps://resendislab.github.io/pubs/uncoding_macrophages/Mon, 22 May 2023 00:00:00 +0000https://resendislab.github.io/pubs/uncoding_macrophages/The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment.A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohorthttps://resendislab.github.io/pubs/mycrobiota_diabetes/Wed, 12 Apr 2023 00:00:00 +0000https://resendislab.github.io/pubs/mycrobiota_diabetes/Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. -Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencinghttps://resendislab.github.io/pubs/spermiogenesis/Thu, 30 Mar 2023 00:00:00 +0000https://resendislab.github.io/pubs/spermiogenesis/CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm.A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiotahttps://resendislab.github.io/pubs/postprandial_glycemic/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/pubs/postprandial_glycemic/A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others.Chronic Comorbidities in Middle Aged Patients Contribute to Ineffective Emergency Hematopoiesis in Covid-19 Fatal Outcomeshttps://resendislab.github.io/pubs/comorbidities_covid/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/pubs/comorbidities_covid/Background and Aims Mexico is among the countries with the highest estimated excess mortality rates due to the COVID–19 pandemic, with more than half of reported deaths occurring in adults younger than 65 years old. Although this behavior is presumably influenced by the young demographics and the high prevalence of metabolic diseases, the underlying mechanisms have not been determined. Methods The age–stratified case fatality rate (CFR) was estimated in a prospective cohort with 245 hospitalized COVID–19 cases, followed through time, for the period October 2020–September 2021.Machine Learning and COVID-19: Lessons from SARS-CoV-2https://resendislab.github.io/pubs/machine_covid/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/pubs/machine_covid/Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population.Metabolic changes in macrophage polarization through in silico approacheshttps://resendislab.github.io/projects/metabolic_macrophages/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/projects/metabolic_macrophages/Macrophages, crucial components of the innate immune system, have the remarkable ability to polarize and adopt various phenotypes in response to fluctuations in their microenvironment. Considered as &ldquo;double-edged swords”, these cells serve a wide array of physiological roles; however, their dysfunction can contribute to the development of various diseases, such as cancer, tuberculosis, and atherosclerosis. Furthermore, macrophage polarization is critically supported by metabolic shifts, and there is an exciting potential for regulating macrophage functions in different contexts by manipulating their metabolism. Research on Children Leukemiahttps://resendislab.github.io/projects/leukemia/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/leukemia/Leukemia is the most common cancer in children worldwide, highest incidences and worse prognostics are for low and middle-income countries where less than 30% are cured. In Mexico 4,000 to 6,000 new cases are registered each year. Epidemiological studies have shown the contribution of environmental factors to the development of Leukemia, but also clinical factors such as late and imprecise diagnosis of the disease, limited access, and /or adherence to treatment, and tolerance and toxicity of antineoplastic drugs.Alteration of gut microbiota induced by metformin and linagliptin/metformin treatment prevents type 2 diabetes.https://resendislab.github.io/projects/t2d/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/t2d/Lifestyle modifications, metformin and dipeptidyl peptidase type 4 inhibitors (DPP4i) reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The efficacy of such interventions may be enhanced by the gut microbiota (GM), which plays a role in mediating glucose-lowering effects through the increased abundance of short-chain fatty acid (SCFA)-producing bacteria. We determined the effect of combined linagliptin+metformin vs metformin monotherapy on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes without a previous treatment and compared it between metformin monotherapy and the combination of linagliptin+metformin.Computational modeling of the gut microbiota metabolism in COVID-19 patientshttps://resendislab.github.io/projects/covidmicom/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/covidmicom/I study the gut microbiota to find how microbes participate in the development of COVID-19. To do that, I use MICOM, a community metabolic computational model, that can predict metabolic interactions within the microbiota and the host.Ecological study on gut microbiotahttps://resendislab.github.io/projects/ecologicalgut/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/ecologicalgut/We analyze the dynamics of the metabolism of the gut microbiota in longitudinal databases through a hybrid model between generalized Lotka-Volterra and flux balance analysis (FBA)Gut microbiota and type 2 diabeteshttps://resendislab.github.io/projects/t2dmicom/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/t2dmicom/A direct link between the gut microbiota (GM) and the progression of type 2 diabetes mellitus (T2D) in individuals has been described. We propose using supervised Machine Learning (ML) methods to identify predictive taxa for patients with prediabetes (pre-T2D) and T2D.Manifold learning approaches for high dimensional biological datahttps://resendislab.github.io/projects/scphenix/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/scphenix/Modern high–throughput biological data yield detailed characterizations of the genomic, transcriptomic, and proteomic states of samples. This kind of data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discovery reliability. Data sparsity makes it difficult to obtain a well-data structure and distorts the distribution of variables. Currently, there is a raised need to develop new algorithms with improved capacities to reduce noise and recover missing information.Macrophage Boolean networks in the time of SARS-CoV-2https://resendislab.github.io/pubs/macrophage_boolean/Mon, 17 Oct 2022 00:00:00 +0000https://resendislab.github.io/pubs/macrophage_boolean/The post-pandemic period of the current coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has lasted longer than expected despite the huge impact of the world-wide vaccination campaign in the past years. Since the pandemic began, endless mathematical models have been published to describe the viral outbreak at a population level. However, the molecular mechanism that drives the pathogenesis of the virus in the human microenvironment has been scarce.Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new erahttps://resendislab.github.io/pubs/type2_diabetes/Mon, 15 Aug 2022 00:00:00 +0000https://resendislab.github.io/pubs/type2_diabetes/The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions.prePrint: A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohorthttps://resendislab.github.io/pubs/ecology_gut/Wed, 20 Jul 2022 00:00:00 +0000https://resendislab.github.io/pubs/ecology_gut/Background -The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Results -Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).Comparative subcellular localization of NRF2 and KEAP1 during the hepatocellular carcinoma development in vivohttps://resendislab.github.io/pubs/comparative_subcellular/Sun, 01 May 2022 00:00:00 +0000https://resendislab.github.io/pubs/comparative_subcellular/The activation of Nuclear Factor, Erythroid 2 Like 2 – Kelch Like ECH Associated Protein 1 (NRF2-KEAP1) signaling pathway plays a critical dual role by either protecting or promoting the carcinogenesis process. However, its activation or nuclear translocation during hepatocellular carcinoma (HCC) progression has not been addressed yet. This study characterizes the subcellular localization of both NRF2 and KEAP1 during diethylnitrosamine-induced hepatocarcinogenesis in the rat. NRF2-KEAP1 pathway was continuously activated along with the increased expression of its target genes, namely Nqo1, Hmox1, Gclc, and Ptgr1.Physiological Network Is Disrupted in Severe COVID-19https://resendislab.github.io/pubs/physiological_networks/Thu, 10 Mar 2022 00:00:00 +0000https://resendislab.github.io/pubs/physiological_networks/The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism’s homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else.Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seqhttps://resendislab.github.io/pubs/mathematics_2021/Thu, 07 Oct 2021 00:00:00 +0000https://resendislab.github.io/pubs/mathematics_2021/The single-cell RNA-seq allows exploring the transcriptome for one cell at a time. By doing so, cellular regulation is pictured. One limitation is the dropout events phenomenon, where a gene is observed at a low or moderate expression level in one cell but not detected in another. Dropouts obscure legitimate biological heterogeneity leading to the description of a small fraction of the meaningful relations. We used a stochastic approach to model the Reverse Transcription Polymerase Chain Reaction (RT-PCR) kinetic, in which we contemplated the temperature profile, RT-PCR duration, and reaction rates.On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markershttps://resendislab.github.io/pubs/frontiers_immunology_2021/Thu, 16 Sep 2021 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_immunology_2021/COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses.Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysishttps://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/Thu, 10 Jun 2021 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells.MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progressionhttps://resendislab.github.io/pubs/fontiers_oncology_2020_erick/Fri, 04 Dec 2020 00:00:00 +0000https://resendislab.github.io/pubs/fontiers_oncology_2020_erick/During tumor progression, cancer cells ire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7).Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgroundshttps://resendislab.github.io/pubs/frontiers_onlogy2020/Sun, 02 Aug 2020 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_onlogy2020/Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds.Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seqhttps://resendislab.github.io/pubs/scientificreports2020/Thu, 02 Jul 2020 00:00:00 +0000https://resendislab.github.io/pubs/scientificreports2020/Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship.Memote: A community driven effort towards a standardized genome-scale metabolic model test suitehttps://resendislab.github.io/pubs/memote/Mon, 02 Mar 2020 00:00:00 +0000https://resendislab.github.io/pubs/memote/Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation.Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota.https://resendislab.github.io/pubs/micom/Tue, 04 Feb 2020 00:00:00 +0000https://resendislab.github.io/pubs/micom/Alterations in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn&rsquo;s disease and diabetes. However, establishing the causality between the microbial composition and disease remains a challenge. We introduce a strategy based on metabolic models of complete microbial gut communities in order to derive the particular metabolic consequences of the microbial composition for the diabetic gut in a balanced cohort of 186 individuals. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that allows growth for the majority of observed taxa in a sample.In silico study of metabolic reprogramming during epithelial-mesenchymal transitionhttps://resendislab.github.io/projects/emt/Fri, 06 Dec 2019 00:00:00 +0000https://resendislab.github.io/projects/emt/An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes.Microbiome metabolism and diabeteshttps://resendislab.github.io/projects/diabe_metabolism/Fri, 06 Dec 2019 00:00:00 +0000https://resendislab.github.io/projects/diabe_metabolism/Alterations in the microbiome has been associated with diabetes progression.Biological Physics Mexico City 2019https://resendislab.github.io/events/biophysmex2019/Fri, 06 Sep 2019 00:00:00 +0000https://resendislab.github.io/events/biophysmex2019/Frontier Science at the Intersection of Physics, Math and Biology The BioPhys Mexico City 2019 conference, the third in a biennial series, is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics, including experimental, theoretical and computational methods, from a single molecule perspective to complex multi-component environments. -The conference is expected to boost the new paradigm of interdisciplinary approaches converging into specific problems in biological physics.3st International Summer Symposium on Systems Biologyhttps://resendislab.github.io/events/is3b_2019/Mon, 05 Aug 2019 00:00:00 +0000https://resendislab.github.io/events/is3b_2019/Those are the proceedings for the 3rd edition of the “International Summer Symposium on Systems Biology”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields.Immunology and cancer: Boolean Modeling of regulatory networkshttps://resendislab.github.io/projects/inmunology_cancer/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/inmunology_cancer/Macrophages are cells of the innate immune system endowed with the capacity to orchestrate the immune response in human tissues. Due to their plasticity biological property, they polarize to several subtypes based on the actions of the tumor microenvironment. These cells have plasticity, because once they are committed to a subtype fate, they can polarize to another subtype by simply modifying the microenvironment. We integrated experimental data for the construction of a network that will explain the plasticity and the importance of the microenvironment in shaping the polarization of macrophages.Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat modelhttps://resendislab.github.io/projects/hepato/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/hepato/Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions.Metabolic heterogeneity in cancer and its applications in Personalized Medicinehttps://resendislab.github.io/projects/prolif/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/prolif/Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation. -In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism.Systems biology and bioinformatics of single cell RNAseq data.https://resendislab.github.io/projects/singlecell_thelma/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/singlecell_thelma/Research in personalized therapy has taken relevance because treatment failures due to intratumoral heterogenety which refers to celular diversity or subpopulations forming within the tumor. Currently, given complex molecular processes of cancer there has been greater use of omic technologies and computational analysis. With the purpuse to contribute in this line, we have opened a new line of research to describe the progress of expression profiles in tumor cell lines through bioinformatic analysis of single cell RNAseq data.The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids developmenthttps://resendislab.github.io/projects/spheroids/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/spheroids/Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer.Cancer: a complex diseasehttps://resendislab.github.io/pubs/cancer_a_complex_disease/Sat, 01 Dec 2018 00:00:00 +0000https://resendislab.github.io/pubs/cancer_a_complex_disease/This is an EBook can be downloaded for free. -The study of complex systems and their related phenomena has become a major research venue in the recent years and it is commonly regarded as an important part of the scientific revolution developing through the 21st century. The science of complexity is concerned with the laws of operation and evolution of systems formed by many locally interacting elements that produce collective order at spatiotemporal scales larger than that of the single constitutive elements.Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.https://resendislab.github.io/pubs/pm30376889/Wed, 31 Oct 2018 00:00:00 +0000https://resendislab.github.io/pubs/pm30376889/Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes.Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.https://resendislab.github.io/pubs/pm29704665/Sun, 29 Apr 2018 00:00:00 +0000https://resendislab.github.io/pubs/pm29704665/Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals.Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissueshttps://resendislab.github.io/pubs/quantitative_modeling/Thu, 01 Mar 2018 00:00:00 +0000https://resendislab.github.io/pubs/quantitative_modeling/This book presents cutting-edge research on the use of physical and mathematical formalisms to model and quantitatively analyze biological phenomena ranging from microscopic to macroscopic systems. The systems discussed in this compilation cover protein folding pathways, gene regulation in prostate cancer, quorum sensing in bacteria to mathematical and physical descriptions to analyze anomalous diffusion in patchy environments and the physical mechanisms that drive active motion in large sets of particles, both fundamental descriptions that can be applied to different phenomena in biology.Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancerhttps://resendislab.github.io/pubs/frontiers_ebook/Mon, 27 Nov 2017 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_ebook/This is an EBook compendium of the respective Frontiers Research Topic and can be downloaded for free. -Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system.micomhttps://resendislab.github.io/software/micom/Sun, 01 Oct 2017 00:00:00 +0000https://resendislab.github.io/software/micom/micom is a Python package for metabolic modeling of microbial communities developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio at the National Institute of Genomic Medicine Mexico. -micom allows you to construct a community model from a list on input COBRA models and manages exchange fluxes between individuals and individuals with the environment. It explicitly accounts for different abundances of individuals in the community and can thus incorporate data from 16S rRNA sequencing experiments.Our microbiome pipelinehttps://resendislab.github.io/software/microbiome/Tue, 01 Aug 2017 00:00:00 +0000https://resendislab.github.io/software/microbiome/This repository contains the standardized analysis pipeline for 16S and metagenome data. It serves as a testing ground for what will be required to analyze around 500 samples.Editorial: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancerhttps://resendislab.github.io/pubs/editorial_frontiers/Fri, 28 Jul 2017 12:00:00 +0000https://resendislab.github.io/pubs/editorial_frontiers/This is the Editorial for our Frontiers Research Topic &ldquo;Systems Biology and the challenge of deciphering the metabolic mechanisms underlying cancer&rdquo;. -The corresponding E-Book will be available soon.Graduate student positionshttps://resendislab.github.io/positions/students/Thu, 29 Jun 2017 12:53:05 -0500https://resendislab.github.io/positions/students/We extend an invitation to undergrads and grad students with interest to continue his/her academic education through a Master&rsquo;s or Doctoral degree in one of these academic programs: biological (http://pcbiol.posgrado.unam.mx), biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical (http://www.pdcb.unam.mx/) Sciences at UNAM. We encourage candidates with an academic background in biology, biology physics, biophysics, genome sciences, applied mathematics and computational sciences. The students incorporated to one of these programs will be guided to develop a systems biology description in one of these areas:Postdoctoral positionhttps://resendislab.github.io/positions/postdocs/Thu, 29 Jun 2017 12:52:56 -0500https://resendislab.github.io/positions/postdocs/We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to [oresendis [at] inmegen."Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.https://resendislab.github.io/pubs/pm28536537/Fri, 26 May 2017 00:00:00 +0000https://resendislab.github.io/pubs/pm28536537/The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the &ldquo;psychiatric phenotype&rdquo; is to provide an improved vision of the shape of the phenotype as it is visualized by &ldquo;Gestalt&rdquo; psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part.Biological Physics Mexico City 2017https://resendislab.github.io/events/biological_physics/Wed, 17 May 2017 00:00:00 +0000https://resendislab.github.io/events/biological_physics/Frontiers at the interface of Physics, Math and Biology. This conference (the second in a series) is intended as an international, multidisciplinary scientific forum to discuss the latest developments in biological physics (including proteins, peptides and enzymes, among many other topics). -The conference is expected to boost a new paradigm of interdisciplinary approaches converging into specific problems in biological physics. Hence, the conference audience is broad: We aim to attract the attention of biologists as well as biochemists, organic chemists, engineers, computational scientists, physicists, and mathematicians.CORDA for Pythonhttps://resendislab.github.io/software/corda/Mon, 01 May 2017 00:00:00 +0000https://resendislab.github.io/software/corda/This is a Python implementation based on the papers of Schultz et. al. with some added optimizations. It is based on the publications of Schultz et. al. [1, 2]. -CORDA, short for Cost Optimization Reaction Dependency Assessment is a method for the reconstruction of metabolic networks from a given reference model (a database of all known reactions) and a confidence mapping for reactions. It allows you to reconstruct metabolic models for tissues, patients or specific experimental conditions from a set of transcription or proteome measurements.Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.https://resendislab.github.io/pubs/pm28410570/Sun, 16 Apr 2017 00:00:00 +0000https://resendislab.github.io/pubs/pm28410570/Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling.Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.https://resendislab.github.io/pubs/pm28082911/Sat, 14 Jan 2017 00:00:00 +0000https://resendislab.github.io/pubs/pm28082911/Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas.Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine.https://resendislab.github.io/pubs/pm28018236/Tue, 27 Dec 2016 00:00:00 +0000https://resendislab.github.io/pubs/pm28018236/It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer.Hola!!!https://resendislab.github.io/posts/test/Tue, 06 Dec 2016 09:19:57 -0600https://resendislab.github.io/posts/test/<h2 id="this-is-an-example-post">This is an example post</h2> -<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> -<p>This is my text now grrrr :)</p>Who we arehttps://resendislab.github.io/about/we/Mon, 05 Dec 2016 14:48:16 -0600https://resendislab.github.io/about/we/Welcome to the webpage of the Human Systems Biology group in the National Institute for Genomic Medicine at Mexico City, INMEGEN. Our group is interdisciplinary and have the objective to develop a systems biology framework to analyze mainly human diseases and metabolic phenotype in microorganisms through the use of computational models and high-throughput technologies. -Currently, our laboratory focuses on the analysis of metabolic alterations in cancer cells by the implementation of genome scale metabolic reconstructions and assess the predictions in terms of experimental data at different scales.Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.https://resendislab.github.io/pubs/pm27616995/Tue, 13 Sep 2016 00:00:00 +0000https://resendislab.github.io/pubs/pm27616995/2nd International Summer Symposium on Systems Biologyhttps://resendislab.github.io/events/is3b/Tue, 02 Aug 2016 00:00:00 +0000https://resendislab.github.io/events/is3b/With great pleasure we are hereby announcing the 2nd International Summer Symposium on Systems Biology (IS3B) taking place in Mexico City, Mexico from August 2nd - 4th 2016. The IS3B 2016 is organized by The Human Systems Biology Laboratory (HSBL), RAI-UNAM &amp; INMEGEN. -The IS3B is currently the largest symposium on Systems Biology in Mexico and Latin America, and strives to unite leading researchers and students in an informal setting with the aim to present current research in Systems Biology and Systems Medicine.The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome.https://resendislab.github.io/pubs/pm27335086/Fri, 24 Jun 2016 00:00:00 +0000https://resendislab.github.io/pubs/pm27335086/During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation.dyconehttps://resendislab.github.io/software/dycone/Wed, 01 Jun 2016 00:00:00 +0000https://resendislab.github.io/software/dycone/Dycone (&ldquo;dynamic cone&rdquo;) allows you infer enzymatic regulation from metabolome measurements. It employs formalisms based on flux and k-cone analysis to connect metabolome data to distinct regulations of enzyme activity. Most of the analysis methods can be applied to genome-scale data.1st International Summer Symposium on Systems Biologyhttps://resendislab.github.io/events/is3b_2014/Mon, 04 Aug 2014 00:00:00 +0000https://resendislab.github.io/events/is3b_2014/Those are the proceedings for the 1st edition of the “International Summer Symposium on Systems Biology: From networks to phenotypes in human diseases”. The meeting took place at the National Institute of Genomic Medicine (INMEGEN) in Mexico City from August 4-6, 2014. This event was the first of a series of meetings to encourage the development of the systems biology in Mexico and the development of this area to tackle basic and applied research in medical and biomedical fields.Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer.https://resendislab.github.io/pubs/pm24747697/Tue, 22 Apr 2014 00:00:00 +0000https://resendislab.github.io/pubs/pm24747697/Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients.Encyclopedia of Systems Biologyhttps://resendislab.github.io/pubs/encyclopedia/Sat, 01 Jun 2013 00:00:00 +0000https://resendislab.github.io/pubs/encyclopedia/The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology.Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.https://resendislab.github.io/pubs/pm23316163/Tue, 15 Jan 2013 00:00:00 +0000https://resendislab.github.io/pubs/pm23316163/One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines.Symbiotic Endophyteshttps://resendislab.github.io/pubs/symbiotic_endophytes/Tue, 01 Jan 2013 00:00:00 +0000https://resendislab.github.io/pubs/symbiotic_endophytes/This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields.Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironmenthttps://resendislab.github.io/pubs/boolean_modeling/Wed, 05 Dec 2012 00:00:00 +0000https://resendislab.github.io/pubs/boolean_modeling/Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage.Functional modules, structural topology, and optimal activity in metabolic networks.https://resendislab.github.io/pubs/pm23071431/Wed, 17 Oct 2012 00:00:00 +0000https://resendislab.github.io/pubs/pm23071431/Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris.Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.https://resendislab.github.io/pubs/pm21801415/Tue, 02 Aug 2011 00:00:00 +0000https://resendislab.github.io/pubs/pm21801415/Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation.Proteomic patterns of cervical cancer cell lines, a network perspective.https://resendislab.github.io/pubs/pm21696634/Fri, 24 Jun 2011 00:00:00 +0000https://resendislab.github.io/pubs/pm21696634/Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect.https://resendislab.github.io/pubs/pm20811631/Fri, 03 Sep 2010 00:00:00 +0000https://resendislab.github.io/pubs/pm20811631/Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.https://resendislab.github.io/pubs/pm19305506/Tue, 24 Mar 2009 00:00:00 +0000https://resendislab.github.io/pubs/pm19305506/Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.Regulation by transcription factors in bacteria: beyond description.https://resendislab.github.io/pubs/pm19076632/Wed, 17 Dec 2008 00:00:00 +0000https://resendislab.github.io/pubs/pm19076632/Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts.Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli.https://resendislab.github.io/pubs/pm17922569/Wed, 10 Oct 2007 00:00:00 +0000https://resendislab.github.io/pubs/pm17922569/Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement.Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.https://resendislab.github.io/pubs/pm17559662/Fri, 15 Jun 2007 00:00:00 +0000https://resendislab.github.io/pubs/pm17559662/Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses.Robustness and evolvability in genetic regulatory networks.https://resendislab.github.io/pubs/pm17188715/Tue, 26 Dec 2006 00:00:00 +0000https://resendislab.github.io/pubs/pm17188715/Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve.Modular analysis of the transcriptional regulatory network of E. coli.https://resendislab.github.io/pubs/pm15680508/Tue, 01 Feb 2005 00:00:00 +0000https://resendislab.github.io/pubs/pm15680508/The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules.Contacthttps://resendislab.github.io/about/contact/Mon, 01 Jan 1900 00:00:00 +0000https://resendislab.github.io/about/contact/Directions Osbaldo Resendis-Antonio, PhD -Laboratory in Systems Biology and Human Diseases -Associated Professor -Instituto Nacional de Medicina Genomica – INMEGEN -Periferico Sur 4809, Arenal Tepepan, Tlalpan, 14610 Mexico City, CDMX -Phone: +52 55 5350 1900 - Ext.1198<link>https://resendislab.github.io/members/dummy/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/members/dummy/</guid><description>This is a dummy page. It&rsquo;s content will not be rendered.</description></item></channel></rss> \ No newline at end of file diff --git a/layouts/index.html b/layouts/index.html new file mode 100644 index 0000000..4c1debb --- /dev/null +++ b/layouts/index.html @@ -0,0 +1,57 @@ +{{ partial "header.html" . }} +<div class="mdl-color--indigo-900" id="neuro"><canvas id="demo-canvas"></canvas></div> +<div class="mdl-color-text--white main-title"><h1>RESENDIS ANTONIO LAB</h1><hr> +<h2>Blending Biology and Computation to understand human diseases.</h2></div> +<div class="page-content mdl-grid"> +<br><br> +{{ range first 1 (where .Site.RegularPages "Section" "about")}} +<div class="mdl-cell mdl-cell--12-col"> +<div class="card mdl-card mdl-shadow--4dp"> + <div class="mdl-card__supporting-text"> + <h1 class="mdl-color-text--primary">{{ .Title }}</h1> + <p>{{.Summary}}</p> + </div> + <div class="mdl-card__actions mdl-card--border"> + <a class="mdl-button mdl-button--colored mdl-js-button mdl-js-ripple-effect" href="/{{ .Section }}">Read more...</a> + </div> +</div></div> +{{ end }} +{{ $fornews := slice "posts" "events" }} +{{ range first 1 (where .Site.RegularPages "Section" "in" $fornews)}} +<div class="mdl-cell mdl-cell--12-col"> +<div class="card mdl-card mdl-shadow--4dp"> + <div class="mdl-card__title" {{ if .Params.hero }}style="background: url('/media/{{.Params.hero}}') center / cover"{{end}}> + <h1 class="mdl-card__title-text">Latest News</h1> + </div> + <div class="mdl-card__supporting-text"> + <h1 class="mdl-color-text--primary">{{.Title}}</h1> + {{ if eq .Section "posts" }}<h2 class="mdl-color-text--teal-500">by {{ .Params.author }}</h2><br/> + {{ else }}<h2 class="mdl-color-text--teal-500">from {{ .Date.Format "02-01-2006" }} to {{ dateFormat "02-01-2006" .Params.end }}</h2><br/>{{ end }} + <p>{{.Summary}}</p> + </div> + <div class="mdl-card__actions mdl-card--border"> + <a class="mdl-button mdl-button--colored mdl-js-button mdl-js-ripple-effect" href="{{ if eq .Section "posts" }}{{.RelPermalink}}{{ else }}/{{ .Section }}{{ end }}">Read more...</a> + </div> +</div></div> +{{end}} +{{ range first 1 (where .Site.RegularPages "Section" "pubs")}} +<div class="mdl-cell mdl-cell--12-col"> +<div class="card mdl-card mdl-shadow--4dp"> + <div class="mdl-card__title"> + <h1 class="mdl-card__title-text">Latest Publication</h1> + </div> +<div class="mdl-card__supporting-text"> + <h1 class="mdl-color-text--primary">{{ .Title }}</h1> + <h3 class="mdl-color-text--gray">{{ if .Params.journal }}{{ title .Params.journal }} {{end}}{{ .Date.Year }}</h3> + <h2 class="mdl-color-text--teal-500">{{ delimit .Params.authors ", " " and "}}</h2> + <p>{{ .Summary }}</p> +</div> + <div class="mdl-card__actions mdl-card--border"> + <a class="mdl-button mdl-button--colored mdl-js-button mdl-js-ripple-effect" href="/pubs">See all...</a> + </div> +</div></div> +{{end}} +</div> +<script src="/js/TweenMax.min.js"></script> +<script src="/js/neuro.js"></script> +{{ partial "footer.html" . }} diff --git a/layouts/partials/footer.html b/layouts/partials/footer.html new file mode 100644 index 0000000..e703bb0 --- /dev/null +++ b/layouts/partials/footer.html @@ -0,0 +1,7 @@ + </main> +</div> + +<script src="https://storage.googleapis.com/code.getmdl.io/{{.Site.Params.mdlversion}}/material.min.js"></script> + +</body> +</html> diff --git a/layouts/partials/footer_single.html b/layouts/partials/footer_single.html new file mode 100644 index 0000000..76cbc4a --- /dev/null +++ b/layouts/partials/footer_single.html @@ -0,0 +1,12 @@ + </main> +</div> + +<script src="https://storage.googleapis.com/code.getmdl.io/{{.Site.Params.mdlversion}}/material.min.js"></script> +<script src="/js/prism.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.6.0/katex.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.6.0/contrib/auto-render.min.js"></script> +<script> + renderMathInElement(document.body); +</script> +</body> +</html> diff --git a/layouts/partials/header.html b/layouts/partials/header.html new file mode 100644 index 0000000..73b5d47 --- /dev/null +++ b/layouts/partials/header.html @@ -0,0 +1,35 @@ +<!doctype html> +<html lang="en"> +<head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge"> + <meta name="description" content="Webpage of the Resendis Lab"> + <meta name="viewport" content="width=device-width, initial-scale=1.0, minimum-scale=1.0"> + {{ .Hugo.Generator }} + <title>{{ .Site.Title }} + + + + + + + + +
+
+
+
+ + +
+
diff --git a/layouts/partials/header_single.html b/layouts/partials/header_single.html new file mode 100644 index 0000000..f499162 --- /dev/null +++ b/layouts/partials/header_single.html @@ -0,0 +1,44 @@ + + + + + + + + {{ .Hugo.Generator }} + + + + + + {{ if .Params.hero }}{{end}} + + {{ .Site.Title }} + + + + + + + + + + +
+
+
+
+ + +
+
diff --git a/layouts/posts/single.html b/layouts/posts/single.html new file mode 100644 index 0000000..e6a5f6e --- /dev/null +++ b/layouts/posts/single.html @@ -0,0 +1,24 @@ +{{ partial "header_single.html" . }} + +
+ +arrow_back +Return to overview... +
+
+
+

{{ .Title }}

+
+
+

by {{ .Params.author }}


+ {{ .Content }} +
+
+ +{{ partial "footer_single.html" . }} diff --git a/layouts/section/about.html b/layouts/section/about.html new file mode 100644 index 0000000..6ef9f48 --- /dev/null +++ b/layouts/section/about.html @@ -0,0 +1,18 @@ +{{ partial "header.html" . }} + +
+{{ range .Data.Pages }} +
+
+
+

{{ .Title }}

+
+
+{{ .Content }} +
+{{ end }}
+{{ partial "footer.html" . }} diff --git a/layouts/section/events.html b/layouts/section/events.html new file mode 100644 index 0000000..1c958cc --- /dev/null +++ b/layouts/section/events.html @@ -0,0 +1,32 @@ +{{ partial "header.html" . }} +
+
+
+ {{ range $i, $p := .Data.Pages.GroupByDate "2006" }} + {{$p.Key}} + {{end}} +
+{{ range $i, $p := .Data.Pages.GroupByDate "2006" }} +
+
+{{ range $p.Pages }} +
+
+
+

{{ .Title }}

+

{{ .Date.Format "02-01-2006" }} to {{ dateFormat "02-01-2006" .Params.end }}

+
{{ .Content }} +
+
+map Map + link Webpage +
+
+
+{{end}} +
+{{end}} +
+{{ partial "footer.html" . }} diff --git a/layouts/section/members.html b/layouts/section/members.html new file mode 100644 index 0000000..26c91d8 --- /dev/null +++ b/layouts/section/members.html @@ -0,0 +1,52 @@ +{{ partial "header.html" . }} +
+
+
+ Current + Former +
+
+
+{{ range .Site.Data.members.current }} +
+
+
+
+ +

{{ .name }}

+

{{ .function }}

+
+
+
+mail outline +person pin + + +
+
+
+{{end}} +
+
+
+{{ range .Site.Data.members.former }} +
+
+
+
+ +

{{ .name }}

+

{{ .function }}

+
+
+
+mail outline +person pin + +
+
+
+{{end}} +
+
+{{ partial "footer.html" . }} diff --git a/layouts/section/positions.html b/layouts/section/positions.html new file mode 100644 index 0000000..d58544a --- /dev/null +++ b/layouts/section/positions.html @@ -0,0 +1,21 @@ +{{ partial "header.html" . }} +
+
+
+
+

Work with us

+
+
+
+{{ range where .Data.Pages "Section" "positions" }} +
+
+
+

{{ .Title }}

+
{{ .Content }} +
+
+
+{{end}} +
+{{ partial "footer.html" . }} diff --git a/layouts/section/posts.html b/layouts/section/posts.html new file mode 100644 index 0000000..c90ea38 --- /dev/null +++ b/layouts/section/posts.html @@ -0,0 +1,29 @@ +{{ partial "header.html" . }} +
+
+
+
+

Blog

+
+
+
+{{ range .Data.Pages }} +
+
+
+

{{ .Title }}

+
+
+

+ {{.Date.Format "02-01-2006"}} + {{.ReadingTime}}m read

+

by {{ .Params.author}}

+

{{ .Summary }}

+
+
+ Read more... +
+{{end}} +
+{{ partial "footer.html" . }} diff --git a/layouts/section/projects.html b/layouts/section/projects.html new file mode 100644 index 0000000..ac812e3 --- /dev/null +++ b/layouts/section/projects.html @@ -0,0 +1,22 @@ +{{ partial "header.html" . }} +
+
+
+
+

Projects

+
+
+
+{{ range where .Data.Pages "Section" "projects" }} +
+
+
+

{{ .Title }}

+

{{ delimit .Params.people ", " " and "}}

+
{{ .Content }} +
+
+
+{{end}} +
+{{ partial "footer.html" . }} diff --git a/layouts/section/pubs.html b/layouts/section/pubs.html new file mode 100644 index 0000000..2d64fdc --- /dev/null +++ b/layouts/section/pubs.html @@ -0,0 +1,45 @@ +{{ partial "header.html" . }} +
+
+
+ Articles + Books +
+
+
+{{ range where .Data.Pages "Params.what" "article"}} +
+
+
+

{{ .Title }}

+

{{ title .Params.journal }} {{ .Date.Year }}

+

{{ delimit .Params.authors ", " " and "}}

+

{{ if .Keywords }}Keywords: {{ delimit .Keywords ", " }}{{ end }}

+{{ if .Content }}{{ .Content }}{{ end }} +
+
+ Full Article + PubMed +
+
+
+{{end}} +
+
+
+{{ range where .Data.Pages "Params.what" "book"}} +
+
+
+

{{ .Title }}

+

{{ dateFormat "January 2006" .Date }}

+

{{ delimit .Params.authors ", " " and "}}

+

{{ if .Keywords }}Keywords: {{ delimit .Keywords ", " }}{{ end }}

+{{ if .Content }}{{ .Content }}{{ end }} +
+
+
+{{end}} +
+
+{{ partial "footer.html" . }} diff --git a/layouts/section/software.html b/layouts/section/software.html new file mode 100644 index 0000000..f35bd39 --- /dev/null +++ b/layouts/section/software.html @@ -0,0 +1,32 @@ +{{ partial "header.html" . }} +
+
+
+
+

Software

+
+
+
+{{ range where .Data.Pages "Section" "software" }} +
+
+
+{{ if .Params.logo }} + +{{ else }}

{{ .Title }}

{{ end }} +

{{ delimit .Params.authors ", " " and "}}

+
{{ .Content }} +
+
+code Source +{{ if .Params.docs }} +link Documentation +{{ end }} +
+
+
+{{end}} +
+{{ partial "footer.html" . }} diff --git a/members/index.html b/members/index.html deleted file mode 100644 index 762d735..0000000 --- a/members/index.html +++ /dev/null @@ -1,98 +0,0 @@ -Webpage of the Resendis Lab
Current -Former

Osbaldo Resendis Antonio

Principal Investigator

mail outline -person pin -

Aarón Vázquez Jiménez

Research Associate

mail outline -person pin -

Edgar Mixcoha

PostDoc

mail outline -person pin -

Juan José Oropeza Valdez

PostDoc

mail outline -person pin -

Crístian Julio Cesar Padrón Manrique

Ph.D. student

mail outline -person pin -

Jorge E. Arellano

Ph.D. student

mail outline -person pin -

Victor Sanz

Ph.D. student

mail outline -person pin -

Perla Alvarado

M.Sc. student

mail outline -person pin -

Yael Alex Colin

M.Sc. student

mail outline -person pin -

Mayavi Peña

Bsc. student

mail outline -person pin -

Gonzalo Aguilar

Logo and webpage designer

mail outline -person pin -

Francisco Giovani Mercado Valle

M.Sc student

mail outline -person pin -

Crístian Mendoza Ortiz

M.Sc. student

mail outline -person pin -

Laura Elena Hernández Juárez

PostDoc

mail outline -person pin -

David Girón Villalobos

M.Sc. student

mail outline -person pin -

Brenda Loaiza

PostDoc

mail outline -person pin -

Ugo Avila Ponce de León

Ph.D. student

mail outline -person pin -

Adrián Santiago Rivera

Bsc. student

mail outline -person pin -

Jean Paul Sanchez

M.Sc student

mail outline -person pin -

Daniel Neri Rosario

M.Sc student

mail outline -person pin -

Estrella Martinez

Ph.D. student

mail outline -person pin -

Thelma Escobedo

M.Sc student

mail outline -person pin -

Vanessa Salvador Rincón

M.Sc student

mail outline -person pin -

Diego A. Esquivel Hernández

PostDoc

mail outline -person pin -

Meztli Matadamas

Ph.D. student

mail outline -person pin -

Erick Muciño

Ph.D. student

mail outline -person pin -

Vanessa Salvador Rincón

M.Sc student

mail outline -person pin -

Felipe Muñoz Gonzalez

Ph.D. student

mail outline -person pin -

Carlos Ramírez Ramos

Visiting Researcher

mail outline -person pin -

Fernanda Díaz

Visiting Researcher

mail outline -person pin -

Luis Santiago Mille

Visiting student

mail outline -person pin -

Alfredo Rodriguez Arteaga

M.Sc student (visiting student)

mail outline -person pin -

Christian Diener

PostDoc

mail outline -person pin -

Akram Mendez

PostDoc

mail outline -person pin -

Ana Manzano

visiting student

mail outline -person pin -

Carlos Salgado Muñoz

Ph.D. student

mail outline -person pin -

Benjamin Cocom Chan

M.Sc. student

mail outline -person pin -

Ricardo Ramirez

Visiting Ph.D. student

mail outline -person pin -

Cristhian Avila

PreDoc

mail outline -person pin -

Erika Hernandez Patiño

M.Sc. student

mail outline -person pin -

Ixchetl Rojas Benito

B.Sc. student

mail outline -person pin -

Layra Cruz

B.Sc. student

mail outline -person pin -

Paola Hernandez

PosDoc

mail outline -person pin -

Lucía Lopez

B.Sc. student

mail outline -person pin -
\ No newline at end of file diff --git a/members/index.xml b/members/index.xml deleted file mode 100644 index 7b21fcb..0000000 --- a/members/index.xml +++ /dev/null @@ -1 +0,0 @@ -Members on Webpage of the Resendis Labhttps://resendislab.github.io/members/Recent content in Members on Webpage of the Resendis LabHugo -- gohugo.ioen-us<link>https://resendislab.github.io/members/dummy/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://resendislab.github.io/members/dummy/</guid><description>This is a dummy page. It&rsquo;s content will not be rendered.</description></item></channel></rss> \ No newline at end of file diff --git a/positions/index.html b/positions/index.html deleted file mode 100644 index ad6d63b..0000000 --- a/positions/index.html +++ /dev/null @@ -1,25 +0,0 @@ -<!doctype html><html lang=en><head><meta charset=utf-8><meta http-equiv=x-ua-compatible content="IE=edge"><meta name=description content="Webpage of the Resendis Lab"><meta name=viewport content="width=device-width,initial-scale=1,minimum-scale=1"><meta name=generator content="Hugo 0.79.0"><title>Webpage of the Resendis Lab

Work with us

Graduate student positions


We extend an invitation to undergrads and grad students with interest to -continue his/her academic education through a Master’s or Doctoral degree in one -of these academic programs: biological (http://pcbiol.posgrado.unam.mx), -biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical -(http://www.pdcb.unam.mx/) Sciences at UNAM. We encourage candidates with an -academic background in biology, biology physics, biophysics, genome sciences, -applied mathematics and computational sciences. The students incorporated to one -of these programs will be guided to develop a systems biology description in one -of these areas:

  1. Identification of metabolic alterations in cancer cell lines -or animal tissues
  2. Tissue-specific genome-scale metabolic reconstruction in -human system
  3. computational modeling of metabolism in microbiota and its -association with diabetes
  4. Development of computational methods to integrate and interpret high-throughput technologies such as RNAseq, proteome, and metabolome
  5. development of systems Biology schemes for the Study of Neuropsychiatric Diseases
  6. Development of computational analysis in Personalized (precision) medicine.

All projects are immersed on an interdisciplinary ambiance and promote an -integrative description of the statistical analysis of high-throughput data in -genomic sciences, development of computational/mathematical modeling of -biological networks; and the physiological knowledge, these points required -for understanding the principles that support the metabolic phenotype in human -diseases. For any further questions do not hesitate to contact us via [E-mail](mailto:oresendis [at] inmegen.gob.mx).

Postdoctoral position


We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to [oresendis [at] inmegen.gob.mx](mailto:oresendis [at] inmegen.gob.mx).

\ No newline at end of file diff --git a/positions/index.xml b/positions/index.xml deleted file mode 100644 index 853dde5..0000000 --- a/positions/index.xml +++ /dev/null @@ -1 +0,0 @@ -Positions on Webpage of the Resendis Labhttps://resendislab.github.io/positions/Recent content in Positions on Webpage of the Resendis LabHugo -- gohugo.ioen-usThu, 29 Jun 2017 12:53:05 -0500Graduate student positionshttps://resendislab.github.io/positions/students/Thu, 29 Jun 2017 12:53:05 -0500https://resendislab.github.io/positions/students/We extend an invitation to undergrads and grad students with interest to continue his/her academic education through a Master&rsquo;s or Doctoral degree in one of these academic programs: biological (http://pcbiol.posgrado.unam.mx), biochemical (http://www.mdcbq.posgrado.unam.mx/) or Biomedical (http://www.pdcb.unam.mx/) Sciences at UNAM. We encourage candidates with an academic background in biology, biology physics, biophysics, genome sciences, applied mathematics and computational sciences. The students incorporated to one of these programs will be guided to develop a systems biology description in one of these areas:Postdoctoral positionhttps://resendislab.github.io/positions/postdocs/Thu, 29 Jun 2017 12:52:56 -0500https://resendislab.github.io/positions/postdocs/We always are looking for researchers with interest to contribute in Systems Biology to understand human diseases. If you are interested in any of the general areas of research described before and would like to carry out post-doctoral or research stays in Systems Biology of the Microbiome, or develop systems paradigms in precision medicine, send your curriculum vitae, a brief statement of your research interests, and the names of 2-3 references to [oresendis [at] inmegen. \ No newline at end of file diff --git a/posts/index.html b/posts/index.html deleted file mode 100644 index b6d2c5d..0000000 --- a/posts/index.html +++ /dev/null @@ -1,10 +0,0 @@ -Webpage of the Resendis Lab

Blog

Hola!!!

06-12-2016 -1m read

by Christian

This is an example post

Please substitute all text below “+++” with your own!

This is my text now grrrr :)

Read more...
\ No newline at end of file diff --git a/posts/index.xml b/posts/index.xml deleted file mode 100644 index f2671b4..0000000 --- a/posts/index.xml +++ /dev/null @@ -1,3 +0,0 @@ -Posts on Webpage of the Resendis Labhttps://resendislab.github.io/posts/Recent content in Posts on Webpage of the Resendis LabHugo -- gohugo.ioen-usTue, 06 Dec 2016 09:19:57 -0600Hola!!!https://resendislab.github.io/posts/test/Tue, 06 Dec 2016 09:19:57 -0600https://resendislab.github.io/posts/test/<h2 id="this-is-an-example-post">This is an example post</h2> -<p>Please substitute all text below &ldquo;+++&rdquo; with your own!</p> -<p>This is my text now grrrr :)</p> \ No newline at end of file diff --git a/posts/test/index.html b/posts/test/index.html deleted file mode 100644 index a435d68..0000000 --- a/posts/test/index.html +++ /dev/null @@ -1,16 +0,0 @@ -Webpage of the Resendis Lab
arrow_back -Return to overview...

Hola!!!

by Christian


This is an example post

Please substitute all text below “+++” with your own!

This is my text now grrrr :)

$$ -\int_a^b e^{2\pi\cdot x} dx -$$

library(data.table)
-df <- data.table(x=1:10)
-
\ No newline at end of file diff --git a/projects/index.html b/projects/index.html deleted file mode 100644 index 2cb3fde..0000000 --- a/projects/index.html +++ /dev/null @@ -1,23 +0,0 @@ -Webpage of the Resendis Lab

Projects

Computational modeling of metabolic dynamics in the intratumoral microenvironment.

Jorge Enrique Arellano Villavicencio


Currently, oncology research has been focused on investigating cancer metabolism due to its -remarkable capacity to adapt to changes in its microenvironment, enabling it to efficiently respond -to gradients of oxygen and nutrients. In 3D spheroid cultures of MCF-7 cells, three distinct cell -subpopulations with varying metabolic characteristics have been identified, indicating that each -subpopulation fulfills specific activities within the tumor, promoting its progression and survival.

This project proposes the utilization of genome-scale metabolic reconstructions (GEMS) to model -the growth of each subpopulation. Additionally, by employing community modeling tools, it aims -to simulate spheroid growth and characterize the dynamics of metabolites among the three -communities. This research will pave the way for understanding the cooperativity between cells -and their response to different types of stress, such as hypoxia and reduced carbon sources.

Machine Learning-Based Exploration of Gut Microbiota's Impact on Type 2 Diabetes

Juan José Oropeza Valdez


Employing machine learning algorithms to investigate the role of the gut microbiota in the development and management of Type 2 diabetes (T2DM). By analyzing microbiome data from individuals with multiple diabetes treatments, my aim is to identify specific microbial compositions associated with the disease and develop predictive models that assess an individual’s risk of developing T2DM based on their microbiota profile. Also using a systems biology approach (MICOM) using the gut microbiota data to identify the metabolic changes in the community associated with T2DM.

On Type 2 diabetes, and their relation with the gut microbiome metabolism.

Laura Elena Hernández Juárez


Type 2 diabetes mellitus (T2D) is a widespread disease worldwide, the etiology may be associated with gut microbiota influenced by different diet patterns. Metformin is a T2D treatment, and it is known that can alter the gut microbiota composition, but a few is known about the relation between this composition and the physio-pathological variables, therefore in this project the microbiota composition is analyzed, as well as the computational modeling of metabolism in microbiota to infer the growth rates of selected bacteria and the metabolic interactions into gut microbiota on patients with T2D under metformin and linagliptin treatment

Metabolic changes in macrophage polarization through in silico approaches

Perla Itzel Alvarado Luis


Macrophages, crucial components of the innate immune system, have the remarkable ability to polarize and adopt various phenotypes in response to fluctuations in their microenvironment. Considered as “double-edged swords”, these cells serve a wide array of physiological roles; however, their dysfunction can contribute to the development of various diseases, such as cancer, tuberculosis, and atherosclerosis. Furthermore, macrophage polarization is critically supported by metabolic shifts, and there is an exciting potential for regulating macrophage functions in different contexts by manipulating their metabolism.

The objective of this project is to use a systems biology approach to analyze metabolomic data from polarized macrophages in order to unravel the underlying mechanisms of metabolic reprogramming during macrophage polarization. Through this study, we aim to identify the specific metabolic factors that contribute to the transition between different phenotypes and ultimately, their potential use in inducing repolarization towards a desired phenotype.

Research on Children Leukemia

Brenda Loaiza


Leukemia is the most common cancer in children worldwide, highest incidences and worse prognostics are for low and middle-income countries where less than 30% are cured. In Mexico 4,000 to 6,000 new cases are registered each year. Epidemiological studies have shown the contribution of environmental factors to the development of Leukemia, but also clinical factors such as late and imprecise diagnosis of the disease, limited access, and /or adherence to treatment, and tolerance and toxicity of antineoplastic drugs. To confront this problem, a National Strategic Program (Pronace) was proposed for the integral study of Children Leukemia. I’m responsible for Collecting all kinds of data from most laboratories of Mexico, through the organization of a DataBase in SQL language to be publicly available, and for integral study including artificial intelligence analysis for creating models that identify risk variables, better prognosis of patients, and a deep understanding of the etiology of Acute Lymphoblastic Leukemia in mexican children.

Alteration of gut microbiota induced by metformin and linagliptin/metformin treatment prevents type 2 diabetes.

Estrella Martínez


Lifestyle modifications, metformin and dipeptidyl peptidase type 4 inhibitors (DPP4i) reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The efficacy of such interventions may be enhanced by the gut microbiota (GM), which plays a role in mediating glucose-lowering effects through the increased abundance of short-chain fatty acid (SCFA)-producing bacteria. We determined the effect of combined linagliptin+metformin vs metformin monotherapy on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes without a previous treatment and compared it between metformin monotherapy and the combination of linagliptin+metformin. A double-blind, randomized parallel clinical trial was conducted in 167 Mexican adults with prediabetes for 12 months. We analyzed the effects of the two treatments on GM using the machine learning algorithm (random forest) and mediation analysis with two structural equation models (SEM) to determine the relationship between body composition, IS, Pβf, and bacterial genera. These treatments modify GM composition, by increasing the abundance of SCFA-producing bacteria [Metformin (Fusicatenibacter and Blautia) and Linagliptin/metformin (Roseburia, Bifidobacterium and [Eubacterium] hallii group)].

Computational modeling of the gut microbiota metabolism in COVID-19 patients

David Girón Villalobos


I study the gut microbiota to find how microbes participate in the development of COVID-19. To do that, I use MICOM, a community metabolic computational model, that can predict metabolic interactions within the microbiota and the host.

Ecological study on gut microbiota

Crístian Mendoza Ortiz


We analyze the dynamics of the metabolism of the gut microbiota in longitudinal databases through a hybrid model between generalized Lotka-Volterra and flux balance analysis (FBA)

Gut microbiota and type 2 diabetes

Daniel Neri


A direct link between the gut microbiota (GM) and the progression of type 2 diabetes mellitus (T2D) in individuals has been described. We propose using supervised Machine Learning (ML) methods to identify predictive taxa for patients with prediabetes (pre-T2D) and T2D.

Manifold learning approaches for high dimensional biological data

Crístian Padrón


Modern high–throughput biological data yield detailed characterizations of the genomic, transcriptomic, and proteomic states of samples. This kind of data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discovery reliability. Data sparsity makes it difficult to obtain a well-data structure and distorts the distribution of variables. Currently, there is a raised need to develop new algorithms with improved capacities to reduce noise and recover missing information. For that reason, we are developing new machine learning methods to better understand noisy and high-dimensional biological data. For example, microbiome and scRNA-seq data imputation. Also, we are developing multi-omic data integration methods to find key variables involved in complex biological systems which is in itself difficult to handle and the problem of the high-dimensionality is accentuated in multi-omics data.

In silico study of metabolic reprogramming during epithelial-mesenchymal transition

Meztli Matadamas


An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes.

The project combine high-throughput data to understand metabolic changes before and after EMT in lung cancer cell lines. In particular to find main fluxes used during EMT. To that extent we employ methods from bioinformatics and Systems Biology. Our goal is to found specific targets which could stop or reverse EMT in cancer cells.

Microbiome metabolism and diabetes

Jean Paul Sanchez


Alterations in the microbiome has been associated with diabetes progression.

Immunology and cancer: Boolean Modeling of regulatory networks

Ugo Avila Ponce de Leon


Macrophages are cells of the innate immune system endowed with the capacity to orchestrate the immune response in human tissues. Due to their plasticity biological property, they polarize to several subtypes based on the actions of the tumor microenvironment. These cells have plasticity, because once they are committed to a subtype fate, they can polarize to another subtype by simply modifying the microenvironment. We integrated experimental data for the construction of a network that will explain the plasticity and the importance of the microenvironment in shaping the polarization of macrophages. The mathematical model was used to describe the genetic control points of macrophage polarization and plasticity, and it can function as groundwork and guidance for an immunotherapeutic approach to modulate the proliferation of cancer cells.

Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat model

Erika Hernandez


Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. -Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions. -This data would not only helped us identify reactions important to maintain the cancerous state but also help us survey the regulatory mechanism this is achieved.

Metabolic heterogeneity in cancer and its applications in Personalized Medicine

Christian Diener


Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation.

In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism. In particular we are asking the question whether distinct genomic aberrations such as mutations or changes in transcription can be related to respective changes in cellular metabolism. To that extent we employ methods from Systems Biology as well as from Data Science and Machine Learning in order to connect genetic information to specific metabolic phenotypes.

Our aim is to use the knowledge we gain in the context of personalized medicine, particularly the use of genotyping for the prediction of the best course of treatment for a specific patient.

Systems biology and bioinformatics of single cell RNAseq data.

Thelma Escobedo


Research in personalized therapy has taken relevance because treatment failures due to intratumoral heterogenety which refers to celular diversity or subpopulations -forming within the tumor. Currently, given complex molecular processes of cancer there has been greater use of omic technologies and computational analysis. With the purpuse to -contribute in this line, we have opened a new line of research to describe the progress of expression profiles in tumor cell lines through bioinformatic analysis of single cell -RNAseq data. Likewise, this work will contribute to infer the principles that guide the population heterogeneity mechanisms for the design of new optimized strategies for the -treatment of cancer.

The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids development

Erick Muciño


Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. Hence, generate models that allow the integration of miRNAs regulation in cancer metabolism will allow us to analyze in a systematically and systemic manner the relations and potential mechanism underlying between miRNAs and central pathways of metabolism.

It is imperative to know the mechanism governing the pathogenesis and progression of cancer to design therapies with greater impact on diagnosis and disease progression. So, this project aims to suggest mechanism that trigger the metabolic change in a breast cancer cell line (MCF-7), integrating miRNAs network. To this end, we propose to develop a scheme of systems biology, which allow us to make an integrative analysis of regulatory networks of miRNAs and metabolism in MCF-7. This approach will allow us to develop models capable of identifying potential therapeutic targets with greater impact, biomarkers that allow early detection of cancer and penetrate in global mechanism in clinical cases.

\ No newline at end of file diff --git a/projects/index.xml b/projects/index.xml deleted file mode 100644 index 9dce3b3..0000000 --- a/projects/index.xml +++ /dev/null @@ -1,3 +0,0 @@ -Projects on Webpage of the Resendis Labhttps://resendislab.github.io/projects/Recent content in Projects on Webpage of the Resendis LabHugo -- gohugo.ioen-usTue, 27 Jun 2023 00:00:00 +0000Computational modeling of metabolic dynamics in the intratumoral microenvironment.https://resendislab.github.io/projects/gem_spheroids/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/projects/gem_spheroids/Currently, oncology research has been focused on investigating cancer metabolism due to its remarkable capacity to adapt to changes in its microenvironment, enabling it to efficiently respond to gradients of oxygen and nutrients. In 3D spheroid cultures of MCF-7 cells, three distinct cell subpopulations with varying metabolic characteristics have been identified, indicating that each subpopulation fulfills specific activities within the tumor, promoting its progression and survival. -This project proposes the utilization of genome-scale metabolic reconstructions (GEMS) to model the growth of each subpopulation.Machine Learning-Based Exploration of Gut Microbiota's Impact on Type 2 Diabeteshttps://resendislab.github.io/projects/diabetes_micom/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/projects/diabetes_micom/Employing machine learning algorithms to investigate the role of the gut microbiota in the development and management of Type 2 diabetes (T2DM). By analyzing microbiome data from individuals with multiple diabetes treatments, my aim is to identify specific microbial compositions associated with the disease and develop predictive models that assess an individual&rsquo;s risk of developing T2DM based on their microbiota profile. Also using a systems biology approach (MICOM) using the gut microbiota data to identify the metabolic changes in the community associated with T2DM.On Type 2 diabetes, and their relation with the gut microbiome metabolism.https://resendislab.github.io/projects/diabetes_metformin/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/projects/diabetes_metformin/Type 2 diabetes mellitus (T2D) is a widespread disease worldwide, the etiology may be associated with gut microbiota influenced by different diet patterns. Metformin is a T2D treatment, and it is known that can alter the gut microbiota composition, but a few is known about the relation between this composition and the physio-pathological variables, therefore in this project the microbiota composition is analyzed, as well as the computational modeling of metabolism in microbiota to infer the growth rates of selected bacteria and the metabolic interactions into gut microbiota on patients with T2D under metformin and linagliptin treatmentMetabolic changes in macrophage polarization through in silico approacheshttps://resendislab.github.io/projects/metabolic_macrophages/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/projects/metabolic_macrophages/Macrophages, crucial components of the innate immune system, have the remarkable ability to polarize and adopt various phenotypes in response to fluctuations in their microenvironment. Considered as &ldquo;double-edged swords”, these cells serve a wide array of physiological roles; however, their dysfunction can contribute to the development of various diseases, such as cancer, tuberculosis, and atherosclerosis. Furthermore, macrophage polarization is critically supported by metabolic shifts, and there is an exciting potential for regulating macrophage functions in different contexts by manipulating their metabolism. Research on Children Leukemiahttps://resendislab.github.io/projects/leukemia/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/leukemia/Leukemia is the most common cancer in children worldwide, highest incidences and worse prognostics are for low and middle-income countries where less than 30% are cured. In Mexico 4,000 to 6,000 new cases are registered each year. Epidemiological studies have shown the contribution of environmental factors to the development of Leukemia, but also clinical factors such as late and imprecise diagnosis of the disease, limited access, and /or adherence to treatment, and tolerance and toxicity of antineoplastic drugs.Alteration of gut microbiota induced by metformin and linagliptin/metformin treatment prevents type 2 diabetes.https://resendislab.github.io/projects/t2d/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/t2d/Lifestyle modifications, metformin and dipeptidyl peptidase type 4 inhibitors (DPP4i) reduce the incidence of type 2 diabetes (T2D) in people with prediabetes. The efficacy of such interventions may be enhanced by the gut microbiota (GM), which plays a role in mediating glucose-lowering effects through the increased abundance of short-chain fatty acid (SCFA)-producing bacteria. We determined the effect of combined linagliptin+metformin vs metformin monotherapy on GM composition and its relationship to insulin sensitivity (IS) and pancreatic β-cell function (Pβf) in patients with prediabetes without a previous treatment and compared it between metformin monotherapy and the combination of linagliptin+metformin.Computational modeling of the gut microbiota metabolism in COVID-19 patientshttps://resendislab.github.io/projects/covidmicom/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/covidmicom/I study the gut microbiota to find how microbes participate in the development of COVID-19. To do that, I use MICOM, a community metabolic computational model, that can predict metabolic interactions within the microbiota and the host.Ecological study on gut microbiotahttps://resendislab.github.io/projects/ecologicalgut/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/ecologicalgut/We analyze the dynamics of the metabolism of the gut microbiota in longitudinal databases through a hybrid model between generalized Lotka-Volterra and flux balance analysis (FBA)Gut microbiota and type 2 diabeteshttps://resendislab.github.io/projects/t2dmicom/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/t2dmicom/A direct link between the gut microbiota (GM) and the progression of type 2 diabetes mellitus (T2D) in individuals has been described. We propose using supervised Machine Learning (ML) methods to identify predictive taxa for patients with prediabetes (pre-T2D) and T2D.Manifold learning approaches for high dimensional biological datahttps://resendislab.github.io/projects/scphenix/Thu, 15 Dec 2022 00:00:00 +0000https://resendislab.github.io/projects/scphenix/Modern high–throughput biological data yield detailed characterizations of the genomic, transcriptomic, and proteomic states of samples. This kind of data suffers from technical noise (reflected as excess of zeros in the count matrix) and the curse of dimensionality. This complicates downstream data analysis and compromises the scientific discovery reliability. Data sparsity makes it difficult to obtain a well-data structure and distorts the distribution of variables. Currently, there is a raised need to develop new algorithms with improved capacities to reduce noise and recover missing information.In silico study of metabolic reprogramming during epithelial-mesenchymal transitionhttps://resendislab.github.io/projects/emt/Fri, 06 Dec 2019 00:00:00 +0000https://resendislab.github.io/projects/emt/An epithelial-mesenchymal transition (EMT) is a biologic process that allows a polarized epithelial cell, which normally interacts with basement membrane via its basal surface, to undergo multiple biochemical changes that enable it to assume a mesenchymal cell phenotype, which includes enhanced migratory capacity, invasiveness, elevated resistance to apoptosis, and greatly increased production of ECM components. EMT induces invasive properties in epithelial tumors and promotes metastasis. Although EMT-mediated cellular and molecular changes are well understood, very little is known about EMT-induced metabolic changes.Microbiome metabolism and diabeteshttps://resendislab.github.io/projects/diabe_metabolism/Fri, 06 Dec 2019 00:00:00 +0000https://resendislab.github.io/projects/diabe_metabolism/Alterations in the microbiome has been associated with diabetes progression.Immunology and cancer: Boolean Modeling of regulatory networkshttps://resendislab.github.io/projects/inmunology_cancer/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/inmunology_cancer/Macrophages are cells of the innate immune system endowed with the capacity to orchestrate the immune response in human tissues. Due to their plasticity biological property, they polarize to several subtypes based on the actions of the tumor microenvironment. These cells have plasticity, because once they are committed to a subtype fate, they can polarize to another subtype by simply modifying the microenvironment. We integrated experimental data for the construction of a network that will explain the plasticity and the importance of the microenvironment in shaping the polarization of macrophages.Integrating transcriptomic and metabolomic to understand hepatocellular carcinoma in a rat modelhttps://resendislab.github.io/projects/hepato/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/hepato/Hepatocellular carcinoma (HCC) is now the third leading cause of cancer deaths worldwide, with over 500,000 people affected. It occurs predominantly in patients with underlying chronic liver disease and cirrhosis. Despite this, knowledge about the metabolic states of this disease is limited. Using a rat model that recreates some of the most important characteristics of HCC, including cirrhosis, we aim to understand the metabolic state when compared to healthy liver. To this end we will integrate transcriptomic and metabolic data in a systems biology framework that point us changes in reactions.Metabolic heterogeneity in cancer and its applications in Personalized Medicinehttps://resendislab.github.io/projects/prolif/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/prolif/Cancer is a very heterogeneous disease and tumors can differ greatly across and within different cancer types. Consequently, cancer is not a single disease but thousands. One property shared by all cancers is the ability to sustain chronic uncontrolled proliferation which raises the question how different cancers alter their metabolism in order to achieve consistent proliferation. -In this project we combine large-scale genomic data from DNA and RNA sequencing as well as proteomics and metabolomics to understand the connection between variations in the genotype and cancer metabolism.Systems biology and bioinformatics of single cell RNAseq data.https://resendislab.github.io/projects/singlecell_thelma/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/singlecell_thelma/Research in personalized therapy has taken relevance because treatment failures due to intratumoral heterogenety which refers to celular diversity or subpopulations forming within the tumor. Currently, given complex molecular processes of cancer there has been greater use of omic technologies and computational analysis. With the purpuse to contribute in this line, we have opened a new line of research to describe the progress of expression profiles in tumor cell lines through bioinformatic analysis of single cell RNAseq data.The impact of the microRNAs in the metabolic reprogramming of the MCF-7 cells during the spheroids developmenthttps://resendislab.github.io/projects/spheroids/Thu, 06 Dec 2018 00:00:00 +0000https://resendislab.github.io/projects/spheroids/Alterations in the metabolism are a common property in cancer cells, so that, many efforts have been directed to develop models to understand the mechanism by which cancer cells behave differently compared to normal tissues. In recent years, it has been reported that microRNAs (miRNAs) are involved in the regulation of all biological process, and there are evidences that shown its dysregulation play an important role in the development and progression of cancer. \ No newline at end of file diff --git a/pubmed_to_hugo.py b/pubmed_to_hugo.py new file mode 100644 index 0000000..b53afe7 --- /dev/null +++ b/pubmed_to_hugo.py @@ -0,0 +1,52 @@ +#!/usr/bin/env python3 + +MD = """+++ +authors = [{{authors}}] +title = "{{title}}" +journal = "{{journal}}" +what = "article" +doi = "{{doi}}" +pubmed = "{{pmid}}" +date = "{{date}}" +keywords = [{{keywords}}] ++++ + +{{abstract}}""" + +if __name__ == "__main__": + import xml.etree.ElementTree as ET + from sys import argv + from jinja2 import Environment + from os.path import exists + from datetime import date as da + + template = Environment().from_string(MD) + + root = ET.parse(argv[1]).getroot() + for child in root: + article = {} + article["pmid"] = child.find(".//PMID").text + article["title"] = child.find(".//ArticleTitle").text + if child.find(".//AbstractText") is not None: + article["abstract"] = child.find(".//AbstractText").text + else: + article["abstract"] = "" + article["authors"] = ", ".join([ + '"' + x.find("ForeName").text + " " + x.find("LastName").text + '"' + for x in child.findall(".//Author") + ]) + article["keywords"] = ", ".join([ + '"' + x.text + '"' for x in child.findall(".//Keyword") + ]) + article["doi"] = child.find( + ".//ArticleId[@IdType='doi']").text + article["journal"] = child.find(".//Journal/Title").text + date = child.find(".//PubMedPubDate[@PubStatus='pubmed']") + article["date"] = da(int(date.find("Year").text), + int(date.find("Month").text), + int(date.find("Day").text)).isoformat() + print(article["keywords"]) + filename = "content/pubs/PM{}.md".format(article["pmid"]) + if not exists(filename): + with open(filename, "w") as f: + f.write(template.render(**article)) diff --git a/pubs/index.html b/pubs/index.html deleted file mode 100644 index b17586a..0000000 --- a/pubs/index.html +++ /dev/null @@ -1,124 +0,0 @@ -Webpage of the Resendis Lab
Articles -Books

mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota data

Bioinformatics 2023

Cristian Padron-Manrique, Aaron Vazquez-Jimenez, Diego A Esquivel-Hernandez, Yoscelina Estrella Martinez-Lopez, Daniel Neri-Rosario, Jean Paul Sanchez, David Giron-Villalobos and Osbaldo Resendis-Antonio

Motivation -Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. -Results -We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails. -Availability and implementation -The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

Full Article -PubMed

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Front. Endocrinol. 2023

Daniel Neri-Rosario, Yoscelina E. Martínez-López, Diego A. Esquivel-Hernández, Jean Paul Sánchez-Castañeda, Crístian Padron-Manrique, Aarón Vázquez-Jiménez, David Giron-Villalobos and Osbaldo Resendis-Antonio

Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual’s gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods: To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results: We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion: These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

Full Article -PubMed

Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approach

Front Immunol 2023

Ugo Avila-Ponce de León, Aarón Vázquez-Jiménez, Pablo Padilla-Longoria and Osbaldo Resendis-Antonio

The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment. To quantify this process, we used a previous regulatory network reconstructed by our group and transformed Boolean Network attractors of macrophage polarization to an ODE scheme, it enables us to quantify the activation of their genes in a continuous fashion. The transformation was developed using the interaction rules with a fuzzy logic approach. By implementing this approach, we analyzed different aspects that cannot be visualized in the Boolean setting. For example, this approach allows us to explore the dynamic behavior at different concentrations of cytokines and transcription factors in the microenvironment. One important aspect to assess is the evaluation of the transitions between phenotypes, some of them characterized by an abrupt or a gradual transition depending on specific concentrations of exogenous cytokines in the tumor microenvironment. For instance, IL-10 can induce a hybrid state that transits between an M2c and an M2b macrophage. Interferon- γ can induce a hybrid between M1 and M1a macrophage. We further demonstrated the plasticity of macrophages based on a combination of cytokines and the existence of hybrid phenotypes or partial polarization. This mathematical model allows us to unravel the patterns of macrophage differentiation based on the competition of expression of transcriptional factors. Finally, we survey how macrophages may respond to a continuously changing immunological response in a tumor microenvironment.

Full Article -PubMed

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort

Front. Endocrinol 2023

Diego A. Esquivel-Hernández, Yoscelina Estrella Martínez-López, Jean Paul Sánchez-Castañeda, Daniel Neri-Rosario, Crístian Padrón-Manrique, David Giron-Villalobos, Crístian Mendoza-Ortíz and Osbaldo Resendis-Antonio

Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host.

Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).

Results: By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter.

Discussion: Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

Full Article -PubMed

Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencing

Front. Cell Dev. Biol. 2023

Ulises Torres-Flores, Fernanda Díaz-Espinosa, Taydé López-Santaella, Rosa Rebollar-Vega, Aarón Vázquez-Jiménez, Ian J. Taylor, Rosario Ortiz-Hernández, Olga M. Echeverría, Gerardo H. Vázquez-Nin, María Concepción Gutierrez-Ruiz, Inti Alberto De la Rosa-Velázquez, Osbaldo Resendis-Antonio and Abrahan Hernández-Hernandez

CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm. In the early stages of spermatogenesis, transcriptional alterations are mild. As germ cells go through the specialization stage or spermiogenesis, transcriptional profiles become more altered. We found morphology defects in spermatids that support the alterations in their transcriptional profiles. Altogether, our study sheds light on the contribution of CTCF to the phenotype of male gametes and provides a fundamental description of its role at different stages of spermiogenesis.

Full Article -PubMed

A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiota

Archives of Medical Research 2023

Rocio Guizar-Heredia, Lilia G. Noriega, Ana Leonor Rivera, Osbaldo Resendis-Antonio, Martha Guevara-Cruz, Nimbe Torres and Armando R. Tovar

A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others. In recent years, continuous glucose monitoring has made it possible to establish predictions on the effect of different dietary foods on PPGRs through machine learning methods, which use algorithms that integrate genetic, biochemical, physiological and gut microbiota variables for identifying associations between them and clinical variables with aim of personalize dietary recommendations. This has allowed to improve the concept of personalized nutrition, since it is now possible to recommend through these predictions specific dietary foods to prevent elevated PPGRs that are highly variable among individuals. Additional components that can enrich the predictive algorithms are findings of nutrigenomics, nutrigenetics and metabolomics. Thus, this review aims to summarize the evidence of the components that integrate personalized nutrition focused on the prevention of PPGRs, and to show the future of personalized nutrition by laying the groundwork for the development of individualized dietary management and its impact on the improvement of metabolic diseases.

Full Article -PubMed

Chronic Comorbidities in Middle Aged Patients Contribute to Ineffective Emergency Hematopoiesis in Covid-19 Fatal Outcomes

Archives of Medical Research 2023

Rubí Romo-Rodríguez, Karla Gutiérrez-de Anda, Jebea A López-Blanco, Gabriela Zamora-Herrera, Paulina Cortés-Hernández, Gerardo Santos-López, Luis Márquez-Domínguez, Armando Vilchis-Ordoñez, Dalia Ramírez-Ramírez, Juan Carlos Balandrán, Israel Parra-Ortega, Osbaldo Resendis-Antonio, Lenin Domínguez-Ramírez, Constantino López-Macías, Laura C Bonifaz, Lourdes A Arriaga-Pizano, Arturo Cérbulo-Vázquez, Eduardo Ferat-Osorio, Antonieta Chavez-González, Samuel Treviño, Eduardo Brambila, Miguel Ángel Ramos-Sánchez, Ricardo Toledo-Tapia, Fabiola Domínguez, Jorge Bayrán-Flores, Alejandro Cruz-Oseguera, Julio Roberto Reyes-Leyva, Socorro Méndez-Martínez, Jorge Ayón-Aguilar, Aurora Treviño-García, Eduardo Monjaraz and Rosana Pelayo

Background and Aims -Mexico is among the countries with the highest estimated excess mortality rates due to the COVID–19 pandemic, with more than half of reported deaths occurring in adults younger than 65 years old. Although this behavior is presumably influenced by the young demographics and the high prevalence of metabolic diseases, the underlying mechanisms have not been determined. -Methods -The age–stratified case fatality rate (CFR) was estimated in a prospective cohort with 245 hospitalized COVID–19 cases, followed through time, for the period October 2020–September 2021. Cellular and inflammatory parameters were exhaustively investigated in blood samples by laboratory test, multiparametric flow cytometry and multiplex immunoassays. -Results -The CFR was 35.51%, with 55.2% of deaths recorded in middle–aged adults. On admission, hematological cell differentiation, physiological stress and inflammation parameters, showed distinctive profiles of potential prognostic value in patients under 65 at 7 days follow–up. Pre–existing metabolic conditions were identified as risk factors of poor outcomes. Chronic kidney disease (CKD), as single comorbidity or in combination with diabetes, had the highest risk for COVID–19 fatality. Of note, fatal outcomes in middle–aged patients were marked from admission by an inflammatory landscape and emergency myeloid hematopoiesis at the expense of functional lymphoid innate cells for antiviral immunosurveillance, including NK and dendritic cell subsets. -Conclusions -Comorbidities increased the development of imbalanced myeloid phenotype, rendering middle–aged individuals unable to effectively control SARS–CoV–2. A predictive signature of high–risk outcomes at day 7 of disease evolution as a tool for their early stratification in vulnerable populations is proposed. -Keywords: Chronic comorbidities; Emergency hematopoiesis; COVID–19; Middle adulthood; Inflammation; Chronic kidney disease

Full Article -PubMed

Macrophage Boolean networks in the time of SARS-CoV-2

Frontiers in Immunology 2022

Ugo Avila-Ponce de León and Osbaldo Resendis-Antonio

The post-pandemic period of the current coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has lasted longer than expected despite the huge impact of the world-wide vaccination campaign in the past years. Since the pandemic began, endless mathematical models have been published to describe the viral outbreak at a population level. However, the molecular mechanism that drives the pathogenesis of the virus in the human microenvironment has been scarce. The existing mechanistic models deal to answer how SARS-CoV-2 invades the lung microenvironment and shapes the composition of the immune system in favor of virus duplication. From the clinical point of view, almost all patients that develop severe COVID-19 lead from a life-threatening acute respiratory distress syndrome (ARDS), which is associated with a hyper-inflammatory microenvironment and injury in the alveolar and lung epithelium. One of the cells associated with this syndrome is an uncontrolled hyper-activated macrophage-associated syndrome (MAS), which promotes a systemic inflammatory response that exacerbates the progress of the cytokine storm in the host. Furthermore, single-cell high-throughput technologies applied in COVID-19 infections have shown that macrophages and monocytes are more abundant over other immune cells in bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC). In addition, the cytokines produced by monocytes and macrophages modulate the response of the immune system in patients with COVID-19. For instance, there is evidence supporting that monocytes in severe patients have a lower expression of the human leukocyte antigen HLA-DRB1, which represses the activation of the immune response through the low production of foreign peptides. Besides, monocytes have a lower expression of interferon-stimulated genes in severe COVID-19 patients, resulting in the delay of the interferon response against SARS-CoV-2. Furthermore, macrophages in severe COVID-19 patients are associated with overexpression of pro-inflammatory genes when compared with moderate COVID-19 patients. Altogether, these and other findings highlight the remarkable role that monocytes and macrophage polarization have in the progression of the disease.

Full Article -PubMed

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era

Gut Microbes 2022

Yoscelina Estrella Martínez-López, Diego A Esquivel-Hernández, Jean Paul Sánchez-Castañeda, Daniel Neri-Rosario, Rodolfo Guardado-Mendoza and Osbaldo Resendis-Antonio

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

Full Article -PubMed

prePrint: A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohort

Research Square 2022

Diego Armando Esquivel-Hernández, Yoscelina Estrella Martínez-López, Jean Paul Sánchez-Castañeda, Daniel Neri-Rosario, Cristian Padrón-Manrique, David Girón-Villalobos, Cristian Mendoza-Ortíz and Osbaldo Resendis-Antonio

Background

The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. -Results

Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. -Conclusions

Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

Full Article -PubMed

Comparative subcellular localization of NRF2 and KEAP1 during the hepatocellular carcinoma development in vivo

Biochimica Et Biophysica Acta (BBA)-Molecular Cell Research 2022

Dafne Guerrero-Escalera, Brisa Rodope Alarcón-Sánchez, Jaime Arellanes-Robledo, Armando Cruz-Rangel, Luis del Pozo-Yauner, Victoria Chagoya de Sánchez, Osbaldo Resendis-Antonio, Saul Villa-Treviño, Julia Esperanza Torres-Mena and Julio Isael Pérez-Carreón

The activation of Nuclear Factor, Erythroid 2 Like 2 – Kelch Like ECH Associated Protein 1 (NRF2-KEAP1) signaling pathway plays a critical dual role by either protecting or promoting the carcinogenesis process. However, its activation or nuclear translocation during hepatocellular carcinoma (HCC) progression has not been addressed yet. This study characterizes the subcellular localization of both NRF2 and KEAP1 during diethylnitrosamine-induced hepatocarcinogenesis in the rat. NRF2-KEAP1 pathway was continuously activated along with the increased expression of its target genes, namely Nqo1, Hmox1, Gclc, and Ptgr1. Similarly, the nuclear translocation of NRF2, MAF, and KEAP1 increased in HCC cells from weeks 12 to 22 during HCC progression. Likewise, colocalization of NRF2 with KEAP1 was higher in the cell nuclei of HCC neoplastic nodules than in surrounding cells. Moreover, immunofluorescence analyses revealed that the interaction of KEAP1 with filamentous Actin was disrupted in HCC cells. This disruption may be contributing to the release and nuclear translocation of NRF2 since the cortical actin cytoskeleton serves as anchoring of KEAP1. In conclusion, this evidence indicates that NRF2 is progressively activated and promotes the progression of experimental HCC.

Full Article -PubMed

Physiological Network Is Disrupted in Severe COVID-19

Frontiers in Physiology 2022

Antonio Barajas-Martínez, Roopa Mehta, Elizabeth Ibarra-Coronado, Ruben Fossion, Vania J Martínez Garcés, Monserrat Ramírez Arellano, Ibar A González Alvarez, Yamilet Viana Moncada Bautista, Omar Y Bello-Chavolla, Natalia Ramírez Pedraza, Bethsabel Rodríguez Encinas, Carolina Isabel Pérez Carrión, María Isabel Jasso Ávila, Jorge Carlos Valladares-García, Pablo Esteban Vanegas-Cedillo, Diana Hernández Juárez, Arsenio Vargas-Vázquez, Neftali Eduardo Antonio-Villa, Paloma Almeda-Valdes, Osbaldo Resendis-Antonio, Marcia Hiriart, Alejandro Frank, Carlos A Aguilar-Salinas and Ana Leonor Rivera

The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism’s homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else. The goal of this study is to determine the differences in the relationships that link physiological variables that characterize the elderly and men, and those that characterize fatal outcomes in young men. To accomplish this, we examined a database of patients with moderate to severe COVID-19 (471 men and 277 women) registered at the “Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán” in March 2020. The sample was stratified by outcome, age, and sex. Physiological networks were built using 67 physiological variables (vital signs, anthropometric, hematic, biochemical, and tomographic variables) recorded upon hospital admission. Individual variables and system behavior were examined by descriptive statistics, differences between groups, principal component analysis, and network analysis. We show how topological network properties, particularly clustering coefficient, become disrupted in disease. Finally, anthropometric, metabolic, inflammatory, and pulmonary cluster interaction characterize the deceased young male group.

Full Article -PubMed

Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seq

Mathematics 2021

Aarón Vázquez-Jiménez and Osbaldo Resendis-Antonio

The single-cell RNA-seq allows exploring the transcriptome for one cell at a time. By doing so, cellular regulation is pictured. One limitation is the dropout events phenomenon, where a gene is observed at a low or moderate expression level in one cell but not detected in another. Dropouts obscure legitimate biological heterogeneity leading to the description of a small fraction of the meaningful relations. We used a stochastic approach to model the Reverse Transcription Polymerase Chain Reaction (RT-PCR) kinetic, in which we contemplated the temperature profile, RT-PCR duration, and reaction rates. By studying the underlying biochemical processes of RT-PCR, using a computational and analytical framework, we show a minimal amount of RNA to avoid dropout events. We further use this fact to characterize the limits in the dispersion reduction. Dispersion asymptotically decreases as the RNA initial value increases. Despite always being a basal dispersion, their decreasing speed is modulated mainly by the degradation rates, particularly for the RNA. We concluded that the critical step into the RT-PCR is the RT phase due to the fragile nature of the RNA. We propose that limiting RNA degradation might ensure that the portraited transcriptional landscape is unbiased by technical error.

Full Article -PubMed

On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markers

Frontiers in Immunology 2021

Aarón Vázquez-Jiménez, Ugo Avila-Ponce De León, Meztli Matadamas-Guzman, Erick Andrés Muciño-Olmos, Estrella Martínez-López, Thelma Escobedo-Tapia and Osbaldo Resendis-Antonio

COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses. On the other hand, understanding how the immune system orchestrates its responses in this spectrum of disease severities is a fundamental issue required to design new and optimized therapeutic strategies. In this work, using single-cell RNAseq of bronchoalveolar lavage fluid of nine patients with COVID-19 and three healthy controls, we contribute to both aspects. First, we presented computational supervised machine-learning models with high accuracy in classifying the disease severity (moderate and severe) in patients with COVID-19 starting from single-cell data from bronchoalveolar lavage fluid. Second, we identified regulatory mechanisms from the heterogeneous cell populations in the lungs microenvironment that correlated with different clinical responses. Given the results, patients with moderate COVID-19 symptoms showed an activation/inactivation profile for their analyzed cells leading to a sequential and innocuous immune response. In comparison, severe patients might be promoting cytotoxic and pro-inflammatory responses in a systemic fashion involving epithelial and immune cells without the possibility to develop viral clearance and immune memory. Consequently, we present an in-depth landscape analysis of how transcriptional factors and pathways from these heterogeneous populations can regulate their expression to promote or restrain an effective immune response directly linked to the patients prognosis.

Full Article -PubMed

Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysis

Frontiers in Immunology 2021

Ugo Avila-Ponce De León, Aarón Vázquez-Jiménez, Meztli Matadamas-Guzman, Rosana Pelayo and Osbaldo Resendis-Antonio

The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells. Here, we used a Boolean modeling approach to understand how the tumor microenvironment shapes macrophage behavior to enhance pro-tumoral functions. Our network reconstruction integrates experimental data and public information that let us study the polarization from monocytes to M1, M2a, M2b, M2c, and M2d subphenotypes. To analyze the dynamics of our model, we modeled macrophage polarization in different conditions and perturbations. Notably, our study identified new hybrid cell populations, undescribed before. Based on the in vivo macrophage behavior, we explained the hybrid macrophages’ role in the tumor microenvironment. The in silico model allowed us to postulate transcriptional factors that maintain the balance between macrophages with anti- and pro-tumoral functions. In our pursuit to maintain the balance of macrophage phenotypes to eliminate malignant tumor cells, we emulated a theoretical genetically modified macrophage by modifying the activation of NFκB and a loss of function in HIF1-α and discussed their phenotype implications. Overall, our theoretical approach is as a guide to design new experiments for unraveling the principles of the dual host-protective or -harmful antagonistic roles of transitional macrophages in tumor immunoediting and cancer cell fate decisions.

Full Article -PubMed

MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

Frontiers in Oncology 2020

Erick Andrés Muciño-Olmos, Aarón Vázquez-Jiménez, Diana Elena López-Esparza, Vilma Maldonado, Mahara Valverde and Osbaldo Resendis-Antonio

During tumor progression, cancer cells ire their metabolism to face their bioenergetic -demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that -inhibit the translation and stability of crucial mRNAs, some of them causing direct -metabolic alterations in cancer. In this study, we investigated the relationship between -miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned -regulation is diversified depending on the tumor stage. To do so, we implemented a -paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The -cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture -conditions. For MCTS, we selected two-time points during their development to -recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA -expression patterns associated with metabolism. As a result, we identified a set of new -direct putative regulatory interactions between miRNAs and metabolic mRNAs -representative for proliferative and quiescent stages. Notably, our study allows us to -suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, -we found that the overexpression of several miRNAs could directly overturn the -expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this -set of miRNAs downregulates their expression in the quiescent stage, the same set is -upregulated in proliferative stages. This last finding suggests an additional metabolic -switch of the above mentioned metabolic pathways between the quiescent and proliferative -stages. Our results contribute to a better understanding of how miRNAs modulate the -metabolic landscape in breast cancer MCTS, which eventually will help to design new -strategies to mitigate cancer phenotype.

Full Article -PubMed

Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgrounds

Frontiers in Onology 2020

Meztli Matadamas-Guzman, Cecilia Zazueta, Emilio Rojas and Osbaldo Resendis-Antonio

Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds. Here we used a mathematical modeling approach to determine which are the metabolic reactions altered after induction of EMT, based on metabolomic and transcriptional data of three non-small cell lung cancer (NSCLC) cell lines. The model suggested that the most affected pathways were the Krebs cycle, amino acid metabolism, and glutathione metabolism. However, glutathione metabolism had many alterations either on the metabolic reactions or at the transcriptional level in the three cell lines. We identified Glutamate-cysteine ligase (GCL), a key enzyme of glutathione synthesis, as an important common feature that is dysregulated after EMT. Analyzing survival data of men with lung cancer, we observed that patients with mutations in GCL catalytic subunit (GCLC) or Glutathione peroxidase 1 (GPX1) genes survived less time than people without mutations on these genes. Besides, patients with low expression of ANPEP, GPX3 and GLS genes also survived less time than those with high expression. Hence, we propose that glutathione metabolism and glutathione itself could be good targets to delay or potentially prevent EMT induction in NSCLC cell lines.

Full Article -PubMed

Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seq

Scientific Reports 2020

Erick Andrés Muciño-Olmos, Aarón Vázquez-Jiménez, Ugo Avila-Ponce de León, Meztli Matadamas-Guzman, Vilma Maldonado, Tayde López-Santaella, Abrahan Hernández-Hernández and Osbaldo Resendis-Antonio

Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship. As a result, three major robust cellular clusters, with a non-redundant complementary composition, were found. Meanwhile, one cluster promotes proliferation, others mainly activate mechanisms to invade other tissues and serve as a reservoir population conserved over time. Our results provide evidence to see cancer as a systemic unit that has cell populations with task stratification with the ultimate goal of preserving the hallmarks in tumors.

Full Article -PubMed

Memote: A community driven effort towards a standardized genome-scale metabolic model test suite

Nature Biotechnology 2020

Christian Lieven, Moritz E. Beber, Brett G. Olivier, Frank T. Bergmann, Meric Ataman, Parizad Babaei, Jennifer A. Bartell, Lars M. Blank, Siddharth Chauhan, Kevin Correia, Christian Diener, Andreas Dräger, Birgitta E. Ebert, Janaka N. Edirisinghe, Jose P. Faria, Adam Feist, Georgios Fengos, Ronan M. T. Fleming, Beatriz García-Jiménez, Vassily Hatzimanikatis, Wout van Helvoirt, Christopher S. Henry, Henning Hermjakob, Markus J. Herrgård, Hyun Uk Kim, Zachary King, Jasper J. Koehorst, Steffen Klamt, Edda Klipp, Meiyappan Lakshmanan, Nicolas Le Novère, Dong-Yup Lee, Sang Yup Lee, Sunjae Lee, Nathan E. Lewis, Hongwu Ma, Daniel Machado, Radhakrishnan Mahadevan, Paulo Maia, Adil Mardinoglu, Gregory L. Medlock, Jonathan M. Monk, Jens Nielsen, Lars Keld Nielsen, Juan Nogales, Intawat Nookaew, Osbaldo Resendis-Antonio, Bernhard O. Palsson, Jason A. Papin, Kiran R. Patil, Mark Poolman, Nathan D. Price, Anne Richelle, Isabel Rocha, Benjamin J. Sanchez, Peter J. Schaap, Rahuman S. Malik Sheriff, Saeed Shoaie, Nikolaus Sonnenschein, Bas Teusink, Paulo Vilaça, Jon Olav Vik, Judith A. Wodke, Joana C. Xavier, Qianqian Yuan, Maksim Zakhartsev and Cheng Zhang

Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. -Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation. In addition to testing a model once, memote can be configured to do so automatically, i.e., while building a GEM. A comprehensive report displays the model’s performance parameters, which supports informed model development and facilitates error detection. -Memote provides a measure for model quality that is consistent across reconstruction platforms and analysis software and simplifies collaboration within the community by establishing workflows for publicly hosted and version controlled models.

Full Article -PubMed

Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota.

Msystems 2020

Christian Diener and Osbaldo Resendis-Antonio

Alterations in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn’s disease and diabetes. However, establishing the causality between the microbial composition and disease remains a challenge. We introduce a strategy based on metabolic models of complete microbial gut communities in order to derive the particular metabolic consequences of the microbial composition for the diabetic gut in a balanced cohort of 186 individuals. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that allows growth for the majority of observed taxa in a sample. We also integrated various additional constraints such as diet and the measured abundances of microbial species to derive the resulting metabolic alterations for individual metagenomic samples. In particular, we show that growth rates vary greatly across samples and that there exists a network of bacteria implicated in health and disease that mutually influence each others growth rates. Studying individual exchange fluxes between the microbiota and the gut lumen we observed that consumption of metabolites by the microbiota follows a niche structure whereas production of short chain fatty acids by the microbiota was highly sample-specific and was altered in type 2 diabetes and restored after metformin treatment in samples from danish individuals. Additionally, we found that production of butyrate could not be easily influenced by single-target interventions.

Full Article -PubMed

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.

Genome Medicine 2018

Vanessa L. Hale, Patricio Jeraldo, Jun Chen, Michael Mundy, Janet Yao, Sambhawa Priya, Gary Keeney, Kelly Lyke, Jason Ridlon, Bryan A. White, Amy J. French, Stephen N. Thibodeau, Christian Diener, Osbaldo Resendis-Antonio, Jaime Gransee, Tumpa Dutta, Xuan-Mai Petterson, Jaeyun Sung, Ran Blekhman, Lisa Boardman, David Larson, Heidi Nelson and Nicholas Chia

Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks.We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC.Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Full Article -PubMed

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.

Methods (San Diego, Calif.) 2018

Vanessa L Hale, Patricio Jeraldo, Michael Mundy, Janet Yao, Gary Keeney, Nancy Scott, E Heidi Cheek, Jennifer Davidson, Megan Green, Christine Martinez, John Lehman, Chandra Pettry, Erica Reed, Kelly Lyke, Bryan A White, Christian Diener, Osbaldo Resendis-Antonio, Jaime Gransee, Tumpa Dutta, Xuan-Mai Petterson, Lisa Boardman, David Larson, Heidi Nelson and Nicholas Chia

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

Full Article -PubMed

Editorial: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer

Frontiers in Physiology 2017

Osbaldo Resendis-Antonio and Christian Diener

This is the Editorial for our Frontiers Research Topic “Systems Biology and -the challenge of deciphering the metabolic mechanisms underlying cancer”.

The corresponding E-Book will be available soon.

Full Article -PubMed

"Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.

Frontiers in Physiology 2017

Nora A Gutierrez Najera, Osbaldo Resendis-Antonio and Humberto Nicolini

Keywords: diagnosis, lung cancer, omics, psychiatry, systems biology

The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.

Full Article -PubMed

Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.

BMC Evolutionary Biology 2017

Cecilio Valadez-Cano, Roberto Olivares-Hernández, Osbaldo Resendis-Antonio, Alexander DeLuna and Luis Delaye

Keywords: Adaptation, Endosymbiont, Metabolic evolution, Metabolic integration

Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore.

Full Article -PubMed

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

Frontiers in Physiology 2017

Christian Diener and Osbaldo Resendis-Antonio

Keywords: NCI60, TCGA, flux balance analysis, personalized medicine, proliferation, systems biology

Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.

Full Article -PubMed

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine.

Frontiers in Physiology 2016

Alejandra V Contreras, Benjamin Cocom-Chan, Georgina Hernandez-Montes, Tobias Portillo-Bobadilla and Osbaldo Resendis-Antonio

Keywords: cancer metabolism, metabolome, microbiome, next generation sequencing (NGS), precision medicine, systems integration

It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer. Although this latter disease has been thoroughly studied using different high-throughput (HT) technologies, its heterogeneous nature makes its understanding and proper treatment in patients a remaining challenge in clinical settings. Notably, given the outstanding role of host-microbiome interactions, the ecological interactions with microorganisms have become a new significant aspect in the systems that can contribute to the diagnosis and potential treatment of solid cancers. As a part of expanding precision medicine in the area of cancer research, efforts aimed at effective treatments for various kinds of cancer based on the knowledge of genetics, biology of the disease and host-microbiome interactions might improve the prediction of disease risk and implement potential microbiota-directed therapeutics. In this review, we present the state of the art of sequencing and metabolome technologies, computational methods and schemes in systems biology that have addressed recent breakthroughs of uncovering relationships or associations between microorganisms and cancer. Together, microbiome studies extend the horizon of new personalized treatments against cancer from the perspective of precision medicine through a synergistic strategy integrating clinical knowledge, HT data, bioinformatics, and systems biology.

Full Article -PubMed

Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.

Frontiers in Physiology 2016

Mahdi Jalili, Ali Salehzadeh-Yazdi, Shailendra Gupta, Olaf Wolkenhauer, Marjan Yaghmaie, Osbaldo Resendis-Antonio and Kamran Alimoghaddam

Keywords: biological centrality, biological network, centrality, essentiality, topological network analysis

Full Article -PubMed

The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome.

Scientific Reports 2016

Christian Diener, Felipe Muñoz-Gonzalez, Sergio Encarnación and Osbaldo Resendis-Antonio

During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation. Here, enzyme activities are expressed by the space of all feasible kinetic constants (k-cone) such that the alteration between two phenotypes is given by their corresponding kinetic spaces. Deriving an expression for the transformation of the healthy to the cancer k-cone we identified putative regulated enzymes between the HeLa and HaCaT cell lines. We show that only a few enzymatic activities change between those two cell lines and that this regulation does not depend on gene transcription but is instead post-transcriptional. Here, we identify phosphofructokinase as the major driver of proliferation in HeLa cells and suggest an optional regulatory program, associated with oxidative stress, that affects the activity of the pentose phosphate pathway.

Full Article -PubMed

Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer.

Seminars in Cancer Biology 2014

Osbaldo Resendis-Antonio, Carolina González-Torres, Gustavo Jaime-Muñoz, Claudia Erika Hernandez-Patiño and Carlos Felipe Salgado-Muñoz

Keywords: Cancer metabolism, Mathematical models, P4 medicine, Systems biology: Constraint-based modeling

Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients. A variety of schemes in systems biology have been suggested to characterize the phenotypic states associated with cancer by utilizing computational modeling and high-throughput data. These theoretical schemes are distinguished by the level of complexity of the biological mechanisms that they represent and by the computational approaches used to simulate them. Notably, these theoretical approaches have provided a proper framework to explore some distinctive metabolic mechanisms observed in cancer cells such as the Warburg effect. In this review, we focus on presenting a general view of some of these approaches whose application and integration will be crucial in the transition from local to global conclusions in cancer studies. We are convinced that multidisciplinary approaches are required to construct the bases of an integrative and personalized medicine, which has been and remains a fundamental task in the medicine of this century.

Full Article -PubMed

Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

Frontiers in Physiology 2013

Claudia E Hernández Patiño, Gustavo Jaime-Muñoz and Osbaldo Resendis-Antonio

Keywords: cancer metabolic phenotype, computational modeling of metabolism, constraint-based modeling, genome scale metabolic reconstruction, high throughput biology

One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.

Full Article -PubMed

Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironment

Frontiers in Immunology 2012

Ugo Avila-Ponce de León, Aarón Vázquez-Jiménez, Meztli Matadamas-Guzmán and Osbaldo Resendis-Antonio

Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage. The same behavior happens when the M2-associated transcription factors are disturbed (STAT3 or STAT6); cycle attractors will prevent a hyper-regulation scenario implicated in providing a suitable environment for tumor growth. Therefore, here we propose that cyclic macrophage phenotypes can serve as a reservoir for balancing the phenotypes when a specific phenotype-based transcription factor is perturbed in the regulatory network of macrophages. We consider that cyclic attractors should not be simply ignored, but it is necessary to carefully evaluate their biological importance. In this work, we suggest one conjecture: the cyclic attractors can serve as a reservoir to balance the inflammatory/regulatory response of the network under external perturbations.

Full Article -PubMed

Functional modules, structural topology, and optimal activity in metabolic networks.

PLoS Computational Biology 2012

Osbaldo Resendis-Antonio, Magdalena Hernández, Yolanda Mora and Sergio Encarnación

Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris. To experimentally characterize the metabolic phenotype of this microorganism, we obtained the metabolic profile of 220 metabolites at two physiological stages: under free-living conditions, and during nitrogen fixation with P. vulgaris. By integrating these data into a constraint-based model, we built a refined computational platform with the capability to survey the metabolic activity underlying nitrogen fixation in R. etli. Topological analysis of the metabolic reconstruction led us to identify modular structures with functional activities. Consistent with modular activity in metabolism, we found that most of the metabolites experimentally detected in each module simultaneously increased their relative abundances during nitrogen fixation. In this work, we explore the relationships among topology, biological function, and optimal activity in the metabolism of R. etli through an integrative analysis based on modeling and metabolome data. Our findings suggest that the metabolic activity during nitrogen fixation is supported by interacting structural modules that correlate with three functional classifications: nucleic acids, peptides, and lipids. More fundamentally, we supply evidence that such modular organization during functional nitrogen fixation is a robust property under different environmental conditions.

Full Article -PubMed

Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.

BMC Systems Biology 2011

Osbaldo Resendis-Antonio, Magdalena Hernández, Emmanuel Salazar, Sandra Contreras, Gabriel Martínez Batallar, Yolanda Mora and Sergio Encarnación

Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation. This undertaking is not trivial, and the development of computational methods useful in accomplishing an integrative, descriptive and predictive framework is a crucial issue to decoding the principles that regulated the metabolic activity of this biological process.

Full Article -PubMed

Proteomic patterns of cervical cancer cell lines, a network perspective.

BMC Systems Biology 2011

Juan Carlos Higareda-Almaraz, María del Rocío Enríquez-Gasca, Magdalena Hernández-Ortiz, Osbaldo Resendis-Antonio and Sergio Encarnación-Guevara

Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.

Full Article -PubMed

Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect.

PloS One 2010

Osbaldo Resendis-Antonio, Alberto Checa and Sergio Encarnación

Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.

Full Article -PubMed

Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.

PloS One 2009

Osbaldo Resendis-Antonio

Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.

Full Article -PubMed

Regulation by transcription factors in bacteria: beyond description.

FEMS Microbiology Reviews 2008

Enrique Balleza, Lucia N López-Bojorquez, Agustino Martínez-Antonio, Osbaldo Resendis-Antonio, Irma Lozada-Chávez, Yalbi I Balderas-Martínez, Sergio Encarnación and Julio Collado-Vides

Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts. We review recent concepts and developments: cis elements and trans regulatory factors, chromosome organization and structure, transcriptional regulatory networks (TRNs) and transcriptomics. We also summarize new important discoveries that will probably affect the direction of research in gene regulation: epigenetics and stochasticity in transcriptional regulation, synthetic circuits and plasticity and evolution of TRNs. Many of the new discoveries in gene regulation are not extensively tested with wetlab approaches. Consequently, we review this broad area in Inference of TRNs and Dynamical Models of TRNs. Finally, we have stepped backwards to trace the origins of these modern concepts, synthesizing their history in a timeline schema.

Full Article -PubMed

Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli.

PLoS Computational Biology 2007

Osbaldo Resendis-Antonio, Jennifer L Reed, Sergio Encarnación, Julio Collado-Vides and Bernhard Ø Palsson

Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement. In this work we present a genome-scale metabolic reconstruction (iOR363) for R. etli CFN42, which includes 387 metabolic and transport reactions across 26 metabolic pathways. This model was used to analyze the physiological capabilities of R. etli during stages of nitrogen fixation. To study the physiological capacities in silico, an objective function was formulated to simulate symbiotic nitrogen fixation. Flux balance analysis (FBA) was performed, and the predicted active metabolic pathways agreed qualitatively with experimental observations. In addition, predictions for the effects of gene deletions during nitrogen fixation in Rhizobia in silico also agreed with reported experimental data. Overall, we present some evidence supporting that FBA of the reconstructed metabolic network for R. etli provides results that are in agreement with physiological observations. Thus, as for other organisms, the reconstructed genome-scale metabolic network provides an important framework which allows us to compare model predictions with experimental measurements and eventually generate hypotheses on ways to improve nitrogen fixation.

Full Article -PubMed

Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.

BMC Microbiology 2007

Rosa María Gutierrez-Ríos, Julio A Freyre-Gonzalez, Osbaldo Resendis, Julio Collado-Vides, Milton Saier and Guillermo Gosset

Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses.

Full Article -PubMed

Robustness and evolvability in genetic regulatory networks.

Journal of Theoretical Biology 2006

Maximino Aldana, Enrique Balleza, Stuart Kauffman and Osbaldo Resendiz

Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve. Here we address this problem by studying the robustness and evolvability of the attractor landscape of genetic regulatory network models under the process of gene duplication followed by divergence. We show that an intrinsic property of this kind of networks is that, after the divergence of the parent and duplicate genes, with a high probability the previous phenotypes, encoded in the attractor landscape of the network, are preserved and new ones might appear. The above is true in a variety of network topologies and even for the case of extreme divergence in which the duplicate gene bears almost no relation with its parent. Our results indicate that networks operating close to the so-called “critical regime” exhibit the maximum robustness and evolvability simultaneously.

Full Article -PubMed

Modular analysis of the transcriptional regulatory network of E. coli.

Trends in Genetics : TIG 2005

Osbaldo Resendis-Antonio, Julio A Freyre-González, Ricardo Menchaca-Méndez, Rosa M Gutiérrez-Ríos, Agustino Martínez-Antonio, Cristhian Avila-Sánchez and Julio Collado-Vides

The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. Feed-forward motifs tend to be embedded within modules, whereas bi-fan motifs tend to link modules, supporting the notion of a hierarchical network with defined functional modules.

Full Article -PubMed

Machine Learning and COVID-19: Lessons from SARS-CoV-2

January 2023

Ugo Avila-Ponce de León, Aarón Vázquez-Jiménez, Alejandra Cervera, Galilea Resendis-González, Daniel Neri-Rosado and Osbaldo Resendis-Antonio

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behaviour and high-throughput technologies, associated with COVID-19 evolution.

Cancer: a complex disease

December 2018

Elena R. Alvarez-Buylla, Juan Carlos Balandran, Jose Luis Caldu-Primo, Jose Davila-Velderrain, Jennifer Enciso, Enrique Hernandez-Lemus, Lucia S. Lopez Castillo, Juan Carlos Martinez-Garcia, Nancy R. Mejia-Dominguez, Leticia R. Paiva, Rosana Pelayo, Osbaldo Resendis-Antonio and Octavio Valadez-Blanco

This is an EBook can -be downloaded for free.

The study of complex systems and their related phenomena has become a major research venue in the recent years and it is commonly regarded as an important part of the scientific revolution developing through the 21st century. The science of complexity is concerned with the laws of operation and evolution of systems formed by many locally interacting elements that produce collective order at spatiotemporal scales larger than that of the single constitutive elements. This new thinking, that explores formally the emergence of spontaneous higher order and feedback hierarchies, has been particularly successful in the biological sciences. One particular life-threatening disease in humans, overwhelmingly common in the modern world is cancer. It is regarded as a collection of phenomena involving anomalous cell growth caused by an underlying genetic instability with the potential to spread to other parts of the human body. In the present book, a group of well recognized specialists discuss new ideas about the disease. These authors coming from solid backgrounds in physics, mathematics, medicine, molecular and cell biology, genetics and anthropology have dedicated their time to write an authoritative free-available text published under the open access philosophy that hopefully would be in the front-line struggle against cancer, a complex disease.

Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues

March 2018

Luis Olivarez-Quiroz and Osbaldo Resendis-Antonio

This book presents cutting-edge research on the use of physical and -mathematical formalisms to model and quantitatively analyze biological -phenomena ranging from microscopic to macroscopic systems. The systems -discussed in this compilation cover protein folding pathways, gene regulation -in prostate cancer, quorum sensing in bacteria to mathematical and physical -descriptions to analyze anomalous diffusion in patchy environments and the -physical mechanisms that drive active motion in large sets of particles, both -fundamental descriptions that can be applied to different phenomena in biology.

Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer

November 2017

Osbaldo Resendis-Antonio and Christian Diener

This is an EBook compendium of the respective Frontiers Research Topic and can -be downloaded for free.

Since the discovery of the Warburg effect in the 1920s cancer has been tightly -associated with the genetic and metabolic state of the cell. One of the -hallmarks of cancer is the alteration of the cellular metabolism in order to -promote proliferation and undermine cellular defense mechanisms such as -apoptosis or detection by the immune system. However, the strategies by which -this is achieved in different cancers and sometimes even in different patients -of the same cancer is very heterogeneous, which hinders the design of general -treatment options. Recently, there has been an ongoing effort to study this -phenomenon on a genomic scale in order to understand the causality underlying -the disease. Hence, current “omics” technologies have contributed to identify -and monitor different biological pieces at different biological levels, such as -genes, proteins or metabolites. These technological capacities have provided us -with vast amounts of clinical data where a single patient may often give rise -to various tissue samples, each of them being characterized in detail by -genomescale data on the sequence, expression, proteome and metabolome level. -Data with such detail poses the imminent problem of extracting meaningful -interpretations and translating them into specific treatment options. To this -purpose, Systems Biology provides a set of promising computational tools in -order to decipher the mechanisms driving a healthy cell’s metabolism into a -cancerous one. However, this enterprise requires bridging the gap between large -data resources, mathematical analysis and modeling specifically designed to -work with the available data. This is by no means trivial and requires high -levels of communication and adaptation between the experimental and theoretical -side of research.

Encyclopedia of Systems Biology

June 2013

Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho and Hiroki Yokota

The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology.

Symbiotic Endophytes

January 2013

Ricardo Aroca

This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. -Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields.

\ No newline at end of file diff --git a/pubs/index.xml b/pubs/index.xml deleted file mode 100644 index 3d88e18..0000000 --- a/pubs/index.xml +++ /dev/null @@ -1,7 +0,0 @@ -Pubs on Webpage of the Resendis Labhttps://resendislab.github.io/pubs/Recent content in Pubs on Webpage of the Resendis LabHugo -- gohugo.ioen-usFri, 01 Dec 2023 00:00:00 +0000mb-PHENIX: Diffusion and Supervised Uniform Manifold Approximation for denoising microbiota datahttps://resendislab.github.io/pubs/mbphenix/Fri, 01 Dec 2023 00:00:00 +0000https://resendislab.github.io/pubs/mbphenix/Motivation Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. Results We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data.Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohorthttps://resendislab.github.io/pubs/dysbiosis_diabetes/Tue, 27 Jun 2023 00:00:00 +0000https://resendislab.github.io/pubs/dysbiosis_diabetes/Introduction: The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual’s gut microbiota profile.Uncoding the interdependency of tumor microenvironment and macrophage polarization: insights from a continuous network approachhttps://resendislab.github.io/pubs/uncoding_macrophages/Mon, 22 May 2023 00:00:00 +0000https://resendislab.github.io/pubs/uncoding_macrophages/The balance between pro- and anti-inflammatory immune system responses is crucial to preventing complex diseases like cancer. Macrophages are essential immune cells that contribute to this balance constrained by the local signaling profile of the tumor microenvironment. To understand how pro- and anti-inflammatory unbalance emerges in cancer, we developed a theoretical analysis of macrophage differentiation that is derived from activated monocytes circulating in the blood. Once recruited to the site of inflammation, monocytes can be polarized based on the specific interleukins and chemokines in the microenvironment.A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohorthttps://resendislab.github.io/pubs/mycrobiota_diabetes/Wed, 12 Apr 2023 00:00:00 +0000https://resendislab.github.io/pubs/mycrobiota_diabetes/Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. -Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).Spermiogenesis alterations in the absence of CTCF revealed by single cell RNA sequencinghttps://resendislab.github.io/pubs/spermiogenesis/Thu, 30 Mar 2023 00:00:00 +0000https://resendislab.github.io/pubs/spermiogenesis/CTCF is an architectonic protein that organizes the genome inside the nucleus in almost all eukaryotic cells. There is evidence that CTCF plays a critical role during spermatogenesis as its depletion produces abnormal sperm and infertility. However, defects produced by its depletion throughout spermatogenesis have not been fully characterized. In this work, we performed single cell RNA sequencing in spermatogenic cells with and without CTCF. We uncovered defects in transcriptional programs that explain the severity of the damage in the produced sperm.A New Approach to Personalized Nutrition: Postprandial Glycemic Response and its Relationship to Gut Microbiotahttps://resendislab.github.io/pubs/postprandial_glycemic/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/pubs/postprandial_glycemic/A prolonged and elevated postprandial glucose response (PPGR) is now considered a main factor contributing for the development of metabolic syndrome and type 2 diabetes, which could be prevented by dietary interventions. However, dietary recommendations to prevent alterations in PPGR have not always been successful. New evidence has supported that PPGR is not only dependent of dietary factors like the content of carbohydrates, or the glycemic index of the foods, but is also dependent on genetics, body composition, gut microbiota, among others.Chronic Comorbidities in Middle Aged Patients Contribute to Ineffective Emergency Hematopoiesis in Covid-19 Fatal Outcomeshttps://resendislab.github.io/pubs/comorbidities_covid/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/pubs/comorbidities_covid/Background and Aims Mexico is among the countries with the highest estimated excess mortality rates due to the COVID–19 pandemic, with more than half of reported deaths occurring in adults younger than 65 years old. Although this behavior is presumably influenced by the young demographics and the high prevalence of metabolic diseases, the underlying mechanisms have not been determined. Methods The age–stratified case fatality rate (CFR) was estimated in a prospective cohort with 245 hospitalized COVID–19 cases, followed through time, for the period October 2020–September 2021.Machine Learning and COVID-19: Lessons from SARS-CoV-2https://resendislab.github.io/pubs/machine_covid/Sun, 01 Jan 2023 00:00:00 +0000https://resendislab.github.io/pubs/machine_covid/Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population.Macrophage Boolean networks in the time of SARS-CoV-2https://resendislab.github.io/pubs/macrophage_boolean/Mon, 17 Oct 2022 00:00:00 +0000https://resendislab.github.io/pubs/macrophage_boolean/The post-pandemic period of the current coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has lasted longer than expected despite the huge impact of the world-wide vaccination campaign in the past years. Since the pandemic began, endless mathematical models have been published to describe the viral outbreak at a population level. However, the molecular mechanism that drives the pathogenesis of the virus in the human microenvironment has been scarce.Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new erahttps://resendislab.github.io/pubs/type2_diabetes/Mon, 15 Aug 2022 00:00:00 +0000https://resendislab.github.io/pubs/type2_diabetes/The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions.prePrint: A network perspective on the ecology of gut microbiota and progression of Type 2 Diabetes: linkages to keystone taxa in a Mexican cohorthttps://resendislab.github.io/pubs/ecology_gut/Wed, 20 Jul 2022 00:00:00 +0000https://resendislab.github.io/pubs/ecology_gut/Background -The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Results -Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment).Comparative subcellular localization of NRF2 and KEAP1 during the hepatocellular carcinoma development in vivohttps://resendislab.github.io/pubs/comparative_subcellular/Sun, 01 May 2022 00:00:00 +0000https://resendislab.github.io/pubs/comparative_subcellular/The activation of Nuclear Factor, Erythroid 2 Like 2 – Kelch Like ECH Associated Protein 1 (NRF2-KEAP1) signaling pathway plays a critical dual role by either protecting or promoting the carcinogenesis process. However, its activation or nuclear translocation during hepatocellular carcinoma (HCC) progression has not been addressed yet. This study characterizes the subcellular localization of both NRF2 and KEAP1 during diethylnitrosamine-induced hepatocarcinogenesis in the rat. NRF2-KEAP1 pathway was continuously activated along with the increased expression of its target genes, namely Nqo1, Hmox1, Gclc, and Ptgr1.Physiological Network Is Disrupted in Severe COVID-19https://resendislab.github.io/pubs/physiological_networks/Thu, 10 Mar 2022 00:00:00 +0000https://resendislab.github.io/pubs/physiological_networks/The human body is a complex system maintained in homeostasis thanks to the interactions between multiple physiological regulation systems. When faced with physical or biological perturbations, this system must react by keeping a balance between adaptability and robustness. The SARS-COV-2 virus infection poses an immune system challenge that tests the organism’s homeostatic response. Notably, the elderly and men are particularly vulnerable to severe disease, poor outcomes, and death. Mexico seems to have more infected young men than anywhere else.Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seqhttps://resendislab.github.io/pubs/mathematics_2021/Thu, 07 Oct 2021 00:00:00 +0000https://resendislab.github.io/pubs/mathematics_2021/The single-cell RNA-seq allows exploring the transcriptome for one cell at a time. By doing so, cellular regulation is pictured. One limitation is the dropout events phenomenon, where a gene is observed at a low or moderate expression level in one cell but not detected in another. Dropouts obscure legitimate biological heterogeneity leading to the description of a small fraction of the meaningful relations. We used a stochastic approach to model the Reverse Transcription Polymerase Chain Reaction (RT-PCR) kinetic, in which we contemplated the temperature profile, RT-PCR duration, and reaction rates.On Deep Landscape Exploration of COVID-19 Patients Cells and Severity Markershttps://resendislab.github.io/pubs/frontiers_immunology_2021/Thu, 16 Sep 2021 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_immunology_2021/COVID-19 is a disease with a spectrum of clinical responses ranging from moderate to critical. To study and control its effects, a large number of researchers are focused on two substantial aims. On the one hand, the discovery of diverse biomarkers to classify and potentially anticipate the disease severity of patients. These biomarkers could serve as a medical criterion to prioritize attention to those patients with higher prone to severe responses.Transcriptional and Microenvironmental Landscape of Macrophage Transition in Cancer: A Boolean Analysishttps://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/Thu, 10 Jun 2021 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/The balance between pro- and anti-inflammatory immune system responses is crucial to face and counteract complex diseases such as cancer. Macrophages are an essential population that contributes to this balance in collusion with the local tumor microenvironment. Cancer cells evade the attack of macrophages by liberating cytokines and enhancing the transition to the M2 phenotype with pro-tumoral functions. Despite this pernicious effect on immune systems, the M1 phenotype still exists in the environment and can eliminate tumor cells by liberating cytokines that recruit and activate the cytotoxic actions of TH1 effector cells.MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progressionhttps://resendislab.github.io/pubs/fontiers_oncology_2020_erick/Fri, 04 Dec 2020 00:00:00 +0000https://resendislab.github.io/pubs/fontiers_oncology_2020_erick/During tumor progression, cancer cells ire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7).Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgroundshttps://resendislab.github.io/pubs/frontiers_onlogy2020/Sun, 02 Aug 2020 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_onlogy2020/Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds.Unveiling functional heterogeneity in breast cancer multicellular tumor spheroids through single-cell RNA-seqhttps://resendislab.github.io/pubs/scientificreports2020/Thu, 02 Jul 2020 00:00:00 +0000https://resendislab.github.io/pubs/scientificreports2020/Heterogeneity is an intrinsic characteristic of cancer. Even in isogenic tumors, cell populations exhibit differential cellular programs that overall supply malignancy and decrease treatment efficiency. In this study, we investigated the functional relationship among cell subtypes and how this interdependency can promote tumor development in a cancer cell line. To do so, we performed single-cell RNA-seq of MCF7 Multicellular Tumor Spheroids as a tumor model. Analysis of single-cell transcriptomes at two-time points of the spheroid growth, allowed us to dissect their functional relationship.Memote: A community driven effort towards a standardized genome-scale metabolic model test suitehttps://resendislab.github.io/pubs/memote/Mon, 02 Mar 2020 00:00:00 +0000https://resendislab.github.io/pubs/memote/Several studies have shown that neither the formal representation nor the functional requirements of genome-scale metabolic models (GEMs) are precisely defined. Without a consistent standard, comparability, reproducibility, and interoperability of models across groups and software tools cannot be guaranteed. Here, we present memote (https://github.com/opencobra/memote) an open-source software containing a community-maintained, standardized set of metabolic model tests. The tests cover a range of aspects from annotations to conceptual integrity and can be extended to include experimental datasets for automatic model validation.Micom: metagenome-scale modeling to infer metabolic interactions in the microbiota.https://resendislab.github.io/pubs/micom/Tue, 04 Feb 2020 00:00:00 +0000https://resendislab.github.io/pubs/micom/Alterations in the gut microbiota have been associated with a variety of medical conditions such as obesity, Crohn&rsquo;s disease and diabetes. However, establishing the causality between the microbial composition and disease remains a challenge. We introduce a strategy based on metabolic models of complete microbial gut communities in order to derive the particular metabolic consequences of the microbial composition for the diabetic gut in a balanced cohort of 186 individuals. By using a heuristic optimization approach based on L2 regularization we were able to obtain a unique set of realistic growth rates that allows growth for the majority of observed taxa in a sample.Cancer: a complex diseasehttps://resendislab.github.io/pubs/cancer_a_complex_disease/Sat, 01 Dec 2018 00:00:00 +0000https://resendislab.github.io/pubs/cancer_a_complex_disease/This is an EBook can be downloaded for free. -The study of complex systems and their related phenomena has become a major research venue in the recent years and it is commonly regarded as an important part of the scientific revolution developing through the 21st century. The science of complexity is concerned with the laws of operation and evolution of systems formed by many locally interacting elements that produce collective order at spatiotemporal scales larger than that of the single constitutive elements.Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.https://resendislab.github.io/pubs/pm30376889/Wed, 31 Oct 2018 00:00:00 +0000https://resendislab.github.io/pubs/pm30376889/Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes.Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer.https://resendislab.github.io/pubs/pm29704665/Sun, 29 Apr 2018 00:00:00 +0000https://resendislab.github.io/pubs/pm29704665/Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals.Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissueshttps://resendislab.github.io/pubs/quantitative_modeling/Thu, 01 Mar 2018 00:00:00 +0000https://resendislab.github.io/pubs/quantitative_modeling/This book presents cutting-edge research on the use of physical and mathematical formalisms to model and quantitatively analyze biological phenomena ranging from microscopic to macroscopic systems. The systems discussed in this compilation cover protein folding pathways, gene regulation in prostate cancer, quorum sensing in bacteria to mathematical and physical descriptions to analyze anomalous diffusion in patchy environments and the physical mechanisms that drive active motion in large sets of particles, both fundamental descriptions that can be applied to different phenomena in biology.Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancerhttps://resendislab.github.io/pubs/frontiers_ebook/Mon, 27 Nov 2017 00:00:00 +0000https://resendislab.github.io/pubs/frontiers_ebook/This is an EBook compendium of the respective Frontiers Research Topic and can be downloaded for free. -Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system.Editorial: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancerhttps://resendislab.github.io/pubs/editorial_frontiers/Fri, 28 Jul 2017 12:00:00 +0000https://resendislab.github.io/pubs/editorial_frontiers/This is the Editorial for our Frontiers Research Topic &ldquo;Systems Biology and the challenge of deciphering the metabolic mechanisms underlying cancer&rdquo;. -The corresponding E-Book will be available soon."Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases.https://resendislab.github.io/pubs/pm28536537/Fri, 26 May 2017 00:00:00 +0000https://resendislab.github.io/pubs/pm28536537/The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the &ldquo;psychiatric phenotype&rdquo; is to provide an improved vision of the shape of the phenotype as it is visualized by &ldquo;Gestalt&rdquo; psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part.Natural selection drove metabolic specialization of the chromatophore in Paulinella chromatophora.https://resendislab.github.io/pubs/pm28410570/Sun, 16 Apr 2017 00:00:00 +0000https://resendislab.github.io/pubs/pm28410570/Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of Paulinella chromatophora by in silico modeling.Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.https://resendislab.github.io/pubs/pm28082911/Sat, 14 Jan 2017 00:00:00 +0000https://resendislab.github.io/pubs/pm28082911/Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas.Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine.https://resendislab.github.io/pubs/pm28018236/Tue, 27 Dec 2016 00:00:00 +0000https://resendislab.github.io/pubs/pm28018236/It has been experimentally shown that host-microbial interaction plays a major role in shaping the wellness or disease of the human body. Microorganisms coexisting in human tissues provide a variety of benefits that contribute to proper functional activity in the host through the modulation of fundamental processes such as signal transduction, immunity and metabolism. The unbalance of this microbial profile, or dysbiosis, has been correlated with the genesis and evolution of complex diseases such as cancer.Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks.https://resendislab.github.io/pubs/pm27616995/Tue, 13 Sep 2016 00:00:00 +0000https://resendislab.github.io/pubs/pm27616995/The space of enzyme regulation in HeLa cells can be inferred from its intracellular metabolome.https://resendislab.github.io/pubs/pm27335086/Fri, 24 Jun 2016 00:00:00 +0000https://resendislab.github.io/pubs/pm27335086/During the transition from a healthy state to a cancerous one, cells alter their metabolism to increase proliferation. The underlying metabolic alterations may be caused by a variety of different regulatory events on the transcriptional or post-transcriptional level whose identification contributes to the rational design of therapeutic targets. We present a mechanistic strategy capable of inferring enzymatic regulation from intracellular metabolome measurements that is independent of the actual mechanism of regulation.Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer.https://resendislab.github.io/pubs/pm24747697/Tue, 22 Apr 2014 00:00:00 +0000https://resendislab.github.io/pubs/pm24747697/Given the multi-factorial nature of cancer, uncovering its metabolic alterations and evaluating their implications is a major challenge in biomedical sciences that will help in the optimal design of personalized treatments. The advance of high-throughput technologies opens an invaluable opportunity to monitor the activity at diverse biological levels and elucidate how cancer originates, evolves and responds under drug treatments. To this end, researchers are confronted with two fundamental questions: how to interpret high-throughput data and how this information can contribute to the development of personalized treatment in patients.Encyclopedia of Systems Biologyhttps://resendislab.github.io/pubs/encyclopedia/Sat, 01 Jun 2013 00:00:00 +0000https://resendislab.github.io/pubs/encyclopedia/The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology – a ‘one-stop shop’ for someone seeking information on key concepts of systems biology.Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.https://resendislab.github.io/pubs/pm23316163/Tue, 15 Jan 2013 00:00:00 +0000https://resendislab.github.io/pubs/pm23316163/One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines.Symbiotic Endophyteshttps://resendislab.github.io/pubs/symbiotic_endophytes/Tue, 01 Jan 2013 00:00:00 +0000https://resendislab.github.io/pubs/symbiotic_endophytes/This Soil Biology volume examines our current understanding of the mechanisms involved in the beneficial effects transferred to plants by endophytes such as rhizobial, actinorhizal, arbuscular mycorrhizal symbionts and yeasts. Topics presented include how symbiosis starts on the molecular level; chemical signaling in mycorrhizal symbiosis; genomic and functional diversity of endophytes; nitrogen fixation; nutrient uptake and cycling; as well as plant protection against various stress conditions. Further, the use of beneficial microorganisms as biopesticides is discussed, particularly the application of Plant Growth Promoter Rhizobacteria (PGPR) in agriculture with the aim to increase yields.Boolean modeling reveals that cyclic attractors in macrophage polarization serve as reservoirs of states to balance external perturbations from the tumor microenvironmenthttps://resendislab.github.io/pubs/boolean_modeling/Wed, 05 Dec 2012 00:00:00 +0000https://resendislab.github.io/pubs/boolean_modeling/Cyclic attractors generated from Boolean models may explain the adaptability of a cell in response to a dynamical complex tumor microenvironment. In contrast to this idea, we postulate that cyclic attractors in certain cases could be a systemic mechanism to face the perturbations coming from the environment. To justify our conjecture, we present a dynamic analysis of a highly curated transcriptional regulatory network of macrophages constrained into a cancer microenvironment. We observed that when M1-associated transcription factors (STAT1 or NF-κB) are perturbed and the microenvironment balances to a hyper-inflammation condition, cycle attractors activate genes whose signals counteract this effect implicated in tissue damage.Functional modules, structural topology, and optimal activity in metabolic networks.https://resendislab.github.io/pubs/pm23071431/Wed, 17 Oct 2012 00:00:00 +0000https://resendislab.github.io/pubs/pm23071431/Modular organization in biological networks has been suggested as a natural mechanism by which a cell coordinates its metabolic strategies for evolving and responding to environmental perturbations. To understand how this occurs, there is a need for developing computational schemes that contribute to integration of genomic-scale information and assist investigators in formulating biological hypotheses in a quantitative and systematic fashion. In this work, we combined metabolome data and constraint-based modeling to elucidate the relationships among structural modules, functional organization, and the optimal metabolic phenotype of Rhizobium etli, a bacterium that fixes nitrogen in symbiosis with Phaseolus vulgaris.Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling.https://resendislab.github.io/pubs/pm21801415/Tue, 02 Aug 2011 00:00:00 +0000https://resendislab.github.io/pubs/pm21801415/Bacterial nitrogen fixation is the biological process by which atmospheric nitrogen is uptaken by bacteroids located in plant root nodules and converted into ammonium through the enzymatic activity of nitrogenase. In practice, this biological process serves as a natural form of fertilization and its optimization has significant implications in sustainable agricultural programs. Currently, the advent of high-throughput technology supplies with valuable data that contribute to understanding the metabolic activity during bacterial nitrogen fixation.Proteomic patterns of cervical cancer cell lines, a network perspective.https://resendislab.github.io/pubs/pm21696634/Fri, 24 Jun 2011 00:00:00 +0000https://resendislab.github.io/pubs/pm21696634/Cervical cancer is a major mortality factor in the female population. This neoplastic is an excellent model for studying the mechanisms involved in cancer maintenance, because the Human Papilloma Virus (HPV) is the etiology factor in most cases. With the purpose of characterizing the effects of malignant transformation in cellular activity, proteomic studies constitute a reliable way to monitor the biological alterations induced by this disease. In this contextual scheme, a systemic description that enables the identification of the common events between cell lines of different origins, is required to distinguish the essence of carcinogenesis.Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect.https://resendislab.github.io/pubs/pm20811631/Fri, 03 Sep 2010 00:00:00 +0000https://resendislab.github.io/pubs/pm20811631/Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority.Filling kinetic gaps: dynamic modeling of metabolism where detailed kinetic information is lacking.https://resendislab.github.io/pubs/pm19305506/Tue, 24 Mar 2009 00:00:00 +0000https://resendislab.github.io/pubs/pm19305506/Integrative analysis between dynamical modeling of metabolic networks and data obtained from high throughput technology represents a worthy effort toward a holistic understanding of the link among phenotype and dynamical response. Even though the theoretical foundation for modeling metabolic network has been extensively treated elsewhere, the lack of kinetic information has limited the analysis in most of the cases. To overcome this constraint, we present and illustrate a new statistical approach that has two purposes: integrate high throughput data and survey the general dynamical mechanisms emerging for a slightly perturbed metabolic network.Regulation by transcription factors in bacteria: beyond description.https://resendislab.github.io/pubs/pm19076632/Wed, 17 Dec 2008 00:00:00 +0000https://resendislab.github.io/pubs/pm19076632/Transcription is an essential step in gene expression and its understanding has been one of the major interests in molecular and cellular biology. By precisely tuning gene expression, transcriptional regulation determines the molecular machinery for developmental plasticity, homeostasis and adaptation. In this review, we transmit the main ideas or concepts behind regulation by transcription factors and give just enough examples to sustain these main ideas, thus avoiding a classical ennumeration of facts.Metabolic reconstruction and modeling of nitrogen fixation in Rhizobium etli.https://resendislab.github.io/pubs/pm17922569/Wed, 10 Oct 2007 00:00:00 +0000https://resendislab.github.io/pubs/pm17922569/Rhizobiaceas are bacteria that fix nitrogen during symbiosis with plants. This symbiotic relationship is crucial for the nitrogen cycle, and understanding symbiotic mechanisms is a scientific challenge with direct applications in agronomy and plant development. Rhizobium etli is a bacteria which provides legumes with ammonia (among other chemical compounds), thereby stimulating plant growth. A genome-scale approach, integrating the biochemical information available for R. etli, constitutes an important step toward understanding the symbiotic relationship and its possible improvement.Identification of regulatory network topological units coordinating the genome-wide transcriptional response to glucose in Escherichia coli.https://resendislab.github.io/pubs/pm17559662/Fri, 15 Jun 2007 00:00:00 +0000https://resendislab.github.io/pubs/pm17559662/Glucose is the preferred carbon and energy source for Escherichia coli. A complex regulatory network coordinates gene expression, transport and enzyme activities in response to the presence of this sugar. To determine the extent of the cellular response to glucose, we applied an approach combining global transcriptome and regulatory network analyses.Robustness and evolvability in genetic regulatory networks.https://resendislab.github.io/pubs/pm17188715/Tue, 26 Dec 2006 00:00:00 +0000https://resendislab.github.io/pubs/pm17188715/Living organisms are robust to a great variety of genetic changes. Gene regulation networks and metabolic pathways self-organize and reaccommodate to make the organism perform with stability and reliability under many point mutations, gene duplications and gene deletions. At the same time, living organisms are evolvable, which means that these kind of genetic perturbations can eventually make the organism acquire new functions and adapt to new environments. It is still an open problem to determine how robustness and evolvability blend together at the genetic level to produce stable organisms that yet can change and evolve.Modular analysis of the transcriptional regulatory network of E. coli.https://resendislab.github.io/pubs/pm15680508/Tue, 01 Feb 2005 00:00:00 +0000https://resendislab.github.io/pubs/pm15680508/The transcriptional network of Escherichia coli is currently the best-understood regulatory network of a single cell. Motivated by statistical evidence, suggesting a hierarchical modular architecture in this network, we identified eight modules with well-defined physiological functions. These modules were identified by a clustering approach, using the shortest path to trace regulatory relationships across genes in the network. We report the type (feed forward and bifan) and distribution of motifs between and within modules. \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml deleted file mode 100644 index 7c28b17..0000000 --- a/sitemap.xml +++ /dev/null @@ -1 +0,0 @@ -https://resendislab.github.io/pubs/mbphenix/2023-12-01T00:00:00+00:00https://resendislab.github.io/pubs/2023-12-01T00:00:00+00:00https://resendislab.github.io/2023-12-01T00:00:00+00:00https://resendislab.github.io/projects/gem_spheroids/2023-06-27T00:00:00+00:00https://resendislab.github.io/pubs/dysbiosis_diabetes/2023-06-27T00:00:00+00:00https://resendislab.github.io/projects/diabetes_micom/2023-06-27T00:00:00+00:00https://resendislab.github.io/projects/diabetes_metformin/2023-06-27T00:00:00+00:00https://resendislab.github.io/projects/2023-06-27T00:00:00+00:00https://resendislab.github.io/pubs/uncoding_macrophages/2023-05-22T00:00:00+00:00https://resendislab.github.io/pubs/mycrobiota_diabetes/2023-04-12T00:00:00+00:00https://resendislab.github.io/pubs/spermiogenesis/2023-03-30T00:00:00+00:00https://resendislab.github.io/pubs/postprandial_glycemic/2023-01-01T00:00:00+00:00https://resendislab.github.io/pubs/comorbidities_covid/2023-01-01T00:00:00+00:00https://resendislab.github.io/pubs/machine_covid/2023-01-01T00:00:00+00:00https://resendislab.github.io/projects/metabolic_macrophages/2023-01-01T00:00:00+00:00https://resendislab.github.io/projects/leukemia/2022-12-15T00:00:00+00:00https://resendislab.github.io/projects/t2d/2022-12-15T00:00:00+00:00https://resendislab.github.io/projects/covidmicom/2022-12-15T00:00:00+00:00https://resendislab.github.io/projects/ecologicalgut/2022-12-15T00:00:00+00:00https://resendislab.github.io/projects/t2dmicom/2022-12-15T00:00:00+00:00https://resendislab.github.io/projects/scphenix/2022-12-15T00:00:00+00:00https://resendislab.github.io/pubs/macrophage_boolean/2022-10-17T00:00:00+00:00https://resendislab.github.io/pubs/type2_diabetes/2022-08-15T00:00:00+00:00https://resendislab.github.io/pubs/ecology_gut/2022-07-20T00:00:00+00:00https://resendislab.github.io/pubs/comparative_subcellular/2022-05-01T00:00:00+00:00https://resendislab.github.io/pubs/physiological_networks/2022-03-10T00:00:00+00:00https://resendislab.github.io/pubs/mathematics_2021/2021-10-07T00:00:00+00:00https://resendislab.github.io/pubs/frontiers_immunology_2021/2021-09-16T00:00:00+00:00https://resendislab.github.io/pubs/frontiers_immunology_2021_ugo/2021-06-10T00:00:00+00:00https://resendislab.github.io/pubs/fontiers_oncology_2020_erick/2020-12-04T00:00:00+00:00https://resendislab.github.io/pubs/frontiers_onlogy2020/2020-08-02T00:00:00+00:00https://resendislab.github.io/pubs/scientificreports2020/2020-07-02T00:00:00+00:00https://resendislab.github.io/pubs/memote/2020-03-02T00:00:00+00:00https://resendislab.github.io/pubs/micom/2020-02-04T00:00:00+00:00https://resendislab.github.io/projects/emt/2019-12-06T00:00:00+00:00https://resendislab.github.io/projects/diabe_metabolism/2019-12-06T00:00:00+00:00https://resendislab.github.io/events/biophysmex2019/2019-09-06T00:00:00+00:00https://resendislab.github.io/events/2019-09-06T00:00:00+00:00https://resendislab.github.io/events/is3b_2019/2019-08-05T00:00:00+00:00https://resendislab.github.io/projects/inmunology_cancer/2018-12-06T00:00:00+00:00https://resendislab.github.io/projects/hepato/2018-12-06T00:00:00+00:00https://resendislab.github.io/projects/prolif/2018-12-06T00:00:00+00:00https://resendislab.github.io/projects/singlecell_thelma/2018-12-06T00:00:00+00:00https://resendislab.github.io/projects/spheroids/2018-12-06T00:00:00+00:00https://resendislab.github.io/pubs/cancer_a_complex_disease/2018-12-01T00:00:00+00:00https://resendislab.github.io/pubs/pm30376889/2018-10-31T00:00:00+00:00https://resendislab.github.io/pubs/pm29704665/2018-04-29T00:00:00+00:00https://resendislab.github.io/pubs/quantitative_modeling/2018-03-01T00:00:00+00:00https://resendislab.github.io/pubs/frontiers_ebook/2017-11-27T00:00:00+00:00https://resendislab.github.io/software/micom/2017-10-01T00:00:00+00:00https://resendislab.github.io/software/2017-10-01T00:00:00+00:00https://resendislab.github.io/software/microbiome/2017-08-01T00:00:00+00:00https://resendislab.github.io/pubs/editorial_frontiers/2017-07-28T12:00:00+00:00https://resendislab.github.io/positions/students/2017-06-29T12:53:05-05:00https://resendislab.github.io/positions/2017-06-29T12:53:05-05:00https://resendislab.github.io/positions/postdocs/2017-06-29T12:52:56-05:00https://resendislab.github.io/pubs/pm28536537/2017-05-26T00:00:00+00:00https://resendislab.github.io/events/biological_physics/2017-05-17T00:00:00+00:00https://resendislab.github.io/software/corda/2017-05-01T00:00:00+00:00https://resendislab.github.io/pubs/pm28410570/2017-04-16T00:00:00+00:00https://resendislab.github.io/pubs/pm28082911/2017-01-14T00:00:00+00:00https://resendislab.github.io/pubs/pm28018236/2016-12-27T00:00:00+00:00https://resendislab.github.io/categories/2016-12-06T09:19:57-06:00https://resendislab.github.io/posts/test/2016-12-06T09:19:57-06:00https://resendislab.github.io/posts/2016-12-06T09:19:57-06:00https://resendislab.github.io/categories/science/2016-12-06T09:19:57-06:00https://resendislab.github.io/categories/tutorial/2016-12-06T09:19:57-06:00https://resendislab.github.io/about/2016-12-05T14:48:16-06:00https://resendislab.github.io/about/we/2016-12-05T14:48:16-06:00https://resendislab.github.io/pubs/pm27616995/2016-09-13T00:00:00+00:00https://resendislab.github.io/events/is3b/2016-08-02T00:00:00+00:00https://resendislab.github.io/pubs/pm27335086/2016-06-24T00:00:00+00:00https://resendislab.github.io/software/dycone/2016-06-01T00:00:00+00:00https://resendislab.github.io/events/is3b_2014/2014-08-04T00:00:00+00:00https://resendislab.github.io/pubs/pm24747697/2014-04-22T00:00:00+00:00https://resendislab.github.io/pubs/encyclopedia/2013-06-01T00:00:00+00:00https://resendislab.github.io/pubs/pm23316163/2013-01-15T00:00:00+00:00https://resendislab.github.io/pubs/symbiotic_endophytes/2013-01-01T00:00:00+00:00https://resendislab.github.io/pubs/boolean_modeling/2012-12-05T00:00:00+00:00https://resendislab.github.io/pubs/pm23071431/2012-10-17T00:00:00+00:00https://resendislab.github.io/pubs/pm21801415/2011-08-02T00:00:00+00:00https://resendislab.github.io/pubs/pm21696634/2011-06-24T00:00:00+00:00https://resendislab.github.io/pubs/pm20811631/2010-09-03T00:00:00+00:00https://resendislab.github.io/pubs/pm19305506/2009-03-24T00:00:00+00:00https://resendislab.github.io/pubs/pm19076632/2008-12-17T00:00:00+00:00https://resendislab.github.io/pubs/pm17922569/2007-10-10T00:00:00+00:00https://resendislab.github.io/pubs/pm17559662/2007-06-15T00:00:00+00:00https://resendislab.github.io/pubs/pm17188715/2006-12-26T00:00:00+00:00https://resendislab.github.io/pubs/pm15680508/2005-02-01T00:00:00+00:00https://resendislab.github.io/about/contact/1900-01-01T00:00:00+00:00https://resendislab.github.io/members/dummy/https://resendislab.github.io/members/https://resendislab.github.io/tags/ \ No newline at end of file diff --git a/software/index.html b/software/index.html deleted file mode 100644 index e162d7c..0000000 --- a/software/index.html +++ /dev/null @@ -1,31 +0,0 @@ -Webpage of the Resendis Lab

Software

Christian Diener and Osbaldo Resendis Antonio


micom is a Python package for metabolic modeling of microbial communities -developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio -at the National Institute of Genomic Medicine Mexico.

micom allows you to construct a community model from a list on input COBRA -models and manages exchange fluxes between individuals and individuals with the -environment. It explicitly accounts for different abundances of individuals in -the community and can thus incorporate data from 16S rRNA sequencing -experiments. It allows optimization with a variety of algorithms modeling the -trade-off between egoistic growth rate maximization and cooperative objectives.

code Source -link Documentation

Our microbiome pipeline

Christian Diener and Osbaldo Resendis Antonio


This repository contains the standardized analysis pipeline for 16S and metagenome -data. It serves as a testing ground for what will be required to analyze around -500 samples.

code Source -link Documentation

CORDA for Python

Christian Diener and Osbaldo Resendis Antonio


This is a Python implementation based on the papers of Schultz et. al. with -some added optimizations. It is based on the publications of Schultz et. al. -[1, 2].

CORDA, short for Cost Optimization Reaction Dependency Assessment is a -method for the reconstruction of metabolic networks from a given -reference model (a database of all known reactions) and a confidence -mapping for reactions. It allows you to reconstruct metabolic models for -tissues, patients or specific experimental conditions from a set of -transcription or proteome measurements.

code Source -link Documentation

Christian Diener and Osbaldo Resendis Antonio


Dycone (“dynamic cone”) allows you infer enzymatic regulation from metabolome -measurements. It employs formalisms based on flux and k-cone analysis to connect -metabolome data to distinct regulations of enzyme activity. Most of the -analysis methods can be applied to genome-scale data.

code Source
\ No newline at end of file diff --git a/software/index.xml b/software/index.xml deleted file mode 100644 index 3586f25..0000000 --- a/software/index.xml +++ /dev/null @@ -1,3 +0,0 @@ -Softwares on Webpage of the Resendis Labhttps://resendislab.github.io/software/Recent content in Softwares on Webpage of the Resendis LabHugo -- gohugo.ioen-usSun, 01 Oct 2017 00:00:00 +0000micomhttps://resendislab.github.io/software/micom/Sun, 01 Oct 2017 00:00:00 +0000https://resendislab.github.io/software/micom/micom is a Python package for metabolic modeling of microbial communities developed in the Human Systems Biology Group of Prof. Osbaldo Resendis Antonio at the National Institute of Genomic Medicine Mexico. -micom allows you to construct a community model from a list on input COBRA models and manages exchange fluxes between individuals and individuals with the environment. It explicitly accounts for different abundances of individuals in the community and can thus incorporate data from 16S rRNA sequencing experiments.Our microbiome pipelinehttps://resendislab.github.io/software/microbiome/Tue, 01 Aug 2017 00:00:00 +0000https://resendislab.github.io/software/microbiome/This repository contains the standardized analysis pipeline for 16S and metagenome data. It serves as a testing ground for what will be required to analyze around 500 samples.CORDA for Pythonhttps://resendislab.github.io/software/corda/Mon, 01 May 2017 00:00:00 +0000https://resendislab.github.io/software/corda/This is a Python implementation based on the papers of Schultz et. al. with some added optimizations. It is based on the publications of Schultz et. al. [1, 2]. -CORDA, short for Cost Optimization Reaction Dependency Assessment is a method for the reconstruction of metabolic networks from a given reference model (a database of all known reactions) and a confidence mapping for reactions. It allows you to reconstruct metabolic models for tissues, patients or specific experimental conditions from a set of transcription or proteome measurements.dyconehttps://resendislab.github.io/software/dycone/Wed, 01 Jun 2016 00:00:00 +0000https://resendislab.github.io/software/dycone/Dycone (&ldquo;dynamic cone&rdquo;) allows you infer enzymatic regulation from metabolome measurements. It employs formalisms based on flux and k-cone analysis to connect metabolome data to distinct regulations of enzyme activity. Most of the analysis methods can be applied to genome-scale data. \ No newline at end of file diff --git a/static/css/prism.css b/static/css/prism.css new file mode 100644 index 0000000..971ece5 --- /dev/null +++ b/static/css/prism.css @@ -0,0 +1,123 @@ +/* http://prismjs.com/download.html?themes=prism-okaidia&languages=markup+css+clike+javascript+bash+markdown+matlab+python+r */ +/** + * okaidia theme for JavaScript, CSS and HTML + * Loosely based on Monokai textmate theme by http://www.monokai.nl/ + * @author ocodia + */ + +code[class*="language-"], +pre[class*="language-"] { + color: #f8f8f2; + background: none; + text-shadow: 0 1px rgba(0, 0, 0, 0.3); + font-family: Consolas, Monaco, 'Andale Mono', 'Ubuntu Mono', monospace; + text-align: left; + white-space: pre; + word-spacing: normal; + word-break: normal; + word-wrap: normal; + line-height: 1.5; + + -moz-tab-size: 4; + -o-tab-size: 4; + tab-size: 4; + + -webkit-hyphens: none; + -moz-hyphens: none; + -ms-hyphens: none; + hyphens: none; +} + +/* Code blocks */ +pre[class*="language-"] { + padding: 1em; + margin: .5em 0; + overflow: auto; + border-radius: 0.3em; +} + +:not(pre) > code[class*="language-"], +pre[class*="language-"] { + background: #272822; +} + +/* Inline code */ +:not(pre) > code[class*="language-"] { + padding: .1em; + border-radius: .3em; + white-space: normal; +} + +.token.comment, +.token.prolog, +.token.doctype, +.token.cdata { + color: slategray; +} + +.token.punctuation { + color: #f8f8f2; +} + +.namespace { + opacity: .7; +} + +.token.property, +.token.tag, +.token.constant, +.token.symbol, +.token.deleted { + color: #f92672; +} + +.token.boolean, +.token.number { + color: #ae81ff; +} + +.token.selector, +.token.attr-name, +.token.string, +.token.char, +.token.builtin, +.token.inserted { + color: #a6e22e; +} + +.token.operator, +.token.entity, +.token.url, +.language-css .token.string, +.style .token.string, +.token.variable { + color: #f8f8f2; +} + +.token.atrule, +.token.attr-value, +.token.function { + color: #e6db74; +} + +.token.keyword { + color: #66d9ef; +} + +.token.regex, +.token.important { + color: #fd971f; +} + +.token.important, +.token.bold { + font-weight: bold; +} +.token.italic { + font-style: italic; +} + +.token.entity { + cursor: help; +} + diff --git a/static/css/styles.css b/static/css/styles.css new file mode 100644 index 0000000..8aad050 --- /dev/null +++ b/static/css/styles.css @@ -0,0 +1,220 @@ +html > body, h1, h2, .mdl-button { + font-family: 'Lato', 'Roboto', 'Arial', sans-serif !important; +} + +a { + text-decoration: none; + font-weight: bold; +} + +#demo-canvas { + width: 100%; + height: 75vh; + min-height: 400px; +} + +.main-title { + position: absolute; + margin: 0; + padding: 0; + color: #fff; + text-align: center; + -webkit-transform: translate3d(-50%, -50%, 0); + transform: translate3d(-50%, -50%, 0); + top: 37.5%; + left: 50%; +} + +.main-title > h1 { + letter-spacing: 5px; + font-size: 42px; +} + +.main-title > h2 { + font-size: 18px; +} + +@media screen and (max-width: 1024px) { +.main-title { + width: 90%; + margin-top: 56px; +} + +#demo-canvas { + background: url(../media/neuro.png) no-repeat fixed center; +} +} + +.mdl-layout__drawer, .mdl-layout__header { + background-color: #2b2b2b; + border-right-color: #2b2b2b; +} + +.mdl-layout__drawer .mdl-navigation .mdl-navigation__link { + color: #fff; + font-weight: bold; + font-size: 1.15rem; + padding: 10px 40px; +} + +.mdl-layout__drawer .mdl-navigation .mdl-navigation__link:hover { + background-color: #aaa; + font-weight: bold; + font-size: 1.15rem; +} + +.mdl-grid { + max-width: 1400px; +} + +.mdl-cell { + padding: 0.5vw; +} + +.mdl-card { + font-family: 'Lato', 'Roboto', 'Arial', sans-serif !important; + width: 100%; + min-height: 10px; + height: 100%; +} + +.card .mdl-card__supporting-text { + padding: 1rem; + width: auto; +} + +.mdl-card__supporting-text p, li { + font-size: 16px; +} + +.mdl-card__supporting-text img { + width: calc(100% - 2em); +} + +.mdl-card__supporting-text.book p img { + display: block; + max-width: 240px; + padding: 0 1em 0 1em; + margin: auto; +} + +@media screen and (min-width: 640px) { +.mdl-card__supporting-text.book p img { + float: left; + padding-right: 2em; + margin: 0; +} +} + +.card .mdl-card__title { + color: #fff; + background-color: #263238; + height: 120px; +} + +.mdl-card__supporting-text h1 { + font-size: 18px; + line-height: normal; + margin: 0.25em 0; +} + +.mdl-card__supporting-text h2 { + font-size: 16px; + margin: 0; + line-height: normal; + margin: 0.25em 0; +} + +.mdl-card__supporting-text h3 { + font-size: 14px; + font-weight: bold; + line-height: normal; + margin: 0.25em 0; +} + +.mdl-card__actions { + margin-top: auto; +} + +.mdl-button--primary img { + margin: auto 0.5em auto 0; +} + +.mdl-mini-footer { + padding: 1em; +} + +.mdl-mini-footer a { + font-weight: bold; + color: #ddd; +} + +.mdl-mini-footer a:hover { + color: #fff; +} + +.basic-info { + display: flex; + flex-flow: row wrap; + justify-content: center; + align-items: center; +} + +.basic-info h1 { + font-size: 1.6rem; + line-height: normal; + margin: 0.5em 0; +} + +.basic-info .face { + max-width: 200px; + margin: 20px 60px; +} + +.basic-info .address p{ + font-size: 1.2em; +} + +#back { + margin: 5px; +} + +table { + margin-left: auto; + margin-right: auto; +} + +.spaced { + display: flex; + justify-content: space-between; +} + +@media screen and (max-width: 1024px) { +.mdl-layout__tab-bar { + width: calc(100% - 120px); + padding: 0 0 0 60px; +} +} + +.mdl-button { + font-weight: bold; +} + +.basic-info { + display: flex; + flex-flow: row wrap; + justify-content: center; + align-items: center; +} + +.basic-info > div { + text-align: center; + width: 250px; +} + +.basic-info .face { + width: 100px; + height: 100px; + border-radius: 50%; + margin: 0 1em; +} diff --git a/static/js/TweenMax.min.js b/static/js/TweenMax.min.js new file mode 100644 index 0000000..e959a64 --- /dev/null +++ b/static/js/TweenMax.min.js @@ -0,0 +1,16 @@ +/*! + * VERSION: 1.11.0 + * DATE: 2013-10-21 + * UPDATES AND DOCS AT: http://www.greensock.com + * + * Includes all of the following: TweenLite, TweenMax, TimelineLite, TimelineMax, EasePack, CSSPlugin, RoundPropsPlugin, BezierPlugin, AttrPlugin, DirectionalRotationPlugin + * + * @license Copyright (c) 2008-2013, GreenSock. All rights reserved. + * This work is subject to the terms at http://www.greensock.com/terms_of_use.html or for + * Club GreenSock members, the software agreement that was issued with your membership. + * + * @author: Jack Doyle, jack@greensock.com + **/ +(window._gsQueue||(window._gsQueue=[])).push(function(){"use strict";window._gsDefine("TweenMax",["core.Animation","core.SimpleTimeline","TweenLite"],function(t,e,i){var s=[].slice,r=function(t,e,s){i.call(this,t,e,s),this._cycle=0,this._yoyo=this.vars.yoyo===!0,this._repeat=this.vars.repeat||0,this._repeatDelay=this.vars.repeatDelay||0,this._dirty=!0,this.render=r.prototype.render},n=1e-10,a=i._internals.isSelector,o=i._internals.isArray,h=r.prototype=i.to({},.1,{}),l=[];r.version="1.11.0",h.constructor=r,h.kill()._gc=!1,r.killTweensOf=r.killDelayedCallsTo=i.killTweensOf,r.getTweensOf=i.getTweensOf,r.ticker=i.ticker,h.invalidate=function(){return this._yoyo=this.vars.yoyo===!0,this._repeat=this.vars.repeat||0,this._repeatDelay=this.vars.repeatDelay||0,this._uncache(!0),i.prototype.invalidate.call(this)},h.updateTo=function(t,e){var s,r=this.ratio;e&&this.timeline&&this._startTime.998){var n=this._time;this.render(0,!0,!1),this._initted=!1,this.render(n,!0,!1)}else if(this._time>0){this._initted=!1,this._init();for(var a,o=1/(1-r),h=this._firstPT;h;)a=h.s+h.c,h.c*=o,h.s=a-h.c,h=h._next}return this},h.render=function(t,e,i){this._initted||0===this._duration&&this.vars.repeat&&this.invalidate();var s,r,a,o,h,_,u,p,f=this._dirty?this.totalDuration():this._totalDuration,c=this._time,m=this._totalTime,d=this._cycle,g=this._duration;if(t>=f?(this._totalTime=f,this._cycle=this._repeat,this._yoyo&&0!==(1&this._cycle)?(this._time=0,this.ratio=this._ease._calcEnd?this._ease.getRatio(0):0):(this._time=g,this.ratio=this._ease._calcEnd?this._ease.getRatio(1):1),this._reversed||(s=!0,r="onComplete"),0===g&&(p=this._rawPrevTime,(0===t||0>p||p===n)&&p!==t&&(i=!0,p>n&&(r="onReverseComplete")),this._rawPrevTime=p=!e||t?t:n)):1e-7>t?(this._totalTime=this._time=this._cycle=0,this.ratio=this._ease._calcEnd?this._ease.getRatio(0):0,(0!==m||0===g&&this._rawPrevTime>n)&&(r="onReverseComplete",s=this._reversed),0>t?(this._active=!1,0===g&&(this._rawPrevTime>=0&&(i=!0),this._rawPrevTime=p=!e||t?t:n)):this._initted||(i=!0)):(this._totalTime=this._time=t,0!==this._repeat&&(o=g+this._repeatDelay,this._cycle=this._totalTime/o>>0,0!==this._cycle&&this._cycle===this._totalTime/o&&this._cycle--,this._time=this._totalTime-this._cycle*o,this._yoyo&&0!==(1&this._cycle)&&(this._time=g-this._time),this._time>g?this._time=g:0>this._time&&(this._time=0)),this._easeType?(h=this._time/g,_=this._easeType,u=this._easePower,(1===_||3===_&&h>=.5)&&(h=1-h),3===_&&(h*=2),1===u?h*=h:2===u?h*=h*h:3===u?h*=h*h*h:4===u&&(h*=h*h*h*h),this.ratio=1===_?1-h:2===_?h:.5>this._time/g?h/2:1-h/2):this.ratio=this._ease.getRatio(this._time/g)),c===this._time&&!i&&d===this._cycle)return m!==this._totalTime&&this._onUpdate&&(e||this._onUpdate.apply(this.vars.onUpdateScope||this,this.vars.onUpdateParams||l)),void 0;if(!this._initted){if(this._init(),!this._initted||this._gc)return;this._time&&!s?this.ratio=this._ease.getRatio(this._time/g):s&&this._ease._calcEnd&&(this.ratio=this._ease.getRatio(0===this._time?0:1))}for(this._active||!this._paused&&this._time!==c&&t>=0&&(this._active=!0),0===m&&(this._startAt&&(t>=0?this._startAt.render(t,e,i):r||(r="_dummyGS")),this.vars.onStart&&(0!==this._totalTime||0===g)&&(e||this.vars.onStart.apply(this.vars.onStartScope||this,this.vars.onStartParams||l))),a=this._firstPT;a;)a.f?a.t[a.p](a.c*this.ratio+a.s):a.t[a.p]=a.c*this.ratio+a.s,a=a._next;this._onUpdate&&(0>t&&this._startAt&&this._startTime&&this._startAt.render(t,e,i),e||this._onUpdate.apply(this.vars.onUpdateScope||this,this.vars.onUpdateParams||l)),this._cycle!==d&&(e||this._gc||this.vars.onRepeat&&this.vars.onRepeat.apply(this.vars.onRepeatScope||this,this.vars.onRepeatParams||l)),r&&(this._gc||(0>t&&this._startAt&&!this._onUpdate&&this._startTime&&this._startAt.render(t,e,i),s&&(this._timeline.autoRemoveChildren&&this._enabled(!1,!1),this._active=!1),!e&&this.vars[r]&&this.vars[r].apply(this.vars[r+"Scope"]||this,this.vars[r+"Params"]||l),0===g&&this._rawPrevTime!==p&&(this._rawPrevTime=0)))},r.to=function(t,e,i){return new r(t,e,i)},r.from=function(t,e,i){return i.runBackwards=!0,i.immediateRender=0!=i.immediateRender,new r(t,e,i)},r.fromTo=function(t,e,i,s){return s.startAt=i,s.immediateRender=0!=s.immediateRender&&0!=i.immediateRender,new r(t,e,s)},r.staggerTo=r.allTo=function(t,e,n,h,_,u,p){h=h||0;var f,c,m,d,g=n.delay||0,v=[],y=function(){n.onComplete&&n.onComplete.apply(n.onCompleteScope||this,arguments),_.apply(p||this,u||l)};for(o(t)||("string"==typeof t&&(t=i.selector(t)||t),a(t)&&(t=s.call(t,0))),f=t.length,m=0;f>m;m++){c={};for(d in n)c[d]=n[d];c.delay=g,m===f-1&&_&&(c.onComplete=y),v[m]=new r(t[m],e,c),g+=h}return v},r.staggerFrom=r.allFrom=function(t,e,i,s,n,a,o){return i.runBackwards=!0,i.immediateRender=0!=i.immediateRender,r.staggerTo(t,e,i,s,n,a,o)},r.staggerFromTo=r.allFromTo=function(t,e,i,s,n,a,o,h){return s.startAt=i,s.immediateRender=0!=s.immediateRender&&0!=i.immediateRender,r.staggerTo(t,e,s,n,a,o,h)},r.delayedCall=function(t,e,i,s,n){return new r(e,0,{delay:t,onComplete:e,onCompleteParams:i,onCompleteScope:s,onReverseComplete:e,onReverseCompleteParams:i,onReverseCompleteScope:s,immediateRender:!1,useFrames:n,overwrite:0})},r.set=function(t,e){return new r(t,0,e)},r.isTweening=function(t){return i.getTweensOf(t,!0).length>0};var _=function(t,e){for(var s=[],r=0,n=t._first;n;)n instanceof i?s[r++]=n:(e&&(s[r++]=n),s=s.concat(_(n,e)),r=s.length),n=n._next;return s},u=r.getAllTweens=function(e){return _(t._rootTimeline,e).concat(_(t._rootFramesTimeline,e))};r.killAll=function(t,i,s,r){null==i&&(i=!0),null==s&&(s=!0);var n,a,o,h=u(0!=r),l=h.length,_=i&&s&&r;for(o=0;l>o;o++)a=h[o],(_||a instanceof e||(n=a.target===a.vars.onComplete)&&s||i&&!n)&&(t?a.totalTime(a.totalDuration()):a._enabled(!1,!1))},r.killChildTweensOf=function(t,e){if(null!=t){var n,h,l,_,u,p=i._tweenLookup;if("string"==typeof t&&(t=i.selector(t)||t),a(t)&&(t=s(t,0)),o(t))for(_=t.length;--_>-1;)r.killChildTweensOf(t[_],e);else{n=[];for(l in p)for(h=p[l].target.parentNode;h;)h===t&&(n=n.concat(p[l].tweens)),h=h.parentNode;for(u=n.length,_=0;u>_;_++)e&&n[_].totalTime(n[_].totalDuration()),n[_]._enabled(!1,!1)}}};var p=function(t,i,s,r){i=i!==!1,s=s!==!1,r=r!==!1;for(var n,a,o=u(r),h=i&&s&&r,l=o.length;--l>-1;)a=o[l],(h||a instanceof e||(n=a.target===a.vars.onComplete)&&s||i&&!n)&&a.paused(t)};return r.pauseAll=function(t,e,i){p(!0,t,e,i)},r.resumeAll=function(t,e,i){p(!1,t,e,i)},r.globalTimeScale=function(e){var s=t._rootTimeline,r=i.ticker.time;return arguments.length?(e=e||n,s._startTime=r-(r-s._startTime)*s._timeScale/e,s=t._rootFramesTimeline,r=i.ticker.frame,s._startTime=r-(r-s._startTime)*s._timeScale/e,s._timeScale=t._rootTimeline._timeScale=e,e):s._timeScale},h.progress=function(t){return arguments.length?this.totalTime(this.duration()*(this._yoyo&&0!==(1&this._cycle)?1-t:t)+this._cycle*(this._duration+this._repeatDelay),!1):this._time/this.duration()},h.totalProgress=function(t){return arguments.length?this.totalTime(this.totalDuration()*t,!1):this._totalTime/this.totalDuration()},h.time=function(t,e){return arguments.length?(this._dirty&&this.totalDuration(),t>this._duration&&(t=this._duration),this._yoyo&&0!==(1&this._cycle)?t=this._duration-t+this._cycle*(this._duration+this._repeatDelay):0!==this._repeat&&(t+=this._cycle*(this._duration+this._repeatDelay)),this.totalTime(t,e)):this._time},h.duration=function(e){return arguments.length?t.prototype.duration.call(this,e):this._duration},h.totalDuration=function(t){return arguments.length?-1===this._repeat?this:this.duration((t-this._repeat*this._repeatDelay)/(this._repeat+1)):(this._dirty&&(this._totalDuration=-1===this._repeat?999999999999:this._duration*(this._repeat+1)+this._repeatDelay*this._repeat,this._dirty=!1),this._totalDuration)},h.repeat=function(t){return arguments.length?(this._repeat=t,this._uncache(!0)):this._repeat},h.repeatDelay=function(t){return arguments.length?(this._repeatDelay=t,this._uncache(!0)):this._repeatDelay},h.yoyo=function(t){return arguments.length?(this._yoyo=t,this):this._yoyo},r},!0),window._gsDefine("TimelineLite",["core.Animation","core.SimpleTimeline","TweenLite"],function(t,e,i){var s=function(t){e.call(this,t),this._labels={},this.autoRemoveChildren=this.vars.autoRemoveChildren===!0,this.smoothChildTiming=this.vars.smoothChildTiming===!0,this._sortChildren=!0,this._onUpdate=this.vars.onUpdate;var i,s,r=this.vars;for(s in r)i=r[s],a(i)&&-1!==i.join("").indexOf("{self}")&&(r[s]=this._swapSelfInParams(i));a(r.tweens)&&this.add(r.tweens,0,r.align,r.stagger)},r=1e-10,n=i._internals.isSelector,a=i._internals.isArray,o=[],h=function(t){var e,i={};for(e in t)i[e]=t[e];return i},l=function(t,e,i,s){t._timeline.pause(t._startTime),e&&e.apply(s||t._timeline,i||o)},_=o.slice,u=s.prototype=new e;return s.version="1.11.0",u.constructor=s,u.kill()._gc=!1,u.to=function(t,e,s,r){return e?this.add(new i(t,e,s),r):this.set(t,s,r)},u.from=function(t,e,s,r){return this.add(i.from(t,e,s),r)},u.fromTo=function(t,e,s,r,n){return e?this.add(i.fromTo(t,e,s,r),n):this.set(t,r,n)},u.staggerTo=function(t,e,r,a,o,l,u,p){var f,c=new s({onComplete:l,onCompleteParams:u,onCompleteScope:p});for("string"==typeof t&&(t=i.selector(t)||t),n(t)&&(t=_.call(t,0)),a=a||0,f=0;t.length>f;f++)r.startAt&&(r.startAt=h(r.startAt)),c.to(t[f],e,h(r),f*a);return this.add(c,o)},u.staggerFrom=function(t,e,i,s,r,n,a,o){return i.immediateRender=0!=i.immediateRender,i.runBackwards=!0,this.staggerTo(t,e,i,s,r,n,a,o)},u.staggerFromTo=function(t,e,i,s,r,n,a,o,h){return s.startAt=i,s.immediateRender=0!=s.immediateRender&&0!=i.immediateRender,this.staggerTo(t,e,s,r,n,a,o,h)},u.call=function(t,e,s,r){return this.add(i.delayedCall(0,t,e,s),r)},u.set=function(t,e,s){return s=this._parseTimeOrLabel(s,0,!0),null==e.immediateRender&&(e.immediateRender=s===this._time&&!this._paused),this.add(new i(t,0,e),s)},s.exportRoot=function(t,e){t=t||{},null==t.smoothChildTiming&&(t.smoothChildTiming=!0);var r,n,a=new s(t),o=a._timeline;for(null==e&&(e=!0),o._remove(a,!0),a._startTime=0,a._rawPrevTime=a._time=a._totalTime=o._time,r=o._first;r;)n=r._next,e&&r instanceof i&&r.target===r.vars.onComplete||a.add(r,r._startTime-r._delay),r=n;return o.add(a,0),a},u.add=function(r,n,o,h){var l,_,u,p,f,c;if("number"!=typeof n&&(n=this._parseTimeOrLabel(n,0,!0,r)),!(r instanceof t)){if(r instanceof Array||r&&r.push&&a(r)){for(o=o||"normal",h=h||0,l=n,_=r.length,u=0;_>u;u++)a(p=r[u])&&(p=new s({tweens:p})),this.add(p,l),"string"!=typeof p&&"function"!=typeof p&&("sequence"===o?l=p._startTime+p.totalDuration()/p._timeScale:"start"===o&&(p._startTime-=p.delay())),l+=h;return this._uncache(!0)}if("string"==typeof r)return this.addLabel(r,n);if("function"!=typeof r)throw"Cannot add "+r+" into the timeline; it is not a tween, timeline, function, or string.";r=i.delayedCall(0,r)}if(e.prototype.add.call(this,r,n),this._gc&&!this._paused&&this._durationr._startTime;f._gc&&f._timeline;)f._timeline.smoothChildTiming&&c?f.totalTime(f._totalTime,!0):f._enabled(!0,!1),f=f._timeline;return this},u.remove=function(e){if(e instanceof t)return this._remove(e,!1);if(e instanceof Array||e&&e.push&&a(e)){for(var i=e.length;--i>-1;)this.remove(e[i]);return this}return"string"==typeof e?this.removeLabel(e):this.kill(null,e)},u._remove=function(t,i){e.prototype._remove.call(this,t,i);var s=this._last;return s?this._time>s._startTime+s._totalDuration/s._timeScale&&(this._time=this.duration(),this._totalTime=this._totalDuration):this._time=this._totalTime=0,this},u.append=function(t,e){return this.add(t,this._parseTimeOrLabel(null,e,!0,t))},u.insert=u.insertMultiple=function(t,e,i,s){return this.add(t,e||0,i,s)},u.appendMultiple=function(t,e,i,s){return this.add(t,this._parseTimeOrLabel(null,e,!0,t),i,s)},u.addLabel=function(t,e){return this._labels[t]=this._parseTimeOrLabel(e),this},u.addPause=function(t,e,i,s){return this.call(l,["{self}",e,i,s],this,t)},u.removeLabel=function(t){return delete this._labels[t],this},u.getLabelTime=function(t){return null!=this._labels[t]?this._labels[t]:-1},u._parseTimeOrLabel=function(e,i,s,r){var n;if(r instanceof t&&r.timeline===this)this.remove(r);else if(r&&(r instanceof Array||r.push&&a(r)))for(n=r.length;--n>-1;)r[n]instanceof t&&r[n].timeline===this&&this.remove(r[n]);if("string"==typeof i)return this._parseTimeOrLabel(i,s&&"number"==typeof e&&null==this._labels[i]?e-this.duration():0,s);if(i=i||0,"string"!=typeof e||!isNaN(e)&&null==this._labels[e])null==e&&(e=this.duration());else{if(n=e.indexOf("="),-1===n)return null==this._labels[e]?s?this._labels[e]=this.duration()+i:i:this._labels[e]+i;i=parseInt(e.charAt(n-1)+"1",10)*Number(e.substr(n+1)),e=n>1?this._parseTimeOrLabel(e.substr(0,n-1),0,s):this.duration()}return Number(e)+i},u.seek=function(t,e){return this.totalTime("number"==typeof t?t:this._parseTimeOrLabel(t),e!==!1)},u.stop=function(){return this.paused(!0)},u.gotoAndPlay=function(t,e){return this.play(t,e)},u.gotoAndStop=function(t,e){return this.pause(t,e)},u.render=function(t,e,i){this._gc&&this._enabled(!0,!1);var s,n,a,h,l,_=this._dirty?this.totalDuration():this._totalDuration,u=this._time,p=this._startTime,f=this._timeScale,c=this._paused;if(t>=_?(this._totalTime=this._time=_,this._reversed||this._hasPausedChild()||(n=!0,h="onComplete",0===this._duration&&(0===t||0>this._rawPrevTime||this._rawPrevTime===r)&&this._rawPrevTime!==t&&this._first&&(l=!0,this._rawPrevTime>r&&(h="onReverseComplete"))),this._rawPrevTime=this._duration||!e||t?t:r,t=_+1e-6):1e-7>t?(this._totalTime=this._time=0,(0!==u||0===this._duration&&(this._rawPrevTime>r||0>t&&this._rawPrevTime>=0))&&(h="onReverseComplete",n=this._reversed),0>t?(this._active=!1,0===this._duration&&this._rawPrevTime>=0&&this._first&&(l=!0),this._rawPrevTime=t):(this._rawPrevTime=this._duration||!e||t?t:r,t=0,this._initted||(l=!0))):this._totalTime=this._time=this._rawPrevTime=t,this._time!==u&&this._first||i||l){if(this._initted||(this._initted=!0),this._active||!this._paused&&this._time!==u&&t>0&&(this._active=!0),0===u&&this.vars.onStart&&0!==this._time&&(e||this.vars.onStart.apply(this.vars.onStartScope||this,this.vars.onStartParams||o)),this._time>=u)for(s=this._first;s&&(a=s._next,!this._paused||c);)(s._active||s._startTime<=this._time&&!s._paused&&!s._gc)&&(s._reversed?s.render((s._dirty?s.totalDuration():s._totalDuration)-(t-s._startTime)*s._timeScale,e,i):s.render((t-s._startTime)*s._timeScale,e,i)),s=a;else for(s=this._last;s&&(a=s._prev,!this._paused||c);)(s._active||u>=s._startTime&&!s._paused&&!s._gc)&&(s._reversed?s.render((s._dirty?s.totalDuration():s._totalDuration)-(t-s._startTime)*s._timeScale,e,i):s.render((t-s._startTime)*s._timeScale,e,i)),s=a;this._onUpdate&&(e||this._onUpdate.apply(this.vars.onUpdateScope||this,this.vars.onUpdateParams||o)),h&&(this._gc||(p===this._startTime||f!==this._timeScale)&&(0===this._time||_>=this.totalDuration())&&(n&&(this._timeline.autoRemoveChildren&&this._enabled(!1,!1),this._active=!1),!e&&this.vars[h]&&this.vars[h].apply(this.vars[h+"Scope"]||this,this.vars[h+"Params"]||o)))}},u._hasPausedChild=function(){for(var t=this._first;t;){if(t._paused||t instanceof s&&t._hasPausedChild())return!0;t=t._next}return!1},u.getChildren=function(t,e,s,r){r=r||-9999999999;for(var n=[],a=this._first,o=0;a;)r>a._startTime||(a instanceof i?e!==!1&&(n[o++]=a):(s!==!1&&(n[o++]=a),t!==!1&&(n=n.concat(a.getChildren(!0,e,s)),o=n.length))),a=a._next;return n},u.getTweensOf=function(t,e){for(var s=i.getTweensOf(t),r=s.length,n=[],a=0;--r>-1;)(s[r].timeline===this||e&&this._contains(s[r]))&&(n[a++]=s[r]);return n},u._contains=function(t){for(var e=t.timeline;e;){if(e===this)return!0;e=e.timeline}return!1},u.shiftChildren=function(t,e,i){i=i||0;for(var s,r=this._first,n=this._labels;r;)r._startTime>=i&&(r._startTime+=t),r=r._next;if(e)for(s in n)n[s]>=i&&(n[s]+=t);return this._uncache(!0)},u._kill=function(t,e){if(!t&&!e)return this._enabled(!1,!1);for(var i=e?this.getTweensOf(e):this.getChildren(!0,!0,!1),s=i.length,r=!1;--s>-1;)i[s]._kill(t,e)&&(r=!0);return r},u.clear=function(t){var e=this.getChildren(!1,!0,!0),i=e.length;for(this._time=this._totalTime=0;--i>-1;)e[i]._enabled(!1,!1);return t!==!1&&(this._labels={}),this._uncache(!0)},u.invalidate=function(){for(var t=this._first;t;)t.invalidate(),t=t._next;return this},u._enabled=function(t,i){if(t===this._gc)for(var s=this._first;s;)s._enabled(t,!0),s=s._next;return e.prototype._enabled.call(this,t,i)},u.duration=function(t){return arguments.length?(0!==this.duration()&&0!==t&&this.timeScale(this._duration/t),this):(this._dirty&&this.totalDuration(),this._duration)},u.totalDuration=function(t){if(!arguments.length){if(this._dirty){for(var e,i,s=0,r=this._last,n=999999999999;r;)e=r._prev,r._dirty&&r.totalDuration(),r._startTime>n&&this._sortChildren&&!r._paused?this.add(r,r._startTime-r._delay):n=r._startTime,0>r._startTime&&!r._paused&&(s-=r._startTime,this._timeline.smoothChildTiming&&(this._startTime+=r._startTime/this._timeScale),this.shiftChildren(-r._startTime,!1,-9999999999),n=0),i=r._startTime+r._totalDuration/r._timeScale,i>s&&(s=i),r=e;this._duration=this._totalDuration=s,this._dirty=!1}return this._totalDuration}return 0!==this.totalDuration()&&0!==t&&this.timeScale(this._totalDuration/t),this},u.usesFrames=function(){for(var e=this._timeline;e._timeline;)e=e._timeline;return e===t._rootFramesTimeline},u.rawTime=function(){return this._paused?this._totalTime:(this._timeline.rawTime()-this._startTime)*this._timeScale},s},!0),window._gsDefine("TimelineMax",["TimelineLite","TweenLite","easing.Ease"],function(t,e,i){var s=function(e){t.call(this,e),this._repeat=this.vars.repeat||0,this._repeatDelay=this.vars.repeatDelay||0,this._cycle=0,this._yoyo=this.vars.yoyo===!0,this._dirty=!0},r=1e-10,n=[],a=new i(null,null,1,0),o=s.prototype=new t;return o.constructor=s,o.kill()._gc=!1,s.version="1.11.0",o.invalidate=function(){return this._yoyo=this.vars.yoyo===!0,this._repeat=this.vars.repeat||0,this._repeatDelay=this.vars.repeatDelay||0,this._uncache(!0),t.prototype.invalidate.call(this)},o.addCallback=function(t,i,s,r){return this.add(e.delayedCall(0,t,s,r),i)},o.removeCallback=function(t,e){if(t)if(null==e)this._kill(null,t);else for(var i=this.getTweensOf(t,!1),s=i.length,r=this._parseTimeOrLabel(e);--s>-1;)i[s]._startTime===r&&i[s]._enabled(!1,!1);return this},o.tweenTo=function(t,i){i=i||{};var s,r,o={ease:a,overwrite:2,useFrames:this.usesFrames(),immediateRender:!1};for(s in i)o[s]=i[s];return o.time=this._parseTimeOrLabel(t),r=new e(this,Math.abs(Number(o.time)-this._time)/this._timeScale||.001,o),o.onStart=function(){r.target.paused(!0),r.vars.time!==r.target.time()&&r.duration(Math.abs(r.vars.time-r.target.time())/r.target._timeScale),i.onStart&&i.onStart.apply(i.onStartScope||r,i.onStartParams||n)},r},o.tweenFromTo=function(t,e,i){i=i||{},t=this._parseTimeOrLabel(t),i.startAt={onComplete:this.seek,onCompleteParams:[t],onCompleteScope:this},i.immediateRender=i.immediateRender!==!1;var s=this.tweenTo(e,i);return s.duration(Math.abs(s.vars.time-t)/this._timeScale||.001)},o.render=function(t,e,i){this._gc&&this._enabled(!0,!1);var s,a,o,h,l,_,u=this._dirty?this.totalDuration():this._totalDuration,p=this._duration,f=this._time,c=this._totalTime,m=this._startTime,d=this._timeScale,g=this._rawPrevTime,v=this._paused,y=this._cycle;if(t>=u?(this._locked||(this._totalTime=u,this._cycle=this._repeat),this._reversed||this._hasPausedChild()||(a=!0,h="onComplete",0===this._duration&&(0===t||0>g||g===r)&&g!==t&&this._first&&(l=!0,g>r&&(h="onReverseComplete"))),this._rawPrevTime=this._duration||!e||t?t:r,this._yoyo&&0!==(1&this._cycle)?this._time=t=0:(this._time=p,t=p+1e-6)):1e-7>t?(this._locked||(this._totalTime=this._cycle=0),this._time=0,(0!==f||0===p&&(g>r||0>t&&g>=0)&&!this._locked)&&(h="onReverseComplete",a=this._reversed),0>t?(this._active=!1,0===p&&g>=0&&this._first&&(l=!0),this._rawPrevTime=t):(this._rawPrevTime=p||!e||t?t:r,t=0,this._initted||(l=!0))):(0===p&&0>g&&(l=!0),this._time=this._rawPrevTime=t,this._locked||(this._totalTime=t,0!==this._repeat&&(_=p+this._repeatDelay,this._cycle=this._totalTime/_>>0,0!==this._cycle&&this._cycle===this._totalTime/_&&this._cycle--,this._time=this._totalTime-this._cycle*_,this._yoyo&&0!==(1&this._cycle)&&(this._time=p-this._time),this._time>p?(this._time=p,t=p+1e-6):0>this._time?this._time=t=0:t=this._time))),this._cycle!==y&&!this._locked){var T=this._yoyo&&0!==(1&y),w=T===(this._yoyo&&0!==(1&this._cycle)),x=this._totalTime,b=this._cycle,P=this._rawPrevTime,S=this._time;if(this._totalTime=y*p,y>this._cycle?T=!T:this._totalTime+=p,this._time=f,this._rawPrevTime=0===p?g-1e-5:g,this._cycle=y,this._locked=!0,f=T?0:p,this.render(f,e,0===p),e||this._gc||this.vars.onRepeat&&this.vars.onRepeat.apply(this.vars.onRepeatScope||this,this.vars.onRepeatParams||n),w&&(f=T?p+1e-6:-1e-6,this.render(f,!0,!1)),this._locked=!1,this._paused&&!v)return;this._time=S,this._totalTime=x,this._cycle=b,this._rawPrevTime=P}if(!(this._time!==f&&this._first||i||l))return c!==this._totalTime&&this._onUpdate&&(e||this._onUpdate.apply(this.vars.onUpdateScope||this,this.vars.onUpdateParams||n)),void 0;if(this._initted||(this._initted=!0),this._active||!this._paused&&this._totalTime!==c&&t>0&&(this._active=!0),0===c&&this.vars.onStart&&0!==this._totalTime&&(e||this.vars.onStart.apply(this.vars.onStartScope||this,this.vars.onStartParams||n)),this._time>=f)for(s=this._first;s&&(o=s._next,!this._paused||v);)(s._active||s._startTime<=this._time&&!s._paused&&!s._gc)&&(s._reversed?s.render((s._dirty?s.totalDuration():s._totalDuration)-(t-s._startTime)*s._timeScale,e,i):s.render((t-s._startTime)*s._timeScale,e,i)),s=o;else for(s=this._last;s&&(o=s._prev,!this._paused||v);)(s._active||f>=s._startTime&&!s._paused&&!s._gc)&&(s._reversed?s.render((s._dirty?s.totalDuration():s._totalDuration)-(t-s._startTime)*s._timeScale,e,i):s.render((t-s._startTime)*s._timeScale,e,i)),s=o;this._onUpdate&&(e||this._onUpdate.apply(this.vars.onUpdateScope||this,this.vars.onUpdateParams||n)),h&&(this._locked||this._gc||(m===this._startTime||d!==this._timeScale)&&(0===this._time||u>=this.totalDuration())&&(a&&(this._timeline.autoRemoveChildren&&this._enabled(!1,!1),this._active=!1),!e&&this.vars[h]&&this.vars[h].apply(this.vars[h+"Scope"]||this,this.vars[h+"Params"]||n)))},o.getActive=function(t,e,i){null==t&&(t=!0),null==e&&(e=!0),null==i&&(i=!1);var s,r,n=[],a=this.getChildren(t,e,i),o=0,h=a.length;for(s=0;h>s;s++)r=a[s],r.isActive()&&(n[o++]=r);return n},o.getLabelAfter=function(t){t||0!==t&&(t=this._time);var e,i=this.getLabelsArray(),s=i.length;for(e=0;s>e;e++)if(i[e].time>t)return i[e].name;return null},o.getLabelBefore=function(t){null==t&&(t=this._time);for(var e=this.getLabelsArray(),i=e.length;--i>-1;)if(t>e[i].time)return e[i].name;return null},o.getLabelsArray=function(){var t,e=[],i=0;for(t in this._labels)e[i++]={time:this._labels[t],name:t};return e.sort(function(t,e){return t.time-e.time}),e},o.progress=function(t){return arguments.length?this.totalTime(this.duration()*(this._yoyo&&0!==(1&this._cycle)?1-t:t)+this._cycle*(this._duration+this._repeatDelay),!1):this._time/this.duration()},o.totalProgress=function(t){return arguments.length?this.totalTime(this.totalDuration()*t,!1):this._totalTime/this.totalDuration()},o.totalDuration=function(e){return arguments.length?-1===this._repeat?this:this.duration((e-this._repeat*this._repeatDelay)/(this._repeat+1)):(this._dirty&&(t.prototype.totalDuration.call(this),this._totalDuration=-1===this._repeat?999999999999:this._duration*(this._repeat+1)+this._repeatDelay*this._repeat),this._totalDuration)},o.time=function(t,e){return arguments.length?(this._dirty&&this.totalDuration(),t>this._duration&&(t=this._duration),this._yoyo&&0!==(1&this._cycle)?t=this._duration-t+this._cycle*(this._duration+this._repeatDelay):0!==this._repeat&&(t+=this._cycle*(this._duration+this._repeatDelay)),this.totalTime(t,e)):this._time},o.repeat=function(t){return arguments.length?(this._repeat=t,this._uncache(!0)):this._repeat},o.repeatDelay=function(t){return arguments.length?(this._repeatDelay=t,this._uncache(!0)):this._repeatDelay},o.yoyo=function(t){return arguments.length?(this._yoyo=t,this):this._yoyo},o.currentLabel=function(t){return arguments.length?this.seek(t,!0):this.getLabelBefore(this._time+1e-8)},s},!0),function(){var t=180/Math.PI,e=[],i=[],s=[],r={},n=function(t,e,i,s){this.a=t,this.b=e,this.c=i,this.d=s,this.da=s-t,this.ca=i-t,this.ba=e-t},a=",x,y,z,left,top,right,bottom,marginTop,marginLeft,marginRight,marginBottom,paddingLeft,paddingTop,paddingRight,paddingBottom,backgroundPosition,backgroundPosition_y,",o=function(t,e,i,s){var r={a:t},n={},a={},o={c:s},h=(t+e)/2,l=(e+i)/2,_=(i+s)/2,u=(h+l)/2,p=(l+_)/2,f=(p-u)/8;return r.b=h+(t-h)/4,n.b=u+f,r.c=n.a=(r.b+n.b)/2,n.c=a.a=(u+p)/2,a.b=p-f,o.b=_+(s-_)/4,a.c=o.a=(a.b+o.b)/2,[r,n,a,o]},h=function(t,r,n,a,h){var l,_,u,p,f,c,m,d,g,v,y,T,w,x=t.length-1,b=0,P=t[0].a;for(l=0;x>l;l++)f=t[b],_=f.a,u=f.d,p=t[b+1].d,h?(y=e[l],T=i[l],w=.25*(T+y)*r/(a?.5:s[l]||.5),c=u-(u-_)*(a?.5*r:0!==y?w/y:0),m=u+(p-u)*(a?.5*r:0!==T?w/T:0),d=u-(c+((m-c)*(3*y/(y+T)+.5)/4||0))):(c=u-.5*(u-_)*r,m=u+.5*(p-u)*r,d=u-(c+m)/2),c+=d,m+=d,f.c=g=c,f.b=0!==l?P:P=f.a+.6*(f.c-f.a),f.da=u-_,f.ca=g-_,f.ba=P-_,n?(v=o(_,P,g,u),t.splice(b,1,v[0],v[1],v[2],v[3]),b+=4):b++,P=m;f=t[b],f.b=P,f.c=P+.4*(f.d-P),f.da=f.d-f.a,f.ca=f.c-f.a,f.ba=P-f.a,n&&(v=o(f.a,P,f.c,f.d),t.splice(b,1,v[0],v[1],v[2],v[3]))},l=function(t,s,r,a){var o,h,l,_,u,p,f=[];if(a)for(t=[a].concat(t),h=t.length;--h>-1;)"string"==typeof(p=t[h][s])&&"="===p.charAt(1)&&(t[h][s]=a[s]+Number(p.charAt(0)+p.substr(2)));if(o=t.length-2,0>o)return f[0]=new n(t[0][s],0,0,t[-1>o?0:1][s]),f;for(h=0;o>h;h++)l=t[h][s],_=t[h+1][s],f[h]=new n(l,0,0,_),r&&(u=t[h+2][s],e[h]=(e[h]||0)+(_-l)*(_-l),i[h]=(i[h]||0)+(u-_)*(u-_));return f[h]=new n(t[h][s],0,0,t[h+1][s]),f},_=function(t,n,o,_,u,p){var f,c,m,d,g,v,y,T,w={},x=[],b=p||t[0];u="string"==typeof u?","+u+",":a,null==n&&(n=1);for(c in t[0])x.push(c);if(t.length>1){for(T=t[t.length-1],y=!0,f=x.length;--f>-1;)if(c=x[f],Math.abs(b[c]-T[c])>.05){y=!1;break}y&&(t=t.concat(),p&&t.unshift(p),t.push(t[1]),p=t[t.length-3])}for(e.length=i.length=s.length=0,f=x.length;--f>-1;)c=x[f],r[c]=-1!==u.indexOf(","+c+","),w[c]=l(t,c,r[c],p);for(f=e.length;--f>-1;)e[f]=Math.sqrt(e[f]),i[f]=Math.sqrt(i[f]);if(!_){for(f=x.length;--f>-1;)if(r[c])for(m=w[x[f]],v=m.length-1,d=0;v>d;d++)g=m[d+1].da/i[d]+m[d].da/e[d],s[d]=(s[d]||0)+g*g;for(f=s.length;--f>-1;)s[f]=Math.sqrt(s[f])}for(f=x.length,d=o?4:1;--f>-1;)c=x[f],m=w[c],h(m,n,o,_,r[c]),y&&(m.splice(0,d),m.splice(m.length-d,d));return w},u=function(t,e,i){e=e||"soft";var s,r,a,o,h,l,_,u,p,f,c,m={},d="cubic"===e?3:2,g="soft"===e,v=[];if(g&&i&&(t=[i].concat(t)),null==t||d+1>t.length)throw"invalid Bezier data";for(p in t[0])v.push(p);for(l=v.length;--l>-1;){for(p=v[l],m[p]=h=[],f=0,u=t.length,_=0;u>_;_++)s=null==i?t[_][p]:"string"==typeof(c=t[_][p])&&"="===c.charAt(1)?i[p]+Number(c.charAt(0)+c.substr(2)):Number(c),g&&_>1&&u-1>_&&(h[f++]=(s+h[f-2])/2),h[f++]=s;for(u=f-d+1,f=0,_=0;u>_;_+=d)s=h[_],r=h[_+1],a=h[_+2],o=2===d?0:h[_+3],h[f++]=c=3===d?new n(s,r,a,o):new n(s,(2*r+s)/3,(2*r+a)/3,a);h.length=f}return m},p=function(t,e,i){for(var s,r,n,a,o,h,l,_,u,p,f,c=1/i,m=t.length;--m>-1;)for(p=t[m],n=p.a,a=p.d-n,o=p.c-n,h=p.b-n,s=r=0,_=1;i>=_;_++)l=c*_,u=1-l,s=r-(r=(l*l*a+3*u*(l*o+u*h))*l),f=m*i+_-1,e[f]=(e[f]||0)+s*s},f=function(t,e){e=e>>0||6;var i,s,r,n,a=[],o=[],h=0,l=0,_=e-1,u=[],f=[];for(i in t)p(t[i],a,e);for(r=a.length,s=0;r>s;s++)h+=Math.sqrt(a[s]),n=s%e,f[n]=h,n===_&&(l+=h,n=s/e>>0,u[n]=f,o[n]=l,h=0,f=[]);return{length:l,lengths:o,segments:u}},c=window._gsDefine.plugin({propName:"bezier",priority:-1,API:2,global:!0,init:function(t,e,i){this._target=t,e instanceof Array&&(e={values:e}),this._func={},this._round={},this._props=[],this._timeRes=null==e.timeResolution?6:parseInt(e.timeResolution,10);var s,r,n,a,o,h=e.values||[],l={},p=h[0],c=e.autoRotate||i.vars.orientToBezier;this._autoRotate=c?c instanceof Array?c:[["x","y","rotation",c===!0?0:Number(c)||0]]:null;for(s in p)this._props.push(s);for(n=this._props.length;--n>-1;)s=this._props[n],this._overwriteProps.push(s),r=this._func[s]="function"==typeof t[s],l[s]=r?t[s.indexOf("set")||"function"!=typeof t["get"+s.substr(3)]?s:"get"+s.substr(3)]():parseFloat(t[s]),o||l[s]!==h[0][s]&&(o=l);if(this._beziers="cubic"!==e.type&&"quadratic"!==e.type&&"soft"!==e.type?_(h,isNaN(e.curviness)?1:e.curviness,!1,"thruBasic"===e.type,e.correlate,o):u(h,e.type,l),this._segCount=this._beziers[s].length,this._timeRes){var m=f(this._beziers,this._timeRes);this._length=m.length,this._lengths=m.lengths,this._segments=m.segments,this._l1=this._li=this._s1=this._si=0,this._l2=this._lengths[0],this._curSeg=this._segments[0],this._s2=this._curSeg[0],this._prec=1/this._curSeg.length}if(c=this._autoRotate)for(c[0]instanceof Array||(this._autoRotate=c=[c]),n=c.length;--n>-1;)for(a=0;3>a;a++)s=c[n][a],this._func[s]="function"==typeof t[s]?t[s.indexOf("set")||"function"!=typeof t["get"+s.substr(3)]?s:"get"+s.substr(3)]:!1;return!0},set:function(e){var i,s,r,n,a,o,h,l,_,u,p=this._segCount,f=this._func,c=this._target;if(this._timeRes){if(_=this._lengths,u=this._curSeg,e*=this._length,r=this._li,e>this._l2&&p-1>r){for(l=p-1;l>r&&e>=(this._l2=_[++r]););this._l1=_[r-1],this._li=r,this._curSeg=u=this._segments[r],this._s2=u[this._s1=this._si=0]}else if(this._l1>e&&r>0){for(;r>0&&(this._l1=_[--r])>=e;);0===r&&this._l1>e?this._l1=0:r++,this._l2=_[r],this._li=r,this._curSeg=u=this._segments[r],this._s1=u[(this._si=u.length-1)-1]||0,this._s2=u[this._si]}if(i=r,e-=this._l1,r=this._si,e>this._s2&&u.length-1>r){for(l=u.length-1;l>r&&e>=(this._s2=u[++r]););this._s1=u[r-1],this._si=r}else if(this._s1>e&&r>0){for(;r>0&&(this._s1=u[--r])>=e;);0===r&&this._s1>e?this._s1=0:r++,this._s2=u[r],this._si=r}o=(r+(e-this._s1)/(this._s2-this._s1))*this._prec}else i=0>e?0:e>=1?p-1:p*e>>0,o=(e-i*(1/p))*p;for(s=1-o,r=this._props.length;--r>-1;)n=this._props[r],a=this._beziers[n][i],h=(o*o*a.da+3*s*(o*a.ca+s*a.ba))*o+a.a,this._round[n]&&(h=h+(h>0?.5:-.5)>>0),f[n]?c[n](h):c[n]=h;if(this._autoRotate){var m,d,g,v,y,T,w,x=this._autoRotate;for(r=x.length;--r>-1;)n=x[r][2],T=x[r][3]||0,w=x[r][4]===!0?1:t,a=this._beziers[x[r][0]],m=this._beziers[x[r][1]],a&&m&&(a=a[i],m=m[i],d=a.a+(a.b-a.a)*o,v=a.b+(a.c-a.b)*o,d+=(v-d)*o,v+=(a.c+(a.d-a.c)*o-v)*o,g=m.a+(m.b-m.a)*o,y=m.b+(m.c-m.b)*o,g+=(y-g)*o,y+=(m.c+(m.d-m.c)*o-y)*o,h=Math.atan2(y-g,v-d)*w+T,f[n]?c[n](h):c[n]=h)}}}),m=c.prototype;c.bezierThrough=_,c.cubicToQuadratic=o,c._autoCSS=!0,c.quadraticToCubic=function(t,e,i){return new n(t,(2*e+t)/3,(2*e+i)/3,i)},c._cssRegister=function(){var t=window._gsDefine.globals.CSSPlugin;if(t){var e=t._internals,i=e._parseToProxy,s=e._setPluginRatio,r=e.CSSPropTween;e._registerComplexSpecialProp("bezier",{parser:function(t,e,n,a,o,h){e instanceof Array&&(e={values:e}),h=new c;var l,_,u,p=e.values,f=p.length-1,m=[],d={};if(0>f)return o;for(l=0;f>=l;l++)u=i(t,p[l],a,o,h,f!==l),m[l]=u.end;for(_ in e)d[_]=e[_];return d.values=m,o=new r(t,"bezier",0,0,u.pt,2),o.data=u,o.plugin=h,o.setRatio=s,0===d.autoRotate&&(d.autoRotate=!0),!d.autoRotate||d.autoRotate instanceof Array||(l=d.autoRotate===!0?0:Number(d.autoRotate),d.autoRotate=null!=u.end.left?[["left","top","rotation",l,!1]]:null!=u.end.x?[["x","y","rotation",l,!1]]:!1),d.autoRotate&&(a._transform||a._enableTransforms(!1),u.autoRotate=a._target._gsTransform),h._onInitTween(u.proxy,d,a._tween),o}})}},m._roundProps=function(t,e){for(var i=this._overwriteProps,s=i.length;--s>-1;)(t[i[s]]||t.bezier||t.bezierThrough)&&(this._round[i[s]]=e)},m._kill=function(t){var e,i,s=this._props; +for(e in this._beziers)if(e in t)for(delete this._beziers[e],delete this._func[e],i=s.length;--i>-1;)s[i]===e&&s.splice(i,1);return this._super._kill.call(this,t)}}(),window._gsDefine("plugins.CSSPlugin",["plugins.TweenPlugin","TweenLite"],function(t,e){var i,s,r,n,a=function(){t.call(this,"css"),this._overwriteProps.length=0,this.setRatio=a.prototype.setRatio},o={},h=a.prototype=new t("css");h.constructor=a,a.version="1.11.0",a.API=2,a.defaultTransformPerspective=0,h="px",a.suffixMap={top:h,right:h,bottom:h,left:h,width:h,height:h,fontSize:h,padding:h,margin:h,perspective:h};var l,_,u,p,f,c,m=/(?:\d|\-\d|\.\d|\-\.\d)+/g,d=/(?:\d|\-\d|\.\d|\-\.\d|\+=\d|\-=\d|\+=.\d|\-=\.\d)+/g,g=/(?:\+=|\-=|\-|\b)[\d\-\.]+[a-zA-Z0-9]*(?:%|\b)/gi,v=/[^\d\-\.]/g,y=/(?:\d|\-|\+|=|#|\.)*/g,T=/opacity *= *([^)]*)/,w=/opacity:([^;]*)/,x=/alpha\(opacity *=.+?\)/i,b=/^(rgb|hsl)/,P=/([A-Z])/g,S=/-([a-z])/gi,k=/(^(?:url\(\"|url\())|(?:(\"\))$|\)$)/gi,R=function(t,e){return e.toUpperCase()},A=/(?:Left|Right|Width)/i,C=/(M11|M12|M21|M22)=[\d\-\.e]+/gi,O=/progid\:DXImageTransform\.Microsoft\.Matrix\(.+?\)/i,D=/,(?=[^\)]*(?:\(|$))/gi,M=Math.PI/180,I=180/Math.PI,F={},E=document,N=E.createElement("div"),L=E.createElement("img"),X=a._internals={_specialProps:o},z=navigator.userAgent,U=function(){var t,e=z.indexOf("Android"),i=E.createElement("div");return u=-1!==z.indexOf("Safari")&&-1===z.indexOf("Chrome")&&(-1===e||Number(z.substr(e+8,1))>3),f=u&&6>Number(z.substr(z.indexOf("Version/")+8,1)),p=-1!==z.indexOf("Firefox"),/MSIE ([0-9]{1,}[\.0-9]{0,})/.exec(z),c=parseFloat(RegExp.$1),i.innerHTML="a",t=i.getElementsByTagName("a")[0],t?/^0.55/.test(t.style.opacity):!1}(),Y=function(t){return T.test("string"==typeof t?t:(t.currentStyle?t.currentStyle.filter:t.style.filter)||"")?parseFloat(RegExp.$1)/100:1},j=function(t){window.console&&console.log(t)},B="",q="",V=function(t,e){e=e||N;var i,s,r=e.style;if(void 0!==r[t])return t;for(t=t.charAt(0).toUpperCase()+t.substr(1),i=["O","Moz","ms","Ms","Webkit"],s=5;--s>-1&&void 0===r[i[s]+t];);return s>=0?(q=3===s?"ms":i[s],B="-"+q.toLowerCase()+"-",q+t):null},Z=E.defaultView?E.defaultView.getComputedStyle:function(){},G=a.getStyle=function(t,e,i,s,r){var n;return U||"opacity"!==e?(!s&&t.style[e]?n=t.style[e]:(i=i||Z(t,null))?(t=i.getPropertyValue(e.replace(P,"-$1").toLowerCase()),n=t||i.length?t:i[e]):t.currentStyle&&(n=t.currentStyle[e]),null==r||n&&"none"!==n&&"auto"!==n&&"auto auto"!==n?n:r):Y(t)},$=function(t,e,i,s,r){if("px"===s||!s)return i;if("auto"===s||!i)return 0;var n,a=A.test(e),o=t,h=N.style,l=0>i;return l&&(i=-i),"%"===s&&-1!==e.indexOf("border")?n=i/100*(a?t.clientWidth:t.clientHeight):(h.cssText="border-style:solid;border-width:0;position:absolute;line-height:0;","%"!==s&&o.appendChild?h[a?"borderLeftWidth":"borderTopWidth"]=i+s:(o=t.parentNode||E.body,h[a?"width":"height"]=i+s),o.appendChild(N),n=parseFloat(N[a?"offsetWidth":"offsetHeight"]),o.removeChild(N),0!==n||r||(n=$(t,e,i,s,!0))),l?-n:n},Q=function(t,e,i){if("absolute"!==G(t,"position",i))return 0;var s="left"===e?"Left":"Top",r=G(t,"margin"+s,i);return t["offset"+s]-($(t,e,parseFloat(r),r.replace(y,""))||0)},W=function(t,e){var i,s,r={};if(e=e||Z(t,null))if(i=e.length)for(;--i>-1;)r[e[i].replace(S,R)]=e.getPropertyValue(e[i]);else for(i in e)r[i]=e[i];else if(e=t.currentStyle||t.style)for(i in e)"string"==typeof i&&void 0!==r[i]&&(r[i.replace(S,R)]=e[i]);return U||(r.opacity=Y(t)),s=be(t,e,!1),r.rotation=s.rotation,r.skewX=s.skewX,r.scaleX=s.scaleX,r.scaleY=s.scaleY,r.x=s.x,r.y=s.y,xe&&(r.z=s.z,r.rotationX=s.rotationX,r.rotationY=s.rotationY,r.scaleZ=s.scaleZ),r.filters&&delete r.filters,r},H=function(t,e,i,s,r){var n,a,o,h={},l=t.style;for(a in i)"cssText"!==a&&"length"!==a&&isNaN(a)&&(e[a]!==(n=i[a])||r&&r[a])&&-1===a.indexOf("Origin")&&("number"==typeof n||"string"==typeof n)&&(h[a]="auto"!==n||"left"!==a&&"top"!==a?""!==n&&"auto"!==n&&"none"!==n||"string"!=typeof e[a]||""===e[a].replace(v,"")?n:0:Q(t,a),void 0!==l[a]&&(o=new ue(l,a,l[a],o)));if(s)for(a in s)"className"!==a&&(h[a]=s[a]);return{difs:h,firstMPT:o}},K={width:["Left","Right"],height:["Top","Bottom"]},J=["marginLeft","marginRight","marginTop","marginBottom"],te=function(t,e,i){var s=parseFloat("width"===e?t.offsetWidth:t.offsetHeight),r=K[e],n=r.length;for(i=i||Z(t,null);--n>-1;)s-=parseFloat(G(t,"padding"+r[n],i,!0))||0,s-=parseFloat(G(t,"border"+r[n]+"Width",i,!0))||0;return s},ee=function(t,e){(null==t||""===t||"auto"===t||"auto auto"===t)&&(t="0 0");var i=t.split(" "),s=-1!==t.indexOf("left")?"0%":-1!==t.indexOf("right")?"100%":i[0],r=-1!==t.indexOf("top")?"0%":-1!==t.indexOf("bottom")?"100%":i[1];return null==r?r="0":"center"===r&&(r="50%"),("center"===s||isNaN(parseFloat(s))&&-1===(s+"").indexOf("="))&&(s="50%"),e&&(e.oxp=-1!==s.indexOf("%"),e.oyp=-1!==r.indexOf("%"),e.oxr="="===s.charAt(1),e.oyr="="===r.charAt(1),e.ox=parseFloat(s.replace(v,"")),e.oy=parseFloat(r.replace(v,""))),s+" "+r+(i.length>2?" "+i[2]:"")},ie=function(t,e){return"string"==typeof t&&"="===t.charAt(1)?parseInt(t.charAt(0)+"1",10)*parseFloat(t.substr(2)):parseFloat(t)-parseFloat(e)},se=function(t,e){return null==t?e:"string"==typeof t&&"="===t.charAt(1)?parseInt(t.charAt(0)+"1",10)*Number(t.substr(2))+e:parseFloat(t)},re=function(t,e,i,s){var r,n,a,o,h=1e-6;return null==t?o=e:"number"==typeof t?o=t:(r=360,n=t.split("_"),a=Number(n[0].replace(v,""))*(-1===t.indexOf("rad")?1:I)-("="===t.charAt(1)?0:e),n.length&&(s&&(s[i]=e+a),-1!==t.indexOf("short")&&(a%=r,a!==a%(r/2)&&(a=0>a?a+r:a-r)),-1!==t.indexOf("_cw")&&0>a?a=(a+9999999999*r)%r-(0|a/r)*r:-1!==t.indexOf("ccw")&&a>0&&(a=(a-9999999999*r)%r-(0|a/r)*r)),o=e+a),h>o&&o>-h&&(o=0),o},ne={aqua:[0,255,255],lime:[0,255,0],silver:[192,192,192],black:[0,0,0],maroon:[128,0,0],teal:[0,128,128],blue:[0,0,255],navy:[0,0,128],white:[255,255,255],fuchsia:[255,0,255],olive:[128,128,0],yellow:[255,255,0],orange:[255,165,0],gray:[128,128,128],purple:[128,0,128],green:[0,128,0],red:[255,0,0],pink:[255,192,203],cyan:[0,255,255],transparent:[255,255,255,0]},ae=function(t,e,i){return t=0>t?t+1:t>1?t-1:t,0|255*(1>6*t?e+6*(i-e)*t:.5>t?i:2>3*t?e+6*(i-e)*(2/3-t):e)+.5},oe=function(t){var e,i,s,r,n,a;return t&&""!==t?"number"==typeof t?[t>>16,255&t>>8,255&t]:(","===t.charAt(t.length-1)&&(t=t.substr(0,t.length-1)),ne[t]?ne[t]:"#"===t.charAt(0)?(4===t.length&&(e=t.charAt(1),i=t.charAt(2),s=t.charAt(3),t="#"+e+e+i+i+s+s),t=parseInt(t.substr(1),16),[t>>16,255&t>>8,255&t]):"hsl"===t.substr(0,3)?(t=t.match(m),r=Number(t[0])%360/360,n=Number(t[1])/100,a=Number(t[2])/100,i=.5>=a?a*(n+1):a+n-a*n,e=2*a-i,t.length>3&&(t[3]=Number(t[3])),t[0]=ae(r+1/3,e,i),t[1]=ae(r,e,i),t[2]=ae(r-1/3,e,i),t):(t=t.match(m)||ne.transparent,t[0]=Number(t[0]),t[1]=Number(t[1]),t[2]=Number(t[2]),t.length>3&&(t[3]=Number(t[3])),t)):ne.black},he="(?:\\b(?:(?:rgb|rgba|hsl|hsla)\\(.+?\\))|\\B#.+?\\b";for(h in ne)he+="|"+h+"\\b";he=RegExp(he+")","gi");var le=function(t,e,i,s){if(null==t)return function(t){return t};var r,n=e?(t.match(he)||[""])[0]:"",a=t.split(n).join("").match(g)||[],o=t.substr(0,t.indexOf(a[0])),h=")"===t.charAt(t.length-1)?")":"",l=-1!==t.indexOf(" ")?" ":",",_=a.length,u=_>0?a[0].replace(m,""):"";return _?r=e?function(t){var e,p,f,c;if("number"==typeof t)t+=u;else if(s&&D.test(t)){for(c=t.replace(D,"|").split("|"),f=0;c.length>f;f++)c[f]=r(c[f]);return c.join(",")}if(e=(t.match(he)||[n])[0],p=t.split(e).join("").match(g)||[],f=p.length,_>f--)for(;_>++f;)p[f]=i?p[0|(f-1)/2]:a[f];return o+p.join(l)+l+e+h+(-1!==t.indexOf("inset")?" inset":"")}:function(t){var e,n,p;if("number"==typeof t)t+=u;else if(s&&D.test(t)){for(n=t.replace(D,"|").split("|"),p=0;n.length>p;p++)n[p]=r(n[p]);return n.join(",")}if(e=t.match(g)||[],p=e.length,_>p--)for(;_>++p;)e[p]=i?e[0|(p-1)/2]:a[p];return o+e.join(l)+h}:function(t){return t}},_e=function(t){return t=t.split(","),function(e,i,s,r,n,a,o){var h,l=(i+"").split(" ");for(o={},h=0;4>h;h++)o[t[h]]=l[h]=l[h]||l[(h-1)/2>>0];return r.parse(e,o,n,a)}},ue=(X._setPluginRatio=function(t){this.plugin.setRatio(t);for(var e,i,s,r,n=this.data,a=n.proxy,o=n.firstMPT,h=1e-6;o;)e=a[o.v],o.r?e=e>0?0|e+.5:0|e-.5:h>e&&e>-h&&(e=0),o.t[o.p]=e,o=o._next;if(n.autoRotate&&(n.autoRotate.rotation=a.rotation),1===t)for(o=n.firstMPT;o;){if(i=o.t,i.type){if(1===i.type){for(r=i.xs0+i.s+i.xs1,s=1;i.l>s;s++)r+=i["xn"+s]+i["xs"+(s+1)];i.e=r}}else i.e=i.s+i.xs0;o=o._next}},function(t,e,i,s,r){this.t=t,this.p=e,this.v=i,this.r=r,s&&(s._prev=this,this._next=s)}),pe=(X._parseToProxy=function(t,e,i,s,r,n){var a,o,h,l,_,u=s,p={},f={},c=i._transform,m=F;for(i._transform=null,F=e,s=_=i.parse(t,e,s,r),F=m,n&&(i._transform=c,u&&(u._prev=null,u._prev&&(u._prev._next=null)));s&&s!==u;){if(1>=s.type&&(o=s.p,f[o]=s.s+s.c,p[o]=s.s,n||(l=new ue(s,"s",o,l,s.r),s.c=0),1===s.type))for(a=s.l;--a>0;)h="xn"+a,o=s.p+"_"+h,f[o]=s.data[h],p[o]=s[h],n||(l=new ue(s,h,o,l,s.rxp[h]));s=s._next}return{proxy:p,end:f,firstMPT:l,pt:_}},X.CSSPropTween=function(t,e,s,r,a,o,h,l,_,u,p){this.t=t,this.p=e,this.s=s,this.c=r,this.n=h||e,t instanceof pe||n.push(this.n),this.r=l,this.type=o||0,_&&(this.pr=_,i=!0),this.b=void 0===u?s:u,this.e=void 0===p?s+r:p,a&&(this._next=a,a._prev=this)}),fe=a.parseComplex=function(t,e,i,s,r,n,a,o,h,_){i=i||n||"",a=new pe(t,e,0,0,a,_?2:1,null,!1,o,i,s),s+="";var u,p,f,c,g,v,y,T,w,x,P,S,k=i.split(", ").join(",").split(" "),R=s.split(", ").join(",").split(" "),A=k.length,C=l!==!1;for((-1!==s.indexOf(",")||-1!==i.indexOf(","))&&(k=k.join(" ").replace(D,", ").split(" "),R=R.join(" ").replace(D,", ").split(" "),A=k.length),A!==R.length&&(k=(n||"").split(" "),A=k.length),a.plugin=h,a.setRatio=_,u=0;A>u;u++)if(c=k[u],g=R[u],T=parseFloat(c),T||0===T)a.appendXtra("",T,ie(g,T),g.replace(d,""),C&&-1!==g.indexOf("px"),!0);else if(r&&("#"===c.charAt(0)||ne[c]||b.test(c)))S=","===g.charAt(g.length-1)?"),":")",c=oe(c),g=oe(g),w=c.length+g.length>6,w&&!U&&0===g[3]?(a["xs"+a.l]+=a.l?" transparent":"transparent",a.e=a.e.split(R[u]).join("transparent")):(U||(w=!1),a.appendXtra(w?"rgba(":"rgb(",c[0],g[0]-c[0],",",!0,!0).appendXtra("",c[1],g[1]-c[1],",",!0).appendXtra("",c[2],g[2]-c[2],w?",":S,!0),w&&(c=4>c.length?1:c[3],a.appendXtra("",c,(4>g.length?1:g[3])-c,S,!1)));else if(v=c.match(m)){if(y=g.match(d),!y||y.length!==v.length)return a;for(f=0,p=0;v.length>p;p++)P=v[p],x=c.indexOf(P,f),a.appendXtra(c.substr(f,x-f),Number(P),ie(y[p],P),"",C&&"px"===c.substr(x+P.length,2),0===p),f=x+P.length;a["xs"+a.l]+=c.substr(f)}else a["xs"+a.l]+=a.l?" "+c:c;if(-1!==s.indexOf("=")&&a.data){for(S=a.xs0+a.data.s,u=1;a.l>u;u++)S+=a["xs"+u]+a.data["xn"+u];a.e=S+a["xs"+u]}return a.l||(a.type=-1,a.xs0=a.e),a.xfirst||a},ce=9;for(h=pe.prototype,h.l=h.pr=0;--ce>0;)h["xn"+ce]=0,h["xs"+ce]="";h.xs0="",h._next=h._prev=h.xfirst=h.data=h.plugin=h.setRatio=h.rxp=null,h.appendXtra=function(t,e,i,s,r,n){var a=this,o=a.l;return a["xs"+o]+=n&&o?" "+t:t||"",i||0===o||a.plugin?(a.l++,a.type=a.setRatio?2:1,a["xs"+a.l]=s||"",o>0?(a.data["xn"+o]=e+i,a.rxp["xn"+o]=r,a["xn"+o]=e,a.plugin||(a.xfirst=new pe(a,"xn"+o,e,i,a.xfirst||a,0,a.n,r,a.pr),a.xfirst.xs0=0),a):(a.data={s:e+i},a.rxp={},a.s=e,a.c=i,a.r=r,a)):(a["xs"+o]+=e+(s||""),a)};var me=function(t,e){e=e||{},this.p=e.prefix?V(t)||t:t,o[t]=o[this.p]=this,this.format=e.formatter||le(e.defaultValue,e.color,e.collapsible,e.multi),e.parser&&(this.parse=e.parser),this.clrs=e.color,this.multi=e.multi,this.keyword=e.keyword,this.dflt=e.defaultValue,this.pr=e.priority||0},de=X._registerComplexSpecialProp=function(t,e,i){"object"!=typeof e&&(e={parser:i});var s,r,n=t.split(","),a=e.defaultValue;for(i=i||[a],s=0;n.length>s;s++)e.prefix=0===s&&e.prefix,e.defaultValue=i[s]||a,r=new me(n[s],e)},ge=function(t){if(!o[t]){var e=t.charAt(0).toUpperCase()+t.substr(1)+"Plugin";de(t,{parser:function(t,i,s,r,n,a,h){var l=(window.GreenSockGlobals||window).com.greensock.plugins[e];return l?(l._cssRegister(),o[s].parse(t,i,s,r,n,a,h)):(j("Error: "+e+" js file not loaded."),n)}})}};h=me.prototype,h.parseComplex=function(t,e,i,s,r,n){var a,o,h,l,_,u,p=this.keyword;if(this.multi&&(D.test(i)||D.test(e)?(o=e.replace(D,"|").split("|"),h=i.replace(D,"|").split("|")):p&&(o=[e],h=[i])),h){for(l=h.length>o.length?h.length:o.length,a=0;l>a;a++)e=o[a]=o[a]||this.dflt,i=h[a]=h[a]||this.dflt,p&&(_=e.indexOf(p),u=i.indexOf(p),_!==u&&(i=-1===u?h:o,i[a]+=" "+p));e=o.join(", "),i=h.join(", ")}return fe(t,this.p,e,i,this.clrs,this.dflt,s,this.pr,r,n)},h.parse=function(t,e,i,s,n,a){return this.parseComplex(t.style,this.format(G(t,this.p,r,!1,this.dflt)),this.format(e),n,a)},a.registerSpecialProp=function(t,e,i){de(t,{parser:function(t,s,r,n,a,o){var h=new pe(t,r,0,0,a,2,r,!1,i);return h.plugin=o,h.setRatio=e(t,s,n._tween,r),h},priority:i})};var ve="scaleX,scaleY,scaleZ,x,y,z,skewX,rotation,rotationX,rotationY,perspective".split(","),ye=V("transform"),Te=B+"transform",we=V("transformOrigin"),xe=null!==V("perspective"),be=function(t,e,i,s){if(t._gsTransform&&i&&!s)return t._gsTransform;var r,n,o,h,l,_,u,p,f,c,m,d,g,v=i?t._gsTransform||{skewY:0}:{skewY:0},y=0>v.scaleX,T=2e-5,w=1e5,x=179.99,b=x*M,P=xe?parseFloat(G(t,we,e,!1,"0 0 0").split(" ")[2])||v.zOrigin||0:0;for(ye?r=G(t,Te,e,!0):t.currentStyle&&(r=t.currentStyle.filter.match(C),r=r&&4===r.length?[r[0].substr(4),Number(r[2].substr(4)),Number(r[1].substr(4)),r[3].substr(4),v.x||0,v.y||0].join(","):""),n=(r||"").match(/(?:\-|\b)[\d\-\.e]+\b/gi)||[],o=n.length;--o>-1;)h=Number(n[o]),n[o]=(l=h-(h|=0))?(0|l*w+(0>l?-.5:.5))/w+h:h;if(16===n.length){var S=n[8],k=n[9],R=n[10],A=n[12],O=n[13],D=n[14];if(v.zOrigin&&(D=-v.zOrigin,A=S*D-n[12],O=k*D-n[13],D=R*D+v.zOrigin-n[14]),!i||s||null==v.rotationX){var F,E,N,L,X,z,U,Y=n[0],j=n[1],B=n[2],q=n[3],V=n[4],Z=n[5],$=n[6],Q=n[7],W=n[11],H=Math.atan2($,R),K=-b>H||H>b;v.rotationX=H*I,H&&(L=Math.cos(-H),X=Math.sin(-H),F=V*L+S*X,E=Z*L+k*X,N=$*L+R*X,S=V*-X+S*L,k=Z*-X+k*L,R=$*-X+R*L,W=Q*-X+W*L,V=F,Z=E,$=N),H=Math.atan2(S,Y),v.rotationY=H*I,H&&(z=-b>H||H>b,L=Math.cos(-H),X=Math.sin(-H),F=Y*L-S*X,E=j*L-k*X,N=B*L-R*X,k=j*X+k*L,R=B*X+R*L,W=q*X+W*L,Y=F,j=E,B=N),H=Math.atan2(j,Z),v.rotation=H*I,H&&(U=-b>H||H>b,L=Math.cos(-H),X=Math.sin(-H),Y=Y*L+V*X,E=j*L+Z*X,Z=j*-X+Z*L,$=B*-X+$*L,j=E),U&&K?v.rotation=v.rotationX=0:U&&z?v.rotation=v.rotationY=0:z&&K&&(v.rotationY=v.rotationX=0),v.scaleX=(0|Math.sqrt(Y*Y+j*j)*w+.5)/w,v.scaleY=(0|Math.sqrt(Z*Z+k*k)*w+.5)/w,v.scaleZ=(0|Math.sqrt($*$+R*R)*w+.5)/w,v.skewX=0,v.perspective=W?1/(0>W?-W:W):0,v.x=A,v.y=O,v.z=D}}else if(!(xe&&!s&&n.length&&v.x===n[4]&&v.y===n[5]&&(v.rotationX||v.rotationY)||void 0!==v.x&&"none"===G(t,"display",e))){var J=n.length>=6,te=J?n[0]:1,ee=n[1]||0,ie=n[2]||0,se=J?n[3]:1;v.x=n[4]||0,v.y=n[5]||0,_=Math.sqrt(te*te+ee*ee),u=Math.sqrt(se*se+ie*ie),p=te||ee?Math.atan2(ee,te)*I:v.rotation||0,f=ie||se?Math.atan2(ie,se)*I+p:v.skewX||0,c=_-Math.abs(v.scaleX||0),m=u-Math.abs(v.scaleY||0),Math.abs(f)>90&&270>Math.abs(f)&&(y?(_*=-1,f+=0>=p?180:-180,p+=0>=p?180:-180):(u*=-1,f+=0>=f?180:-180)),d=(p-v.rotation)%180,g=(f-v.skewX)%180,(void 0===v.skewX||c>T||-T>c||m>T||-T>m||d>-x&&x>d&&false|d*w||g>-x&&x>g&&false|g*w)&&(v.scaleX=_,v.scaleY=u,v.rotation=p,v.skewX=f),xe&&(v.rotationX=v.rotationY=v.z=0,v.perspective=parseFloat(a.defaultTransformPerspective)||0,v.scaleZ=1)}v.zOrigin=P;for(o in v)T>v[o]&&v[o]>-T&&(v[o]=0);return i&&(t._gsTransform=v),v},Pe=function(t){var e,i,s=this.data,r=-s.rotation*M,n=r+s.skewX*M,a=1e5,o=(0|Math.cos(r)*s.scaleX*a)/a,h=(0|Math.sin(r)*s.scaleX*a)/a,l=(0|Math.sin(n)*-s.scaleY*a)/a,_=(0|Math.cos(n)*s.scaleY*a)/a,u=this.t.style,p=this.t.currentStyle;if(p){i=h,h=-l,l=-i,e=p.filter,u.filter="";var f,m,d=this.t.offsetWidth,g=this.t.offsetHeight,v="absolute"!==p.position,w="progid:DXImageTransform.Microsoft.Matrix(M11="+o+", M12="+h+", M21="+l+", M22="+_,x=s.x,b=s.y;if(null!=s.ox&&(f=(s.oxp?.01*d*s.ox:s.ox)-d/2,m=(s.oyp?.01*g*s.oy:s.oy)-g/2,x+=f-(f*o+m*h),b+=m-(f*l+m*_)),v?(f=d/2,m=g/2,w+=", Dx="+(f-(f*o+m*h)+x)+", Dy="+(m-(f*l+m*_)+b)+")"):w+=", sizingMethod='auto expand')",u.filter=-1!==e.indexOf("DXImageTransform.Microsoft.Matrix(")?e.replace(O,w):w+" "+e,(0===t||1===t)&&1===o&&0===h&&0===l&&1===_&&(v&&-1===w.indexOf("Dx=0, Dy=0")||T.test(e)&&100!==parseFloat(RegExp.$1)||-1===e.indexOf("gradient("&&e.indexOf("Alpha"))&&u.removeAttribute("filter")),!v){var P,S,k,R=8>c?1:-1;for(f=s.ieOffsetX||0,m=s.ieOffsetY||0,s.ieOffsetX=Math.round((d-((0>o?-o:o)*d+(0>h?-h:h)*g))/2+x),s.ieOffsetY=Math.round((g-((0>_?-_:_)*g+(0>l?-l:l)*d))/2+b),ce=0;4>ce;ce++)S=J[ce],P=p[S],i=-1!==P.indexOf("px")?parseFloat(P):$(this.t,S,parseFloat(P),P.replace(y,""))||0,k=i!==s[S]?2>ce?-s.ieOffsetX:-s.ieOffsetY:2>ce?f-s.ieOffsetX:m-s.ieOffsetY,u[S]=(s[S]=Math.round(i-k*(0===ce||2===ce?1:R)))+"px"}}},Se=function(){var t,e,i,s,r,n,a,o,h,l,_,u,f,c,m,d,g,v,y,T,w,x,b,P,S,k,R=this.data,A=this.t.style,C=R.rotation*M,O=R.scaleX,D=R.scaleY,I=R.scaleZ,F=R.perspective;if(p&&(P=A.top?"top":A.bottom?"bottom":parseFloat(G(this.t,"top",null,!1))?"bottom":"top",T=G(this.t,P,null,!1),S=parseFloat(T)||0,k=T.substr((S+"").length)||"px",R._ffFix=!R._ffFix,A[P]=(R._ffFix?S+.05:S-.05)+k),C||R.skewX)v=Math.cos(C),y=Math.sin(C),t=v,r=y,R.skewX&&(C-=R.skewX*M,v=Math.cos(C),y=Math.sin(C)),e=-y,n=v;else{if(!(R.rotationY||R.rotationX||1!==I||F))return A[ye]="translate3d("+R.x+"px,"+R.y+"px,"+R.z+"px)"+(1!==O||1!==D?" scale("+O+","+D+")":""),void 0;t=n=1,e=r=0}_=1,i=s=a=o=h=l=u=f=c=0,m=F?-1/F:0,d=R.zOrigin,g=1e5,C=R.rotationY*M,C&&(v=Math.cos(C),y=Math.sin(C),h=_*-y,f=m*-y,i=t*y,a=r*y,_*=v,m*=v,t*=v,r*=v),C=R.rotationX*M,C&&(v=Math.cos(C),y=Math.sin(C),T=e*v+i*y,w=n*v+a*y,x=l*v+_*y,b=c*v+m*y,i=e*-y+i*v,a=n*-y+a*v,_=l*-y+_*v,m=c*-y+m*v,e=T,n=w,l=x,c=b),1!==I&&(i*=I,a*=I,_*=I,m*=I),1!==D&&(e*=D,n*=D,l*=D,c*=D),1!==O&&(t*=O,r*=O,h*=O,f*=O),d&&(u-=d,s=i*u,o=a*u,u=_*u+d),s=(T=(s+=R.x)-(s|=0))?(0|T*g+(0>T?-.5:.5))/g+s:s,o=(T=(o+=R.y)-(o|=0))?(0|T*g+(0>T?-.5:.5))/g+o:o,u=(T=(u+=R.z)-(u|=0))?(0|T*g+(0>T?-.5:.5))/g+u:u,A[ye]="matrix3d("+[(0|t*g)/g,(0|r*g)/g,(0|h*g)/g,(0|f*g)/g,(0|e*g)/g,(0|n*g)/g,(0|l*g)/g,(0|c*g)/g,(0|i*g)/g,(0|a*g)/g,(0|_*g)/g,(0|m*g)/g,s,o,u,F?1+-u/F:1].join(",")+")"},ke=function(){var t,e,i,s,r,n,a,o,h,l=this.data,_=this.t,u=_.style;p&&(t=u.top?"top":u.bottom?"bottom":parseFloat(G(_,"top",null,!1))?"bottom":"top",e=G(_,t,null,!1),i=parseFloat(e)||0,s=e.substr((i+"").length)||"px",l._ffFix=!l._ffFix,u[t]=(l._ffFix?i+.05:i-.05)+s),l.rotation||l.skewX?(r=l.rotation*M,n=r-l.skewX*M,a=1e5,o=l.scaleX*a,h=l.scaleY*a,u[ye]="matrix("+(0|Math.cos(r)*o)/a+","+(0|Math.sin(r)*o)/a+","+(0|Math.sin(n)*-h)/a+","+(0|Math.cos(n)*h)/a+","+l.x+","+l.y+")"):u[ye]="matrix("+l.scaleX+",0,0,"+l.scaleY+","+l.x+","+l.y+")"};de("transform,scale,scaleX,scaleY,scaleZ,x,y,z,rotation,rotationX,rotationY,rotationZ,skewX,skewY,shortRotation,shortRotationX,shortRotationY,shortRotationZ,transformOrigin,transformPerspective,directionalRotation,parseTransform,force3D",{parser:function(t,e,i,s,n,a,o){if(s._transform)return n;var h,l,_,u,p,f,c,m=s._transform=be(t,r,!0,o.parseTransform),d=t.style,g=1e-6,v=ve.length,y=o,T={};if("string"==typeof y.transform&&ye)_=d.cssText,d[ye]=y.transform,d.display="block",h=be(t,null,!1),d.cssText=_;else if("object"==typeof y){if(h={scaleX:se(null!=y.scaleX?y.scaleX:y.scale,m.scaleX),scaleY:se(null!=y.scaleY?y.scaleY:y.scale,m.scaleY),scaleZ:se(null!=y.scaleZ?y.scaleZ:y.scale,m.scaleZ),x:se(y.x,m.x),y:se(y.y,m.y),z:se(y.z,m.z),perspective:se(y.transformPerspective,m.perspective)},c=y.directionalRotation,null!=c)if("object"==typeof c)for(_ in c)y[_]=c[_];else y.rotation=c;h.rotation=re("rotation"in y?y.rotation:"shortRotation"in y?y.shortRotation+"_short":"rotationZ"in y?y.rotationZ:m.rotation,m.rotation,"rotation",T),xe&&(h.rotationX=re("rotationX"in y?y.rotationX:"shortRotationX"in y?y.shortRotationX+"_short":m.rotationX||0,m.rotationX,"rotationX",T),h.rotationY=re("rotationY"in y?y.rotationY:"shortRotationY"in y?y.shortRotationY+"_short":m.rotationY||0,m.rotationY,"rotationY",T)),h.skewX=null==y.skewX?m.skewX:re(y.skewX,m.skewX),h.skewY=null==y.skewY?m.skewY:re(y.skewY,m.skewY),(l=h.skewY-m.skewY)&&(h.skewX+=l,h.rotation+=l)}for(null!=y.force3D&&(m.force3D=y.force3D,f=!0),p=m.force3D||m.z||m.rotationX||m.rotationY||h.z||h.rotationX||h.rotationY||h.perspective,p||null==y.scale||(h.scaleZ=1);--v>-1;)i=ve[v],u=h[i]-m[i],(u>g||-g>u||null!=F[i])&&(f=!0,n=new pe(m,i,m[i],u,n),i in T&&(n.e=T[i]),n.xs0=0,n.plugin=a,s._overwriteProps.push(n.n));return u=y.transformOrigin,(u||xe&&p&&m.zOrigin)&&(ye?(f=!0,i=we,u=(u||G(t,i,r,!1,"50% 50%"))+"",n=new pe(d,i,0,0,n,-1,"transformOrigin"),n.b=d[i],n.plugin=a,xe?(_=m.zOrigin,u=u.split(" "),m.zOrigin=(u.length>2&&(0===_||"0px"!==u[2])?parseFloat(u[2]):_)||0,n.xs0=n.e=d[i]=u[0]+" "+(u[1]||"50%")+" 0px",n=new pe(m,"zOrigin",0,0,n,-1,n.n),n.b=_,n.xs0=n.e=m.zOrigin):n.xs0=n.e=d[i]=u):ee(u+"",m)),f&&(s._transformType=p||3===this._transformType?3:2),n},prefix:!0}),de("boxShadow",{defaultValue:"0px 0px 0px 0px #999",prefix:!0,color:!0,multi:!0,keyword:"inset"}),de("borderRadius",{defaultValue:"0px",parser:function(t,e,i,n,a){e=this.format(e);var o,h,l,_,u,p,f,c,m,d,g,v,y,T,w,x,b=["borderTopLeftRadius","borderTopRightRadius","borderBottomRightRadius","borderBottomLeftRadius"],P=t.style;for(m=parseFloat(t.offsetWidth),d=parseFloat(t.offsetHeight),o=e.split(" "),h=0;b.length>h;h++)this.p.indexOf("border")&&(b[h]=V(b[h])),u=_=G(t,b[h],r,!1,"0px"),-1!==u.indexOf(" ")&&(_=u.split(" "),u=_[0],_=_[1]),p=l=o[h],f=parseFloat(u),v=u.substr((f+"").length),y="="===p.charAt(1),y?(c=parseInt(p.charAt(0)+"1",10),p=p.substr(2),c*=parseFloat(p),g=p.substr((c+"").length-(0>c?1:0))||""):(c=parseFloat(p),g=p.substr((c+"").length)),""===g&&(g=s[i]||v),g!==v&&(T=$(t,"borderLeft",f,v),w=$(t,"borderTop",f,v),"%"===g?(u=100*(T/m)+"%",_=100*(w/d)+"%"):"em"===g?(x=$(t,"borderLeft",1,"em"),u=T/x+"em",_=w/x+"em"):(u=T+"px",_=w+"px"),y&&(p=parseFloat(u)+c+g,l=parseFloat(_)+c+g)),a=fe(P,b[h],u+" "+_,p+" "+l,!1,"0px",a);return a},prefix:!0,formatter:le("0px 0px 0px 0px",!1,!0)}),de("backgroundPosition",{defaultValue:"0 0",parser:function(t,e,i,s,n,a){var o,h,l,_,u,p,f="background-position",m=r||Z(t,null),d=this.format((m?c?m.getPropertyValue(f+"-x")+" "+m.getPropertyValue(f+"-y"):m.getPropertyValue(f):t.currentStyle.backgroundPositionX+" "+t.currentStyle.backgroundPositionY)||"0 0"),g=this.format(e);if(-1!==d.indexOf("%")!=(-1!==g.indexOf("%"))&&(p=G(t,"backgroundImage").replace(k,""),p&&"none"!==p)){for(o=d.split(" "),h=g.split(" "),L.setAttribute("src",p),l=2;--l>-1;)d=o[l],_=-1!==d.indexOf("%"),_!==(-1!==h[l].indexOf("%"))&&(u=0===l?t.offsetWidth-L.width:t.offsetHeight-L.height,o[l]=_?parseFloat(d)/100*u+"px":100*(parseFloat(d)/u)+"%");d=o.join(" ")}return this.parseComplex(t.style,d,g,n,a)},formatter:ee}),de("backgroundSize",{defaultValue:"0 0",formatter:ee}),de("perspective",{defaultValue:"0px",prefix:!0}),de("perspectiveOrigin",{defaultValue:"50% 50%",prefix:!0}),de("transformStyle",{prefix:!0}),de("backfaceVisibility",{prefix:!0}),de("userSelect",{prefix:!0}),de("margin",{parser:_e("marginTop,marginRight,marginBottom,marginLeft")}),de("padding",{parser:_e("paddingTop,paddingRight,paddingBottom,paddingLeft")}),de("clip",{defaultValue:"rect(0px,0px,0px,0px)",parser:function(t,e,i,s,n,a){var o,h,l;return 9>c?(h=t.currentStyle,l=8>c?" ":",",o="rect("+h.clipTop+l+h.clipRight+l+h.clipBottom+l+h.clipLeft+")",e=this.format(e).split(",").join(l)):(o=this.format(G(t,this.p,r,!1,this.dflt)),e=this.format(e)),this.parseComplex(t.style,o,e,n,a)}}),de("textShadow",{defaultValue:"0px 0px 0px #999",color:!0,multi:!0}),de("autoRound,strictUnits",{parser:function(t,e,i,s,r){return r}}),de("border",{defaultValue:"0px solid #000",parser:function(t,e,i,s,n,a){return this.parseComplex(t.style,this.format(G(t,"borderTopWidth",r,!1,"0px")+" "+G(t,"borderTopStyle",r,!1,"solid")+" "+G(t,"borderTopColor",r,!1,"#000")),this.format(e),n,a)},color:!0,formatter:function(t){var e=t.split(" ");return e[0]+" "+(e[1]||"solid")+" "+(t.match(he)||["#000"])[0]}}),de("float,cssFloat,styleFloat",{parser:function(t,e,i,s,r){var n=t.style,a="cssFloat"in n?"cssFloat":"styleFloat";return new pe(n,a,0,0,r,-1,i,!1,0,n[a],e)}});var Re=function(t){var e,i=this.t,s=i.filter||G(this.data,"filter"),r=0|this.s+this.c*t;100===r&&(-1===s.indexOf("atrix(")&&-1===s.indexOf("radient(")&&-1===s.indexOf("oader(")?(i.removeAttribute("filter"),e=!G(this.data,"filter")):(i.filter=s.replace(x,""),e=!0)),e||(this.xn1&&(i.filter=s=s||"alpha(opacity="+r+")"),-1===s.indexOf("opacity")?0===r&&this.xn1||(i.filter=s+" alpha(opacity="+r+")"):i.filter=s.replace(T,"opacity="+r))};de("opacity,alpha,autoAlpha",{defaultValue:"1",parser:function(t,e,i,s,n,a){var o=parseFloat(G(t,"opacity",r,!1,"1")),h=t.style,l="autoAlpha"===i;return"string"==typeof e&&"="===e.charAt(1)&&(e=("-"===e.charAt(0)?-1:1)*parseFloat(e.substr(2))+o),l&&1===o&&"hidden"===G(t,"visibility",r)&&0!==e&&(o=0),U?n=new pe(h,"opacity",o,e-o,n):(n=new pe(h,"opacity",100*o,100*(e-o),n),n.xn1=l?1:0,h.zoom=1,n.type=2,n.b="alpha(opacity="+n.s+")",n.e="alpha(opacity="+(n.s+n.c)+")",n.data=t,n.plugin=a,n.setRatio=Re),l&&(n=new pe(h,"visibility",0,0,n,-1,null,!1,0,0!==o?"inherit":"hidden",0===e?"hidden":"inherit"),n.xs0="inherit",s._overwriteProps.push(n.n),s._overwriteProps.push(i)),n}});var Ae=function(t,e){e&&(t.removeProperty?t.removeProperty(e.replace(P,"-$1").toLowerCase()):t.removeAttribute(e))},Ce=function(t){if(this.t._gsClassPT=this,1===t||0===t){this.t.className=0===t?this.b:this.e;for(var e=this.data,i=this.t.style;e;)e.v?i[e.p]=e.v:Ae(i,e.p),e=e._next;1===t&&this.t._gsClassPT===this&&(this.t._gsClassPT=null)}else this.t.className!==this.e&&(this.t.className=this.e)};de("className",{parser:function(t,e,s,n,a,o,h){var l,_,u,p,f,c=t.className,m=t.style.cssText;if(a=n._classNamePT=new pe(t,s,0,0,a,2),a.setRatio=Ce,a.pr=-11,i=!0,a.b=c,_=W(t,r),u=t._gsClassPT){for(p={},f=u.data;f;)p[f.p]=1,f=f._next;u.setRatio(1)}return t._gsClassPT=a,a.e="="!==e.charAt(1)?e:c.replace(RegExp("\\s*\\b"+e.substr(2)+"\\b"),"")+("+"===e.charAt(0)?" "+e.substr(2):""),n._tween._duration&&(t.className=a.e,l=H(t,_,W(t),h,p),t.className=c,a.data=l.firstMPT,t.style.cssText=m,a=a.xfirst=n.parse(t,l.difs,a,o)),a}});var Oe=function(t){if((1===t||0===t)&&this.data._totalTime===this.data._totalDuration&&"isFromStart"!==this.data.data){var e,i,s,r,n=this.t.style,a=o.transform.parse;if("all"===this.e)n.cssText="",r=!0;else for(e=this.e.split(","),s=e.length;--s>-1;)i=e[s],o[i]&&(o[i].parse===a?r=!0:i="transformOrigin"===i?we:o[i].p),Ae(n,i);r&&(Ae(n,ye),this.t._gsTransform&&delete this.t._gsTransform)}};for(de("clearProps",{parser:function(t,e,s,r,n){return n=new pe(t,s,0,0,n,2),n.setRatio=Oe,n.e=e,n.pr=-10,n.data=r._tween,i=!0,n}}),h="bezier,throwProps,physicsProps,physics2D".split(","),ce=h.length;ce--;)ge(h[ce]);h=a.prototype,h._firstPT=null,h._onInitTween=function(t,e,o){if(!t.nodeType)return!1;this._target=t,this._tween=o,this._vars=e,l=e.autoRound,i=!1,s=e.suffixMap||a.suffixMap,r=Z(t,""),n=this._overwriteProps;var h,p,c,m,d,g,v,y,T,x=t.style;if(_&&""===x.zIndex&&(h=G(t,"zIndex",r),("auto"===h||""===h)&&(x.zIndex=0)),"string"==typeof e&&(m=x.cssText,h=W(t,r),x.cssText=m+";"+e,h=H(t,h,W(t)).difs,!U&&w.test(e)&&(h.opacity=parseFloat(RegExp.$1)),e=h,x.cssText=m),this._firstPT=p=this.parse(t,e,null),this._transformType){for(T=3===this._transformType,ye?u&&(_=!0,""===x.zIndex&&(v=G(t,"zIndex",r),("auto"===v||""===v)&&(x.zIndex=0)),f&&(x.WebkitBackfaceVisibility=this._vars.WebkitBackfaceVisibility||(T?"visible":"hidden"))):x.zoom=1,c=p;c&&c._next;)c=c._next;y=new pe(t,"transform",0,0,null,2),this._linkCSSP(y,null,c),y.setRatio=T&&xe?Se:ye?ke:Pe,y.data=this._transform||be(t,r,!0),n.pop()}if(i){for(;p;){for(g=p._next,c=m;c&&c.pr>p.pr;)c=c._next;(p._prev=c?c._prev:d)?p._prev._next=p:m=p,(p._next=c)?c._prev=p:d=p,p=g}this._firstPT=m}return!0},h.parse=function(t,e,i,n){var a,h,_,u,p,f,c,m,d,g,v=t.style;for(a in e)f=e[a],h=o[a],h?i=h.parse(t,f,a,this,i,n,e):(p=G(t,a,r)+"",d="string"==typeof f,"color"===a||"fill"===a||"stroke"===a||-1!==a.indexOf("Color")||d&&b.test(f)?(d||(f=oe(f),f=(f.length>3?"rgba(":"rgb(")+f.join(",")+")"),i=fe(v,a,p,f,!0,"transparent",i,0,n)):!d||-1===f.indexOf(" ")&&-1===f.indexOf(",")?(_=parseFloat(p),c=_||0===_?p.substr((_+"").length):"",(""===p||"auto"===p)&&("width"===a||"height"===a?(_=te(t,a,r),c="px"):"left"===a||"top"===a?(_=Q(t,a,r),c="px"):(_="opacity"!==a?0:1,c="")),g=d&&"="===f.charAt(1),g?(u=parseInt(f.charAt(0)+"1",10),f=f.substr(2),u*=parseFloat(f),m=f.replace(y,"")):(u=parseFloat(f),m=d?f.substr((u+"").length)||"":""),""===m&&(m=s[a]||c),f=u||0===u?(g?u+_:u)+m:e[a],c!==m&&""!==m&&(u||0===u)&&(_||0===_)&&(_=$(t,a,_,c),"%"===m?(_/=$(t,a,100,"%")/100,_>100&&(_=100),e.strictUnits!==!0&&(p=_+"%")):"em"===m?_/=$(t,a,1,"em"):(u=$(t,a,u,m),m="px"),g&&(u||0===u)&&(f=u+_+m)),g&&(u+=_),!_&&0!==_||!u&&0!==u?void 0!==v[a]&&(f||"NaN"!=f+""&&null!=f)?(i=new pe(v,a,u||_||0,0,i,-1,a,!1,0,p,f),i.xs0="none"!==f||"display"!==a&&-1===a.indexOf("Style")?f:p):j("invalid "+a+" tween value: "+e[a]):(i=new pe(v,a,_,u-_,i,0,a,l!==!1&&("px"===m||"zIndex"===a),0,p,f),i.xs0=m)):i=fe(v,a,p,f,!0,null,i,0,n)),n&&i&&!i.plugin&&(i.plugin=n);return i},h.setRatio=function(t){var e,i,s,r=this._firstPT,n=1e-6;if(1!==t||this._tween._time!==this._tween._duration&&0!==this._tween._time)if(t||this._tween._time!==this._tween._duration&&0!==this._tween._time||this._tween._rawPrevTime===-1e-6)for(;r;){if(e=r.c*t+r.s,r.r?e=e>0?0|e+.5:0|e-.5:n>e&&e>-n&&(e=0),r.type)if(1===r.type)if(s=r.l,2===s)r.t[r.p]=r.xs0+e+r.xs1+r.xn1+r.xs2;else if(3===s)r.t[r.p]=r.xs0+e+r.xs1+r.xn1+r.xs2+r.xn2+r.xs3;else if(4===s)r.t[r.p]=r.xs0+e+r.xs1+r.xn1+r.xs2+r.xn2+r.xs3+r.xn3+r.xs4;else if(5===s)r.t[r.p]=r.xs0+e+r.xs1+r.xn1+r.xs2+r.xn2+r.xs3+r.xn3+r.xs4+r.xn4+r.xs5;else{for(i=r.xs0+e+r.xs1,s=1;r.l>s;s++)i+=r["xn"+s]+r["xs"+(s+1)];r.t[r.p]=i}else-1===r.type?r.t[r.p]=r.xs0:r.setRatio&&r.setRatio(t);else r.t[r.p]=e+r.xs0;r=r._next}else for(;r;)2!==r.type?r.t[r.p]=r.b:r.setRatio(t),r=r._next;else for(;r;)2!==r.type?r.t[r.p]=r.e:r.setRatio(t),r=r._next},h._enableTransforms=function(t){this._transformType=t||3===this._transformType?3:2,this._transform=this._transform||be(this._target,r,!0)},h._linkCSSP=function(t,e,i,s){return t&&(e&&(e._prev=t),t._next&&(t._next._prev=t._prev),t._prev?t._prev._next=t._next:this._firstPT===t&&(this._firstPT=t._next,s=!0),i?i._next=t:s||null!==this._firstPT||(this._firstPT=t),t._next=e,t._prev=i),t},h._kill=function(e){var i,s,r,n=e;if(e.autoAlpha||e.alpha){n={};for(s in e)n[s]=e[s];n.opacity=1,n.autoAlpha&&(n.visibility=1)}return e.className&&(i=this._classNamePT)&&(r=i.xfirst,r&&r._prev?this._linkCSSP(r._prev,i._next,r._prev._prev):r===this._firstPT&&(this._firstPT=i._next),i._next&&this._linkCSSP(i._next,i._next._next,r._prev),this._classNamePT=null),t.prototype._kill.call(this,n)};var De=function(t,e,i){var s,r,n,a;if(t.slice)for(r=t.length;--r>-1;)De(t[r],e,i);else for(s=t.childNodes,r=s.length;--r>-1;)n=s[r],a=n.type,n.style&&(e.push(W(n)),i&&i.push(n)),1!==a&&9!==a&&11!==a||!n.childNodes.length||De(n,e,i)};return a.cascadeTo=function(t,i,s){var r,n,a,o=e.to(t,i,s),h=[o],l=[],_=[],u=[],p=e._internals.reservedProps;for(t=o._targets||o.target,De(t,l,u),o.render(i,!0),De(t,_),o.render(0,!0),o._enabled(!0),r=u.length;--r>-1;)if(n=H(u[r],l[r],_[r]),n.firstMPT){n=n.difs;for(a in s)p[a]&&(n[a]=s[a]);h.push(e.to(u[r],i,n))}return h},t.activate([a]),a},!0),function(){var t=window._gsDefine.plugin({propName:"roundProps",priority:-1,API:2,init:function(t,e,i){return this._tween=i,!0}}),e=t.prototype;e._onInitAllProps=function(){for(var t,e,i,s=this._tween,r=s.vars.roundProps instanceof Array?s.vars.roundProps:s.vars.roundProps.split(","),n=r.length,a={},o=s._propLookup.roundProps;--n>-1;)a[r[n]]=1;for(n=r.length;--n>-1;)for(t=r[n],e=s._firstPT;e;)i=e._next,e.pg?e.t._roundProps(a,!0):e.n===t&&(this._add(e.t,t,e.s,e.c),i&&(i._prev=e._prev),e._prev?e._prev._next=i:s._firstPT===e&&(s._firstPT=i),e._next=e._prev=null,s._propLookup[t]=o),e=i;return!1},e._add=function(t,e,i,s){this._addTween(t,e,i,i+s,e,!0),this._overwriteProps.push(e)}}(),window._gsDefine.plugin({propName:"attr",API:2,init:function(t,e){var i; +if("function"!=typeof t.setAttribute)return!1;this._target=t,this._proxy={};for(i in e)this._addTween(this._proxy,i,parseFloat(t.getAttribute(i)),e[i],i)&&this._overwriteProps.push(i);return!0},set:function(t){this._super.setRatio.call(this,t);for(var e,i=this._overwriteProps,s=i.length;--s>-1;)e=i[s],this._target.setAttribute(e,this._proxy[e]+"")}}),window._gsDefine.plugin({propName:"directionalRotation",API:2,init:function(t,e){"object"!=typeof e&&(e={rotation:e}),this.finals={};var i,s,r,n,a,o,h=e.useRadians===!0?2*Math.PI:360,l=1e-6;for(i in e)"useRadians"!==i&&(o=(e[i]+"").split("_"),s=o[0],r=parseFloat("function"!=typeof t[i]?t[i]:t[i.indexOf("set")||"function"!=typeof t["get"+i.substr(3)]?i:"get"+i.substr(3)]()),n=this.finals[i]="string"==typeof s&&"="===s.charAt(1)?r+parseInt(s.charAt(0)+"1",10)*Number(s.substr(2)):Number(s)||0,a=n-r,o.length&&(s=o.join("_"),-1!==s.indexOf("short")&&(a%=h,a!==a%(h/2)&&(a=0>a?a+h:a-h)),-1!==s.indexOf("_cw")&&0>a?a=(a+9999999999*h)%h-(0|a/h)*h:-1!==s.indexOf("ccw")&&a>0&&(a=(a-9999999999*h)%h-(0|a/h)*h)),(a>l||-l>a)&&(this._addTween(t,i,r,r+a,i),this._overwriteProps.push(i)));return!0},set:function(t){var e;if(1!==t)this._super.setRatio.call(this,t);else for(e=this._firstPT;e;)e.f?e.t[e.p](this.finals[e.p]):e.t[e.p]=this.finals[e.p],e=e._next}})._autoCSS=!0,window._gsDefine("easing.Back",["easing.Ease"],function(t){var e,i,s,r=window.GreenSockGlobals||window,n=r.com.greensock,a=2*Math.PI,o=Math.PI/2,h=n._class,l=function(e,i){var s=h("easing."+e,function(){},!0),r=s.prototype=new t;return r.constructor=s,r.getRatio=i,s},_=t.register||function(){},u=function(t,e,i,s){var r=h("easing."+t,{easeOut:new e,easeIn:new i,easeInOut:new s},!0);return _(r,t),r},p=function(t,e,i){this.t=t,this.v=e,i&&(this.next=i,i.prev=this,this.c=i.v-e,this.gap=i.t-t)},f=function(e,i){var s=h("easing."+e,function(t){this._p1=t||0===t?t:1.70158,this._p2=1.525*this._p1},!0),r=s.prototype=new t;return r.constructor=s,r.getRatio=i,r.config=function(t){return new s(t)},s},c=u("Back",f("BackOut",function(t){return(t-=1)*t*((this._p1+1)*t+this._p1)+1}),f("BackIn",function(t){return t*t*((this._p1+1)*t-this._p1)}),f("BackInOut",function(t){return 1>(t*=2)?.5*t*t*((this._p2+1)*t-this._p2):.5*((t-=2)*t*((this._p2+1)*t+this._p2)+2)})),m=h("easing.SlowMo",function(t,e,i){e=e||0===e?e:.7,null==t?t=.7:t>1&&(t=1),this._p=1!==t?e:0,this._p1=(1-t)/2,this._p2=t,this._p3=this._p1+this._p2,this._calcEnd=i===!0},!0),d=m.prototype=new t;return d.constructor=m,d.getRatio=function(t){var e=t+(.5-t)*this._p;return this._p1>t?this._calcEnd?1-(t=1-t/this._p1)*t:e-(t=1-t/this._p1)*t*t*t*e:t>this._p3?this._calcEnd?1-(t=(t-this._p3)/this._p1)*t:e+(t-e)*(t=(t-this._p3)/this._p1)*t*t*t:this._calcEnd?1:e},m.ease=new m(.7,.7),d.config=m.config=function(t,e,i){return new m(t,e,i)},e=h("easing.SteppedEase",function(t){t=t||1,this._p1=1/t,this._p2=t+1},!0),d=e.prototype=new t,d.constructor=e,d.getRatio=function(t){return 0>t?t=0:t>=1&&(t=.999999999),(this._p2*t>>0)*this._p1},d.config=e.config=function(t){return new e(t)},i=h("easing.RoughEase",function(e){e=e||{};for(var i,s,r,n,a,o,h=e.taper||"none",l=[],_=0,u=0|(e.points||20),f=u,c=e.randomize!==!1,m=e.clamp===!0,d=e.template instanceof t?e.template:null,g="number"==typeof e.strength?.4*e.strength:.4;--f>-1;)i=c?Math.random():1/u*f,s=d?d.getRatio(i):i,"none"===h?r=g:"out"===h?(n=1-i,r=n*n*g):"in"===h?r=i*i*g:.5>i?(n=2*i,r=.5*n*n*g):(n=2*(1-i),r=.5*n*n*g),c?s+=Math.random()*r-.5*r:f%2?s+=.5*r:s-=.5*r,m&&(s>1?s=1:0>s&&(s=0)),l[_++]={x:i,y:s};for(l.sort(function(t,e){return t.x-e.x}),o=new p(1,1,null),f=u;--f>-1;)a=l[f],o=new p(a.x,a.y,o);this._prev=new p(0,0,0!==o.t?o:o.next)},!0),d=i.prototype=new t,d.constructor=i,d.getRatio=function(t){var e=this._prev;if(t>e.t){for(;e.next&&t>=e.t;)e=e.next;e=e.prev}else for(;e.prev&&e.t>=t;)e=e.prev;return this._prev=e,e.v+(t-e.t)/e.gap*e.c},d.config=function(t){return new i(t)},i.ease=new i,u("Bounce",l("BounceOut",function(t){return 1/2.75>t?7.5625*t*t:2/2.75>t?7.5625*(t-=1.5/2.75)*t+.75:2.5/2.75>t?7.5625*(t-=2.25/2.75)*t+.9375:7.5625*(t-=2.625/2.75)*t+.984375}),l("BounceIn",function(t){return 1/2.75>(t=1-t)?1-7.5625*t*t:2/2.75>t?1-(7.5625*(t-=1.5/2.75)*t+.75):2.5/2.75>t?1-(7.5625*(t-=2.25/2.75)*t+.9375):1-(7.5625*(t-=2.625/2.75)*t+.984375)}),l("BounceInOut",function(t){var e=.5>t;return t=e?1-2*t:2*t-1,t=1/2.75>t?7.5625*t*t:2/2.75>t?7.5625*(t-=1.5/2.75)*t+.75:2.5/2.75>t?7.5625*(t-=2.25/2.75)*t+.9375:7.5625*(t-=2.625/2.75)*t+.984375,e?.5*(1-t):.5*t+.5})),u("Circ",l("CircOut",function(t){return Math.sqrt(1-(t-=1)*t)}),l("CircIn",function(t){return-(Math.sqrt(1-t*t)-1)}),l("CircInOut",function(t){return 1>(t*=2)?-.5*(Math.sqrt(1-t*t)-1):.5*(Math.sqrt(1-(t-=2)*t)+1)})),s=function(e,i,s){var r=h("easing."+e,function(t,e){this._p1=t||1,this._p2=e||s,this._p3=this._p2/a*(Math.asin(1/this._p1)||0)},!0),n=r.prototype=new t;return n.constructor=r,n.getRatio=i,n.config=function(t,e){return new r(t,e)},r},u("Elastic",s("ElasticOut",function(t){return this._p1*Math.pow(2,-10*t)*Math.sin((t-this._p3)*a/this._p2)+1},.3),s("ElasticIn",function(t){return-(this._p1*Math.pow(2,10*(t-=1))*Math.sin((t-this._p3)*a/this._p2))},.3),s("ElasticInOut",function(t){return 1>(t*=2)?-.5*this._p1*Math.pow(2,10*(t-=1))*Math.sin((t-this._p3)*a/this._p2):.5*this._p1*Math.pow(2,-10*(t-=1))*Math.sin((t-this._p3)*a/this._p2)+1},.45)),u("Expo",l("ExpoOut",function(t){return 1-Math.pow(2,-10*t)}),l("ExpoIn",function(t){return Math.pow(2,10*(t-1))-.001}),l("ExpoInOut",function(t){return 1>(t*=2)?.5*Math.pow(2,10*(t-1)):.5*(2-Math.pow(2,-10*(t-1)))})),u("Sine",l("SineOut",function(t){return Math.sin(t*o)}),l("SineIn",function(t){return-Math.cos(t*o)+1}),l("SineInOut",function(t){return-.5*(Math.cos(Math.PI*t)-1)})),h("easing.EaseLookup",{find:function(e){return t.map[e]}},!0),_(r.SlowMo,"SlowMo","ease,"),_(i,"RoughEase","ease,"),_(e,"SteppedEase","ease,"),c},!0)}),function(t){"use strict";var e=t.GreenSockGlobals||t;if(!e.TweenLite){var i,s,r,n,a,o=function(t){var i,s=t.split("."),r=e;for(i=0;s.length>i;i++)r[s[i]]=r=r[s[i]]||{};return r},h=o("com.greensock"),l=1e-10,_=[].slice,u=function(){},p=function(){var t=Object.prototype.toString,e=t.call([]);return function(i){return i instanceof Array||"object"==typeof i&&!!i.push&&t.call(i)===e}}(),f={},c=function(i,s,r,n){this.sc=f[i]?f[i].sc:[],f[i]=this,this.gsClass=null,this.func=r;var a=[];this.check=function(h){for(var l,_,u,p,m=s.length,d=m;--m>-1;)(l=f[s[m]]||new c(s[m],[])).gsClass?(a[m]=l.gsClass,d--):h&&l.sc.push(this);if(0===d&&r)for(_=("com.greensock."+i).split("."),u=_.pop(),p=o(_.join("."))[u]=this.gsClass=r.apply(r,a),n&&(e[u]=p,"function"==typeof define&&define.amd?define((t.GreenSockAMDPath?t.GreenSockAMDPath+"/":"")+i.split(".").join("/"),[],function(){return p}):"undefined"!=typeof module&&module.exports&&(module.exports=p)),m=0;this.sc.length>m;m++)this.sc[m].check()},this.check(!0)},m=t._gsDefine=function(t,e,i,s){return new c(t,e,i,s)},d=h._class=function(t,e,i){return e=e||function(){},m(t,[],function(){return e},i),e};m.globals=e;var g=[0,0,1,1],v=[],y=d("easing.Ease",function(t,e,i,s){this._func=t,this._type=i||0,this._power=s||0,this._params=e?g.concat(e):g},!0),T=y.map={},w=y.register=function(t,e,i,s){for(var r,n,a,o,l=e.split(","),_=l.length,u=(i||"easeIn,easeOut,easeInOut").split(",");--_>-1;)for(n=l[_],r=s?d("easing."+n,null,!0):h.easing[n]||{},a=u.length;--a>-1;)o=u[a],T[n+"."+o]=T[o+n]=r[o]=t.getRatio?t:t[o]||new t};for(r=y.prototype,r._calcEnd=!1,r.getRatio=function(t){if(this._func)return this._params[0]=t,this._func.apply(null,this._params);var e=this._type,i=this._power,s=1===e?1-t:2===e?t:.5>t?2*t:2*(1-t);return 1===i?s*=s:2===i?s*=s*s:3===i?s*=s*s*s:4===i&&(s*=s*s*s*s),1===e?1-s:2===e?s:.5>t?s/2:1-s/2},i=["Linear","Quad","Cubic","Quart","Quint,Strong"],s=i.length;--s>-1;)r=i[s]+",Power"+s,w(new y(null,null,1,s),r,"easeOut",!0),w(new y(null,null,2,s),r,"easeIn"+(0===s?",easeNone":"")),w(new y(null,null,3,s),r,"easeInOut");T.linear=h.easing.Linear.easeIn,T.swing=h.easing.Quad.easeInOut;var x=d("events.EventDispatcher",function(t){this._listeners={},this._eventTarget=t||this});r=x.prototype,r.addEventListener=function(t,e,i,s,r){r=r||0;var o,h,l=this._listeners[t],_=0;for(null==l&&(this._listeners[t]=l=[]),h=l.length;--h>-1;)o=l[h],o.c===e&&o.s===i?l.splice(h,1):0===_&&r>o.pr&&(_=h+1);l.splice(_,0,{c:e,s:i,up:s,pr:r}),this!==n||a||n.wake()},r.removeEventListener=function(t,e){var i,s=this._listeners[t];if(s)for(i=s.length;--i>-1;)if(s[i].c===e)return s.splice(i,1),void 0},r.dispatchEvent=function(t){var e,i,s,r=this._listeners[t];if(r)for(e=r.length,i=this._eventTarget;--e>-1;)s=r[e],s.up?s.c.call(s.s||i,{type:t,target:i}):s.c.call(s.s||i)};var b=t.requestAnimationFrame,P=t.cancelAnimationFrame,S=Date.now||function(){return(new Date).getTime()},k=S();for(i=["ms","moz","webkit","o"],s=i.length;--s>-1&&!b;)b=t[i[s]+"RequestAnimationFrame"],P=t[i[s]+"CancelAnimationFrame"]||t[i[s]+"CancelRequestAnimationFrame"];d("Ticker",function(t,e){var i,s,r,o,h,l=this,_=S(),p=e!==!1&&b,f=function(t){k=S(),l.time=(k-_)/1e3;var e,n=l.time-h;(!i||n>0||t===!0)&&(l.frame++,h+=n+(n>=o?.004:o-n),e=!0),t!==!0&&(r=s(f)),e&&l.dispatchEvent("tick")};x.call(l),l.time=l.frame=0,l.tick=function(){f(!0)},l.sleep=function(){null!=r&&(p&&P?P(r):clearTimeout(r),s=u,r=null,l===n&&(a=!1))},l.wake=function(){null!==r&&l.sleep(),s=0===i?u:p&&b?b:function(t){return setTimeout(t,0|1e3*(h-l.time)+1)},l===n&&(a=!0),f(2)},l.fps=function(t){return arguments.length?(i=t,o=1/(i||60),h=this.time+o,l.wake(),void 0):i},l.useRAF=function(t){return arguments.length?(l.sleep(),p=t,l.fps(i),void 0):p},l.fps(t),setTimeout(function(){p&&(!r||5>l.frame)&&l.useRAF(!1)},1500)}),r=h.Ticker.prototype=new h.events.EventDispatcher,r.constructor=h.Ticker;var R=d("core.Animation",function(t,e){if(this.vars=e=e||{},this._duration=this._totalDuration=t||0,this._delay=Number(e.delay)||0,this._timeScale=1,this._active=e.immediateRender===!0,this.data=e.data,this._reversed=e.reversed===!0,U){a||n.wake();var i=this.vars.useFrames?z:U;i.add(this,i._time),this.vars.paused&&this.paused(!0)}});n=R.ticker=new h.Ticker,r=R.prototype,r._dirty=r._gc=r._initted=r._paused=!1,r._totalTime=r._time=0,r._rawPrevTime=-1,r._next=r._last=r._onUpdate=r._timeline=r.timeline=null,r._paused=!1;var A=function(){S()-k>2e3&&n.wake(),setTimeout(A,2e3)};A(),r.play=function(t,e){return arguments.length&&this.seek(t,e),this.reversed(!1).paused(!1)},r.pause=function(t,e){return arguments.length&&this.seek(t,e),this.paused(!0)},r.resume=function(t,e){return arguments.length&&this.seek(t,e),this.paused(!1)},r.seek=function(t,e){return this.totalTime(Number(t),e!==!1)},r.restart=function(t,e){return this.reversed(!1).paused(!1).totalTime(t?-this._delay:0,e!==!1,!0)},r.reverse=function(t,e){return arguments.length&&this.seek(t||this.totalDuration(),e),this.reversed(!0).paused(!1)},r.render=function(){},r.invalidate=function(){return this},r.isActive=function(){var t,e=this._timeline,i=this._startTime;return!e||!this._gc&&!this._paused&&e.isActive()&&(t=e.rawTime())>=i&&i+this.totalDuration()/this._timeScale>t},r._enabled=function(t,e){return a||n.wake(),this._gc=!t,this._active=this.isActive(),e!==!0&&(t&&!this.timeline?this._timeline.add(this,this._startTime-this._delay):!t&&this.timeline&&this._timeline._remove(this,!0)),!1},r._kill=function(){return this._enabled(!1,!1)},r.kill=function(t,e){return this._kill(t,e),this},r._uncache=function(t){for(var e=t?this:this.timeline;e;)e._dirty=!0,e=e.timeline;return this},r._swapSelfInParams=function(t){for(var e=t.length,i=t.concat();--e>-1;)"{self}"===t[e]&&(i[e]=this);return i},r.eventCallback=function(t,e,i,s){if("on"===(t||"").substr(0,2)){var r=this.vars;if(1===arguments.length)return r[t];null==e?delete r[t]:(r[t]=e,r[t+"Params"]=p(i)&&-1!==i.join("").indexOf("{self}")?this._swapSelfInParams(i):i,r[t+"Scope"]=s),"onUpdate"===t&&(this._onUpdate=e)}return this},r.delay=function(t){return arguments.length?(this._timeline.smoothChildTiming&&this.startTime(this._startTime+t-this._delay),this._delay=t,this):this._delay},r.duration=function(t){return arguments.length?(this._duration=this._totalDuration=t,this._uncache(!0),this._timeline.smoothChildTiming&&this._time>0&&this._timethis._duration?this._duration:t,e)):this._time},r.totalTime=function(t,e,i){if(a||n.wake(),!arguments.length)return this._totalTime;if(this._timeline){if(0>t&&!i&&(t+=this.totalDuration()),this._timeline.smoothChildTiming){this._dirty&&this.totalDuration();var s=this._totalDuration,r=this._timeline;if(t>s&&!i&&(t=s),this._startTime=(this._paused?this._pauseTime:r._time)-(this._reversed?s-t:t)/this._timeScale,r._dirty||this._uncache(!1),r._timeline)for(;r._timeline;)r._timeline._time!==(r._startTime+r._totalTime)/r._timeScale&&r.totalTime(r._totalTime,!0),r=r._timeline}this._gc&&this._enabled(!0,!1),(this._totalTime!==t||0===this._duration)&&this.render(t,e,!1)}return this},r.progress=r.totalProgress=function(t,e){return arguments.length?this.totalTime(this.duration()*t,e):this._time/this.duration()},r.startTime=function(t){return arguments.length?(t!==this._startTime&&(this._startTime=t,this.timeline&&this.timeline._sortChildren&&this.timeline.add(this,t-this._delay)),this):this._startTime},r.timeScale=function(t){if(!arguments.length)return this._timeScale;if(t=t||l,this._timeline&&this._timeline.smoothChildTiming){var e=this._pauseTime,i=e||0===e?e:this._timeline.totalTime();this._startTime=i-(i-this._startTime)*this._timeScale/t}return this._timeScale=t,this._uncache(!1)},r.reversed=function(t){return arguments.length?(t!=this._reversed&&(this._reversed=t,this.totalTime(this._totalTime,!0)),this):this._reversed},r.paused=function(t){if(!arguments.length)return this._paused;if(t!=this._paused&&this._timeline){a||t||n.wake();var e=this._timeline,i=e.rawTime(),s=i-this._pauseTime;!t&&e.smoothChildTiming&&(this._startTime+=s,this._uncache(!1)),this._pauseTime=t?i:null,this._paused=t,this._active=this.isActive(),!t&&0!==s&&this._initted&&this.duration()&&this.render(e.smoothChildTiming?this._totalTime:(i-this._startTime)/this._timeScale,!0,!0)}return this._gc&&!t&&this._enabled(!0,!1),this};var C=d("core.SimpleTimeline",function(t){R.call(this,0,t),this.autoRemoveChildren=this.smoothChildTiming=!0});r=C.prototype=new R,r.constructor=C,r.kill()._gc=!1,r._first=r._last=null,r._sortChildren=!1,r.add=r.insert=function(t,e){var i,s;if(t._startTime=Number(e||0)+t._delay,t._paused&&this!==t._timeline&&(t._pauseTime=t._startTime+(this.rawTime()-t._startTime)/t._timeScale),t.timeline&&t.timeline._remove(t,!0),t.timeline=t._timeline=this,t._gc&&t._enabled(!0,!0),i=this._last,this._sortChildren)for(s=t._startTime;i&&i._startTime>s;)i=i._prev;return i?(t._next=i._next,i._next=t):(t._next=this._first,this._first=t),t._next?t._next._prev=t:this._last=t,t._prev=i,this._timeline&&this._uncache(!0),this},r._remove=function(t,e){return t.timeline===this&&(e||t._enabled(!1,!0),t.timeline=null,t._prev?t._prev._next=t._next:this._first===t&&(this._first=t._next),t._next?t._next._prev=t._prev:this._last===t&&(this._last=t._prev),this._timeline&&this._uncache(!0)),this},r.render=function(t,e,i){var s,r=this._first;for(this._totalTime=this._time=this._rawPrevTime=t;r;)s=r._next,(r._active||t>=r._startTime&&!r._paused)&&(r._reversed?r.render((r._dirty?r.totalDuration():r._totalDuration)-(t-r._startTime)*r._timeScale,e,i):r.render((t-r._startTime)*r._timeScale,e,i)),r=s},r.rawTime=function(){return a||n.wake(),this._totalTime};var O=d("TweenLite",function(e,i,s){if(R.call(this,i,s),this.render=O.prototype.render,null==e)throw"Cannot tween a null target.";this.target=e="string"!=typeof e?e:O.selector(e)||e;var r,n,a,o=e.jquery||e.length&&e!==t&&e[0]&&(e[0]===t||e[0].nodeType&&e[0].style&&!e.nodeType),h=this.vars.overwrite;if(this._overwrite=h=null==h?X[O.defaultOverwrite]:"number"==typeof h?h>>0:X[h],(o||e instanceof Array||e.push&&p(e))&&"number"!=typeof e[0])for(this._targets=a=_.call(e,0),this._propLookup=[],this._siblings=[],r=0;a.length>r;r++)n=a[r],n?"string"!=typeof n?n.length&&n!==t&&n[0]&&(n[0]===t||n[0].nodeType&&n[0].style&&!n.nodeType)?(a.splice(r--,1),this._targets=a=a.concat(_.call(n,0))):(this._siblings[r]=Y(n,this,!1),1===h&&this._siblings[r].length>1&&j(n,this,null,1,this._siblings[r])):(n=a[r--]=O.selector(n),"string"==typeof n&&a.splice(r+1,1)):a.splice(r--,1);else this._propLookup={},this._siblings=Y(e,this,!1),1===h&&this._siblings.length>1&&j(e,this,null,1,this._siblings);(this.vars.immediateRender||0===i&&0===this._delay&&this.vars.immediateRender!==!1)&&this.render(-this._delay,!1,!0)},!0),D=function(e){return e.length&&e!==t&&e[0]&&(e[0]===t||e[0].nodeType&&e[0].style&&!e.nodeType)},M=function(t,e){var i,s={};for(i in t)L[i]||i in e&&"x"!==i&&"y"!==i&&"width"!==i&&"height"!==i&&"className"!==i&&"border"!==i||!(!F[i]||F[i]&&F[i]._autoCSS)||(s[i]=t[i],delete t[i]);t.css=s};r=O.prototype=new R,r.constructor=O,r.kill()._gc=!1,r.ratio=0,r._firstPT=r._targets=r._overwrittenProps=r._startAt=null,r._notifyPluginsOfEnabled=!1,O.version="1.11.0",O.defaultEase=r._ease=new y(null,null,1,1),O.defaultOverwrite="auto",O.ticker=n,O.autoSleep=!0,O.selector=t.$||t.jQuery||function(e){return t.$?(O.selector=t.$,t.$(e)):t.document?t.document.getElementById("#"===e.charAt(0)?e.substr(1):e):e};var I=O._internals={isArray:p,isSelector:D},F=O._plugins={},E=O._tweenLookup={},N=0,L=I.reservedProps={ease:1,delay:1,overwrite:1,onComplete:1,onCompleteParams:1,onCompleteScope:1,useFrames:1,runBackwards:1,startAt:1,onUpdate:1,onUpdateParams:1,onUpdateScope:1,onStart:1,onStartParams:1,onStartScope:1,onReverseComplete:1,onReverseCompleteParams:1,onReverseCompleteScope:1,onRepeat:1,onRepeatParams:1,onRepeatScope:1,easeParams:1,yoyo:1,immediateRender:1,repeat:1,repeatDelay:1,data:1,paused:1,reversed:1,autoCSS:1},X={none:0,all:1,auto:2,concurrent:3,allOnStart:4,preexisting:5,"true":1,"false":0},z=R._rootFramesTimeline=new C,U=R._rootTimeline=new C;U._startTime=n.time,z._startTime=n.frame,U._active=z._active=!0,R._updateRoot=function(){if(U.render((n.time-U._startTime)*U._timeScale,!1,!1),z.render((n.frame-z._startTime)*z._timeScale,!1,!1),!(n.frame%120)){var t,e,i;for(i in E){for(e=E[i].tweens,t=e.length;--t>-1;)e[t]._gc&&e.splice(t,1);0===e.length&&delete E[i]}if(i=U._first,(!i||i._paused)&&O.autoSleep&&!z._first&&1===n._listeners.tick.length){for(;i&&i._paused;)i=i._next;i||n.sleep()}}},n.addEventListener("tick",R._updateRoot);var Y=function(t,e,i){var s,r,n=t._gsTweenID;if(E[n||(t._gsTweenID=n="t"+N++)]||(E[n]={target:t,tweens:[]}),e&&(s=E[n].tweens,s[r=s.length]=e,i))for(;--r>-1;)s[r]===e&&s.splice(r,1);return E[n].tweens},j=function(t,e,i,s,r){var n,a,o,h;if(1===s||s>=4){for(h=r.length,n=0;h>n;n++)if((o=r[n])!==e)o._gc||o._enabled(!1,!1)&&(a=!0);else if(5===s)break;return a}var _,u=e._startTime+l,p=[],f=0,c=0===e._duration;for(n=r.length;--n>-1;)(o=r[n])===e||o._gc||o._paused||(o._timeline!==e._timeline?(_=_||B(e,0,c),0===B(o,_,c)&&(p[f++]=o)):u>=o._startTime&&o._startTime+o.totalDuration()/o._timeScale+l>u&&((c||!o._initted)&&2e-10>=u-o._startTime||(p[f++]=o)));for(n=f;--n>-1;)o=p[n],2===s&&o._kill(i,t)&&(a=!0),(2!==s||!o._firstPT&&o._initted)&&o._enabled(!1,!1)&&(a=!0);return a},B=function(t,e,i){for(var s=t._timeline,r=s._timeScale,n=t._startTime;s._timeline;){if(n+=s._startTime,r*=s._timeScale,s._paused)return-100;s=s._timeline}return n/=r,n>e?n-e:i&&n===e||!t._initted&&2*l>n-e?l:(n+=t.totalDuration()/t._timeScale/r)>e+l?0:n-e-l};r._init=function(){var t,e,i,s,r=this.vars,n=this._overwrittenProps,a=this._duration,o=r.immediateRender,h=r.ease;if(r.startAt){if(this._startAt&&this._startAt.render(-1,!0),r.startAt.overwrite=0,r.startAt.immediateRender=!0,this._startAt=O.to(this.target,0,r.startAt),o)if(this._time>0)this._startAt=null;else if(0!==a)return}else if(r.runBackwards&&0!==a)if(this._startAt)this._startAt.render(-1,!0),this._startAt=null;else{i={};for(s in r)L[s]&&"autoCSS"!==s||(i[s]=r[s]);if(i.overwrite=0,i.data="isFromStart",this._startAt=O.to(this.target,0,i),r.immediateRender){if(0===this._time)return}else this._startAt.render(-1,!0)}if(this._ease=h?h instanceof y?r.easeParams instanceof Array?h.config.apply(h,r.easeParams):h:"function"==typeof h?new y(h,r.easeParams):T[h]||O.defaultEase:O.defaultEase,this._easeType=this._ease._type,this._easePower=this._ease._power,this._firstPT=null,this._targets)for(t=this._targets.length;--t>-1;)this._initProps(this._targets[t],this._propLookup[t]={},this._siblings[t],n?n[t]:null)&&(e=!0);else e=this._initProps(this.target,this._propLookup,this._siblings,n);if(e&&O._onPluginEvent("_onInitAllProps",this),n&&(this._firstPT||"function"!=typeof this.target&&this._enabled(!1,!1)),r.runBackwards)for(i=this._firstPT;i;)i.s+=i.c,i.c=-i.c,i=i._next;this._onUpdate=r.onUpdate,this._initted=!0},r._initProps=function(e,i,s,r){var n,a,o,h,l,_;if(null==e)return!1;this.vars.css||e.style&&e!==t&&e.nodeType&&F.css&&this.vars.autoCSS!==!1&&M(this.vars,e);for(n in this.vars){if(_=this.vars[n],L[n])_&&(_ instanceof Array||_.push&&p(_))&&-1!==_.join("").indexOf("{self}")&&(this.vars[n]=_=this._swapSelfInParams(_,this));else if(F[n]&&(h=new F[n])._onInitTween(e,this.vars[n],this)){for(this._firstPT=l={_next:this._firstPT,t:h,p:"setRatio",s:0,c:1,f:!0,n:n,pg:!0,pr:h._priority},a=h._overwriteProps.length;--a>-1;)i[h._overwriteProps[a]]=this._firstPT;(h._priority||h._onInitAllProps)&&(o=!0),(h._onDisable||h._onEnable)&&(this._notifyPluginsOfEnabled=!0)}else this._firstPT=i[n]=l={_next:this._firstPT,t:e,p:n,f:"function"==typeof e[n],n:n,pg:!1,pr:0},l.s=l.f?e[n.indexOf("set")||"function"!=typeof e["get"+n.substr(3)]?n:"get"+n.substr(3)]():parseFloat(e[n]),l.c="string"==typeof _&&"="===_.charAt(1)?parseInt(_.charAt(0)+"1",10)*Number(_.substr(2)):Number(_)-l.s||0;l&&l._next&&(l._next._prev=l)}return r&&this._kill(r,e)?this._initProps(e,i,s,r):this._overwrite>1&&this._firstPT&&s.length>1&&j(e,this,i,this._overwrite,s)?(this._kill(i,e),this._initProps(e,i,s,r)):o},r.render=function(t,e,i){var s,r,n,a,o=this._time,h=this._duration;if(t>=h)this._totalTime=this._time=h,this.ratio=this._ease._calcEnd?this._ease.getRatio(1):1,this._reversed||(s=!0,r="onComplete"),0===h&&(a=this._rawPrevTime,(0===t||0>a||a===l)&&a!==t&&(i=!0,a>l&&(r="onReverseComplete")),this._rawPrevTime=a=!e||t?t:l);else if(1e-7>t)this._totalTime=this._time=0,this.ratio=this._ease._calcEnd?this._ease.getRatio(0):0,(0!==o||0===h&&this._rawPrevTime>l)&&(r="onReverseComplete",s=this._reversed),0>t?(this._active=!1,0===h&&(this._rawPrevTime>=0&&(i=!0),this._rawPrevTime=a=!e||t?t:l)):this._initted||(i=!0);else if(this._totalTime=this._time=t,this._easeType){var _=t/h,u=this._easeType,p=this._easePower;(1===u||3===u&&_>=.5)&&(_=1-_),3===u&&(_*=2),1===p?_*=_:2===p?_*=_*_:3===p?_*=_*_*_:4===p&&(_*=_*_*_*_),this.ratio=1===u?1-_:2===u?_:.5>t/h?_/2:1-_/2}else this.ratio=this._ease.getRatio(t/h);if(this._time!==o||i){if(!this._initted){if(this._init(),!this._initted||this._gc)return;this._time&&!s?this.ratio=this._ease.getRatio(this._time/h):s&&this._ease._calcEnd&&(this.ratio=this._ease.getRatio(0===this._time?0:1))}for(this._active||!this._paused&&this._time!==o&&t>=0&&(this._active=!0),0===o&&(this._startAt&&(t>=0?this._startAt.render(t,e,i):r||(r="_dummyGS")),this.vars.onStart&&(0!==this._time||0===h)&&(e||this.vars.onStart.apply(this.vars.onStartScope||this,this.vars.onStartParams||v))),n=this._firstPT;n;)n.f?n.t[n.p](n.c*this.ratio+n.s):n.t[n.p]=n.c*this.ratio+n.s,n=n._next;this._onUpdate&&(0>t&&this._startAt&&this._startTime&&this._startAt.render(t,e,i),e||i&&0===this._time&&0===o||this._onUpdate.apply(this.vars.onUpdateScope||this,this.vars.onUpdateParams||v)),r&&(this._gc||(0>t&&this._startAt&&!this._onUpdate&&this._startTime&&this._startAt.render(t,e,i),s&&(this._timeline.autoRemoveChildren&&this._enabled(!1,!1),this._active=!1),!e&&this.vars[r]&&this.vars[r].apply(this.vars[r+"Scope"]||this,this.vars[r+"Params"]||v),0===h&&this._rawPrevTime!==a&&(this._rawPrevTime=0)))}},r._kill=function(t,e){if("all"===t&&(t=null),null==t&&(null==e||e===this.target))return this._enabled(!1,!1);e="string"!=typeof e?e||this._targets||this.target:O.selector(e)||e;var i,s,r,n,a,o,h,l;if((p(e)||D(e))&&"number"!=typeof e[0])for(i=e.length;--i>-1;)this._kill(t,e[i])&&(o=!0);else{if(this._targets){for(i=this._targets.length;--i>-1;)if(e===this._targets[i]){a=this._propLookup[i]||{},this._overwrittenProps=this._overwrittenProps||[],s=this._overwrittenProps[i]=t?this._overwrittenProps[i]||{}:"all";break}}else{if(e!==this.target)return!1;a=this._propLookup,s=this._overwrittenProps=t?this._overwrittenProps||{}:"all"}if(a){h=t||a,l=t!==s&&"all"!==s&&t!==a&&("object"!=typeof t||!t._tempKill);for(r in h)(n=a[r])&&(n.pg&&n.t._kill(h)&&(o=!0),n.pg&&0!==n.t._overwriteProps.length||(n._prev?n._prev._next=n._next:n===this._firstPT&&(this._firstPT=n._next),n._next&&(n._next._prev=n._prev),n._next=n._prev=null),delete a[r]),l&&(s[r]=1);!this._firstPT&&this._initted&&this._enabled(!1,!1)}}return o},r.invalidate=function(){return this._notifyPluginsOfEnabled&&O._onPluginEvent("_onDisable",this),this._firstPT=null,this._overwrittenProps=null,this._onUpdate=null,this._startAt=null,this._initted=this._active=this._notifyPluginsOfEnabled=!1,this._propLookup=this._targets?{}:[],this},r._enabled=function(t,e){if(a||n.wake(),t&&this._gc){var i,s=this._targets;if(s)for(i=s.length;--i>-1;)this._siblings[i]=Y(s[i],this,!0);else this._siblings=Y(this.target,this,!0)}return R.prototype._enabled.call(this,t,e),this._notifyPluginsOfEnabled&&this._firstPT?O._onPluginEvent(t?"_onEnable":"_onDisable",this):!1},O.to=function(t,e,i){return new O(t,e,i)},O.from=function(t,e,i){return i.runBackwards=!0,i.immediateRender=0!=i.immediateRender,new O(t,e,i)},O.fromTo=function(t,e,i,s){return s.startAt=i,s.immediateRender=0!=s.immediateRender&&0!=i.immediateRender,new O(t,e,s)},O.delayedCall=function(t,e,i,s,r){return new O(e,0,{delay:t,onComplete:e,onCompleteParams:i,onCompleteScope:s,onReverseComplete:e,onReverseCompleteParams:i,onReverseCompleteScope:s,immediateRender:!1,useFrames:r,overwrite:0})},O.set=function(t,e){return new O(t,0,e)},O.getTweensOf=function(t,e){if(null==t)return[];t="string"!=typeof t?t:O.selector(t)||t;var i,s,r,n;if((p(t)||D(t))&&"number"!=typeof t[0]){for(i=t.length,s=[];--i>-1;)s=s.concat(O.getTweensOf(t[i],e));for(i=s.length;--i>-1;)for(n=s[i],r=i;--r>-1;)n===s[r]&&s.splice(i,1)}else for(s=Y(t).concat(),i=s.length;--i>-1;)(s[i]._gc||e&&!s[i].isActive())&&s.splice(i,1);return s},O.killTweensOf=O.killDelayedCallsTo=function(t,e,i){"object"==typeof e&&(i=e,e=!1);for(var s=O.getTweensOf(t,e),r=s.length;--r>-1;)s[r]._kill(i,t)};var q=d("plugins.TweenPlugin",function(t,e){this._overwriteProps=(t||"").split(","),this._propName=this._overwriteProps[0],this._priority=e||0,this._super=q.prototype},!0);if(r=q.prototype,q.version="1.10.1",q.API=2,r._firstPT=null,r._addTween=function(t,e,i,s,r,n){var a,o;return null!=s&&(a="number"==typeof s||"="!==s.charAt(1)?Number(s)-i:parseInt(s.charAt(0)+"1",10)*Number(s.substr(2)))?(this._firstPT=o={_next:this._firstPT,t:t,p:e,s:i,c:a,f:"function"==typeof t[e],n:r||e,r:n},o._next&&(o._next._prev=o),o):void 0},r.setRatio=function(t){for(var e,i=this._firstPT,s=1e-6;i;)e=i.c*t+i.s,i.r?e=0|e+(e>0?.5:-.5):s>e&&e>-s&&(e=0),i.f?i.t[i.p](e):i.t[i.p]=e,i=i._next},r._kill=function(t){var e,i=this._overwriteProps,s=this._firstPT;if(null!=t[this._propName])this._overwriteProps=[];else for(e=i.length;--e>-1;)null!=t[i[e]]&&i.splice(e,1);for(;s;)null!=t[s.n]&&(s._next&&(s._next._prev=s._prev),s._prev?(s._prev._next=s._next,s._prev=null):this._firstPT===s&&(this._firstPT=s._next)),s=s._next;return!1},r._roundProps=function(t,e){for(var i=this._firstPT;i;)(t[this._propName]||null!=i.n&&t[i.n.split(this._propName+"_").join("")])&&(i.r=e),i=i._next},O._onPluginEvent=function(t,e){var i,s,r,n,a,o=e._firstPT;if("_onInitAllProps"===t){for(;o;){for(a=o._next,s=r;s&&s.pr>o.pr;)s=s._next;(o._prev=s?s._prev:n)?o._prev._next=o:r=o,(o._next=s)?s._prev=o:n=o,o=a}o=e._firstPT=r}for(;o;)o.pg&&"function"==typeof o.t[t]&&o.t[t]()&&(i=!0),o=o._next;return i},q.activate=function(t){for(var e=t.length;--e>-1;)t[e].API===q.API&&(F[(new t[e])._propName]=t[e]);return!0},m.plugin=function(t){if(!(t&&t.propName&&t.init&&t.API))throw"illegal plugin definition.";var e,i=t.propName,s=t.priority||0,r=t.overwriteProps,n={init:"_onInitTween",set:"setRatio",kill:"_kill",round:"_roundProps",initAll:"_onInitAllProps"},a=d("plugins."+i.charAt(0).toUpperCase()+i.substr(1)+"Plugin",function(){q.call(this,i,s),this._overwriteProps=r||[]},t.global===!0),o=a.prototype=new q(i);o.constructor=a,a.API=t.API;for(e in n)"function"==typeof t[e]&&(o[n[e]]=t[e]);return a.version=t.version,q.activate([a]),a},i=t._gsQueue){for(s=0;i.length>s;s++)i[s]();for(r in f)f[r].func||t.console.log("GSAP encountered missing dependency: com.greensock."+r)}a=!1}}(window); \ No newline at end of file diff --git a/static/js/neuro.js b/static/js/neuro.js new file mode 100644 index 0000000..3c57922 --- /dev/null +++ b/static/js/neuro.js @@ -0,0 +1,200 @@ +(function() { + + var isMobile = false; //initiate as false + // device detection + if(/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|ipad|iris|kindle|Android|Silk|lge |maemo|midp|mmp|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i.test(navigator.userAgent) + || /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(navigator.userAgent.substr(0,4))) isMobile = true; + + var width, height, largeHeader, canvas, ctx, points, target, animateHeader = true; + + // Main + if(!isMobile && window.innerWidth > 1024) { + TweenLite.ticker.fps(30); + initHeader(); + initAnimation(); + addListeners(); + } + + + function initHeader() { + var np = 40; + + canvas = document.getElementById('demo-canvas'); + var rect = canvas.getBoundingClientRect(); + + width = canvas.width = rect.width; + height = canvas.height = rect.height; + target = { + x: width / 2, + y: height / 2 + }; + + ctx = canvas.getContext('2d'); + // TweenLite.set(largeHeader, { + // x: -250 + // }); + + // create points + points = []; + for (var x = 0; x < width; x = x + width / np) { + for (var y = 0; y < height; y = y + height / np) { + var px = x + Math.random() * width / np; + var py = y + Math.random() * height / np; + var p = { + x: px, + originX: px, + y: py, + originY: py + }; + points.push(p); + } + } + + // for each point find the 5 closest points + for (var i = 0; i < points.length; i++) { + var closest = []; + var p1 = points[i]; + for (var j = 0; j < points.length; j++) { + var p2 = points[j] + if (!(p1 == p2)) { + var placed = false; + for (var k = 0; k < 5; k++) { + if (!placed) { + if (closest[k] == undefined) { + closest[k] = p2; + placed = true; + } + } + } + + for (var k = 0; k < 5; k++) { + if (!placed) { + if (getDistance(p1, p2) < getDistance(p1, closest[k])) { + closest[k] = p2; + placed = true; + } + } + } + } + } + p1.closest = closest; + } + + // assign a circle to each point + for (var i in points) { + var c = new Circle(points[i], 2 + Math.random() * 2, 'rgba(255,255,255,0.3)'); + points[i].circle = c; + } + } + + // Event handling + function addListeners() { + if (!('ontouchstart' in window)) { + window.addEventListener('mousemove', mouseMove); + } + window.addEventListener('scroll', scrollCheck); + window.addEventListener('resize', resize); + } + + function mouseMove(e) { + var posx = posy = 0; + var rect = canvas.getBoundingClientRect(); + posx = e.clientX - rect.left; + posy = e.clientY - rect.top; + target.x = posx; + target.y = posy; + } + + function scrollCheck() { + if (document.body.scrollTop > height) animateHeader = false; + else animateHeader = true; + } + + function resize() { + var rect = canvas.getBoundingClientRect(); + width = canvas.width = rect.width; + height = canvas.height = rect.height; + } + + // animation + function initAnimation() { + animate(); + for (var i in points) { + shiftPoint(points[i]); + } + } + + function animate() { + if (animateHeader) { + ctx.clearRect(0, 0, width, height); + for (var i in points) { + // detect points in range + if (Math.abs(getDistance(target, points[i])) < 4000) { + points[i].active = 0.3; + points[i].circle.active = 0.6; + } else if (Math.abs(getDistance(target, points[i])) < 40000) { + points[i].active = 0.1; + points[i].circle.active = 0.3; + } else if (Math.abs(getDistance(target, points[i])) < 70000) { + points[i].active = 0.02; + points[i].circle.active = 0.1; + } else { + points[i].active = 0; + points[i].circle.active = 0; + } + + drawLines(points[i]); + points[i].circle.draw(); + } + } + requestAnimationFrame(animate); + } + + function shiftPoint(p) { + TweenLite.to(p, 1 + 1 * Math.random(), { + x: p.originX - 50 + Math.random() * 100, + y: p.originY - 50 + Math.random() * 100, + ease: Circ.easeInOut, + onComplete: function() { + shiftPoint(p); + } + }); + } + + // Canvas manipulation + function drawLines(p) { + if (!p.active) return; + for (var i in p.closest) { + ctx.beginPath(); + ctx.moveTo(p.x, p.y); + ctx.lineTo(p.closest[i].x, p.closest[i].y); + ctx.strokeStyle = 'rgba(81,97,172,' + p.active + ')'; + ctx.stroke(); + } + } + + function Circle(pos, rad, color) { + var _this = this; + + // constructor + (function() { + _this.pos = pos || null; + _this.radius = rad || null; + _this.color = color || null; + })(); + + this.draw = function() { + if (!_this.active) return; + ctx.beginPath(); + ctx.arc(_this.pos.x, _this.pos.y, _this.radius, 0, 2 * Math.PI, false); + ctx.fillStyle = 'rgba(141,198,63,' + _this.active + ')'; + ctx.fill(); + }; + } + + // Util + function getDistance(p1, p2) { + return Math.pow(p1.x - p2.x, 2) + Math.pow(p1.y - p2.y, 2); + } + +})(); diff --git a/static/js/prism.js b/static/js/prism.js new file mode 100644 index 0000000..a4aaa9b --- /dev/null +++ b/static/js/prism.js @@ -0,0 +1,11 @@ +/* http://prismjs.com/download.html?themes=prism-okaidia&languages=markup+css+clike+javascript+bash+markdown+matlab+python+r */ +var _self="undefined"!=typeof window?window:"undefined"!=typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope?self:{},Prism=function(){var e=/\blang(?:uage)?-(\w+)\b/i,t=0,n=_self.Prism={util:{encode:function(e){return e instanceof a?new a(e.type,n.util.encode(e.content),e.alias):"Array"===n.util.type(e)?e.map(n.util.encode):e.replace(/&/g,"&").replace(/e.length)break e;if(!(v instanceof a)){u.lastIndex=0;var b=u.exec(v),k=1;if(!b&&h&&m!=r.length-1){if(u.lastIndex=y,b=u.exec(e),!b)break;for(var w=b.index+(c?b[1].length:0),_=b.index+b[0].length,A=m,P=y,j=r.length;j>A&&_>P;++A)P+=r[A].length,w>=P&&(++m,y=P);if(r[m]instanceof a||r[A-1].greedy)continue;k=A-m,v=e.slice(y,P),b.index-=y}if(b){c&&(f=b[1].length);var w=b.index+f,b=b[0].slice(f),_=w+b.length,x=v.slice(0,w),O=v.slice(_),S=[m,k];x&&S.push(x);var N=new a(l,g?n.tokenize(b,g):b,d,b,h);S.push(N),O&&S.push(O),Array.prototype.splice.apply(r,S)}}}}}return r},hooks:{all:{},add:function(e,t){var a=n.hooks.all;a[e]=a[e]||[],a[e].push(t)},run:function(e,t){var a=n.hooks.all[e];if(a&&a.length)for(var r,i=0;r=a[i++];)r(t)}}},a=n.Token=function(e,t,n,a,r){this.type=e,this.content=t,this.alias=n,this.length=0|(a||"").length,this.greedy=!!r};if(a.stringify=function(e,t,r){if("string"==typeof e)return e;if("Array"===n.util.type(e))return e.map(function(n){return a.stringify(n,t,e)}).join("");var i={type:e.type,content:a.stringify(e.content,t,r),tag:"span",classes:["token",e.type],attributes:{},language:t,parent:r};if("comment"==i.type&&(i.attributes.spellcheck="true"),e.alias){var l="Array"===n.util.type(e.alias)?e.alias:[e.alias];Array.prototype.push.apply(i.classes,l)}n.hooks.run("wrap",i);var o=Object.keys(i.attributes).map(function(e){return e+'="'+(i.attributes[e]||"").replace(/"/g,""")+'"'}).join(" ");return"<"+i.tag+' class="'+i.classes.join(" ")+'"'+(o?" "+o:"")+">"+i.content+""},!_self.document)return _self.addEventListener?(_self.addEventListener("message",function(e){var t=JSON.parse(e.data),a=t.language,r=t.code,i=t.immediateClose;_self.postMessage(n.highlight(r,n.languages[a],a)),i&&_self.close()},!1),_self.Prism):_self.Prism;var r=document.currentScript||[].slice.call(document.getElementsByTagName("script")).pop();return r&&(n.filename=r.src,document.addEventListener&&!r.hasAttribute("data-manual")&&("loading"!==document.readyState?window.requestAnimationFrame?window.requestAnimationFrame(n.highlightAll):window.setTimeout(n.highlightAll,16):document.addEventListener("DOMContentLoaded",n.highlightAll))),_self.Prism}();"undefined"!=typeof module&&module.exports&&(module.exports=Prism),"undefined"!=typeof global&&(global.Prism=Prism); +Prism.languages.markup={comment://,prolog:/<\?[\w\W]+?\?>/,doctype://i,cdata://i,tag:{pattern:/<\/?(?!\d)[^\s>\/=$<]+(?:\s+[^\s>\/=]+(?:=(?:("|')(?:\\\1|\\?(?!\1)[\w\W])*\1|[^\s'">=]+))?)*\s*\/?>/i,inside:{tag:{pattern:/^<\/?[^\s>\/]+/i,inside:{punctuation:/^<\/?/,namespace:/^[^\s>\/:]+:/}},"attr-value":{pattern:/=(?:('|")[\w\W]*?(\1)|[^\s>]+)/i,inside:{punctuation:/[=>"']/}},punctuation:/\/?>/,"attr-name":{pattern:/[^\s>\/]+/,inside:{namespace:/^[^\s>\/:]+:/}}}},entity:/&#?[\da-z]{1,8};/i},Prism.hooks.add("wrap",function(a){"entity"===a.type&&(a.attributes.title=a.content.replace(/&/,"&"))}),Prism.languages.xml=Prism.languages.markup,Prism.languages.html=Prism.languages.markup,Prism.languages.mathml=Prism.languages.markup,Prism.languages.svg=Prism.languages.markup; +Prism.languages.css={comment:/\/\*[\w\W]*?\*\//,atrule:{pattern:/@[\w-]+?.*?(;|(?=\s*\{))/i,inside:{rule:/@[\w-]+/}},url:/url\((?:(["'])(\\(?:\r\n|[\w\W])|(?!\1)[^\\\r\n])*\1|.*?)\)/i,selector:/[^\{\}\s][^\{\};]*?(?=\s*\{)/,string:{pattern:/("|')(\\(?:\r\n|[\w\W])|(?!\1)[^\\\r\n])*\1/,greedy:!0},property:/(\b|\B)[\w-]+(?=\s*:)/i,important:/\B!important\b/i,"function":/[-a-z0-9]+(?=\()/i,punctuation:/[(){};:]/},Prism.languages.css.atrule.inside.rest=Prism.util.clone(Prism.languages.css),Prism.languages.markup&&(Prism.languages.insertBefore("markup","tag",{style:{pattern:/()[\w\W]*?(?=<\/style>)/i,lookbehind:!0,inside:Prism.languages.css,alias:"language-css"}}),Prism.languages.insertBefore("inside","attr-value",{"style-attr":{pattern:/\s*style=("|').*?\1/i,inside:{"attr-name":{pattern:/^\s*style/i,inside:Prism.languages.markup.tag.inside},punctuation:/^\s*=\s*['"]|['"]\s*$/,"attr-value":{pattern:/.+/i,inside:Prism.languages.css}},alias:"language-css"}},Prism.languages.markup.tag)); +Prism.languages.clike={comment:[{pattern:/(^|[^\\])\/\*[\w\W]*?\*\//,lookbehind:!0},{pattern:/(^|[^\\:])\/\/.*/,lookbehind:!0}],string:{pattern:/(["'])(\\(?:\r\n|[\s\S])|(?!\1)[^\\\r\n])*\1/,greedy:!0},"class-name":{pattern:/((?:\b(?:class|interface|extends|implements|trait|instanceof|new)\s+)|(?:catch\s+\())[a-z0-9_\.\\]+/i,lookbehind:!0,inside:{punctuation:/(\.|\\)/}},keyword:/\b(if|else|while|do|for|return|in|instanceof|function|new|try|throw|catch|finally|null|break|continue)\b/,"boolean":/\b(true|false)\b/,"function":/[a-z0-9_]+(?=\()/i,number:/\b-?(?:0x[\da-f]+|\d*\.?\d+(?:e[+-]?\d+)?)\b/i,operator:/--?|\+\+?|!=?=?|<=?|>=?|==?=?|&&?|\|\|?|\?|\*|\/|~|\^|%/,punctuation:/[{}[\];(),.:]/}; +Prism.languages.javascript=Prism.languages.extend("clike",{keyword:/\b(as|async|await|break|case|catch|class|const|continue|debugger|default|delete|do|else|enum|export|extends|finally|for|from|function|get|if|implements|import|in|instanceof|interface|let|new|null|of|package|private|protected|public|return|set|static|super|switch|this|throw|try|typeof|var|void|while|with|yield)\b/,number:/\b-?(0x[\dA-Fa-f]+|0b[01]+|0o[0-7]+|\d*\.?\d+([Ee][+-]?\d+)?|NaN|Infinity)\b/,"function":/[_$a-zA-Z\xA0-\uFFFF][_$a-zA-Z0-9\xA0-\uFFFF]*(?=\()/i,operator:/--?|\+\+?|!=?=?|<=?|>=?|==?=?|&&?|\|\|?|\?|\*\*?|\/|~|\^|%|\.{3}/}),Prism.languages.insertBefore("javascript","keyword",{regex:{pattern:/(^|[^\/])\/(?!\/)(\[.+?]|\\.|[^\/\\\r\n])+\/[gimyu]{0,5}(?=\s*($|[\r\n,.;})]))/,lookbehind:!0,greedy:!0}}),Prism.languages.insertBefore("javascript","string",{"template-string":{pattern:/`(?:\\\\|\\?[^\\])*?`/,greedy:!0,inside:{interpolation:{pattern:/\$\{[^}]+\}/,inside:{"interpolation-punctuation":{pattern:/^\$\{|\}$/,alias:"punctuation"},rest:Prism.languages.javascript}},string:/[\s\S]+/}}}),Prism.languages.markup&&Prism.languages.insertBefore("markup","tag",{script:{pattern:/()[\w\W]*?(?=<\/script>)/i,lookbehind:!0,inside:Prism.languages.javascript,alias:"language-javascript"}}),Prism.languages.js=Prism.languages.javascript; +!function(e){var t={variable:[{pattern:/\$?\(\([\w\W]+?\)\)/,inside:{variable:[{pattern:/(^\$\(\([\w\W]+)\)\)/,lookbehind:!0},/^\$\(\(/],number:/\b-?(?:0x[\dA-Fa-f]+|\d*\.?\d+(?:[Ee]-?\d+)?)\b/,operator:/--?|-=|\+\+?|\+=|!=?|~|\*\*?|\*=|\/=?|%=?|<<=?|>>=?|<=?|>=?|==?|&&?|&=|\^=?|\|\|?|\|=|\?|:/,punctuation:/\(\(?|\)\)?|,|;/}},{pattern:/\$\([^)]+\)|`[^`]+`/,inside:{variable:/^\$\(|^`|\)$|`$/}},/\$(?:[a-z0-9_#\?\*!@]+|\{[^}]+\})/i]};e.languages.bash={shebang:{pattern:/^#!\s*\/bin\/bash|^#!\s*\/bin\/sh/,alias:"important"},comment:{pattern:/(^|[^"{\\])#.*/,lookbehind:!0},string:[{pattern:/((?:^|[^<])<<\s*)(?:"|')?(\w+?)(?:"|')?\s*\r?\n(?:[\s\S])*?\r?\n\2/g,lookbehind:!0,greedy:!0,inside:t},{pattern:/(["'])(?:\\\\|\\?[^\\])*?\1/g,greedy:!0,inside:t}],variable:t.variable,"function":{pattern:/(^|\s|;|\||&)(?:alias|apropos|apt-get|aptitude|aspell|awk|basename|bash|bc|bg|builtin|bzip2|cal|cat|cd|cfdisk|chgrp|chmod|chown|chroot|chkconfig|cksum|clear|cmp|comm|command|cp|cron|crontab|csplit|cut|date|dc|dd|ddrescue|df|diff|diff3|dig|dir|dircolors|dirname|dirs|dmesg|du|egrep|eject|enable|env|ethtool|eval|exec|expand|expect|export|expr|fdformat|fdisk|fg|fgrep|file|find|fmt|fold|format|free|fsck|ftp|fuser|gawk|getopts|git|grep|groupadd|groupdel|groupmod|groups|gzip|hash|head|help|hg|history|hostname|htop|iconv|id|ifconfig|ifdown|ifup|import|install|jobs|join|kill|killall|less|link|ln|locate|logname|logout|look|lpc|lpr|lprint|lprintd|lprintq|lprm|ls|lsof|make|man|mkdir|mkfifo|mkisofs|mknod|more|most|mount|mtools|mtr|mv|mmv|nano|netstat|nice|nl|nohup|notify-send|npm|nslookup|open|op|passwd|paste|pathchk|ping|pkill|popd|pr|printcap|printenv|printf|ps|pushd|pv|pwd|quota|quotacheck|quotactl|ram|rar|rcp|read|readarray|readonly|reboot|rename|renice|remsync|rev|rm|rmdir|rsync|screen|scp|sdiff|sed|seq|service|sftp|shift|shopt|shutdown|sleep|slocate|sort|source|split|ssh|stat|strace|su|sudo|sum|suspend|sync|tail|tar|tee|test|time|timeout|times|touch|top|traceroute|trap|tr|tsort|tty|type|ulimit|umask|umount|unalias|uname|unexpand|uniq|units|unrar|unshar|uptime|useradd|userdel|usermod|users|uuencode|uudecode|v|vdir|vi|vmstat|wait|watch|wc|wget|whereis|which|who|whoami|write|xargs|xdg-open|yes|zip)(?=$|\s|;|\||&)/,lookbehind:!0},keyword:{pattern:/(^|\s|;|\||&)(?:let|:|\.|if|then|else|elif|fi|for|break|continue|while|in|case|function|select|do|done|until|echo|exit|return|set|declare)(?=$|\s|;|\||&)/,lookbehind:!0},"boolean":{pattern:/(^|\s|;|\||&)(?:true|false)(?=$|\s|;|\||&)/,lookbehind:!0},operator:/&&?|\|\|?|==?|!=?|<<>|<=?|>=?|=~/,punctuation:/\$?\(\(?|\)\)?|\.\.|[{}[\];]/};var a=t.variable[1].inside;a["function"]=e.languages.bash["function"],a.keyword=e.languages.bash.keyword,a.boolean=e.languages.bash.boolean,a.operator=e.languages.bash.operator,a.punctuation=e.languages.bash.punctuation}(Prism); +Prism.languages.markdown=Prism.languages.extend("markup",{}),Prism.languages.insertBefore("markdown","prolog",{blockquote:{pattern:/^>(?:[\t ]*>)*/m,alias:"punctuation"},code:[{pattern:/^(?: {4}|\t).+/m,alias:"keyword"},{pattern:/``.+?``|`[^`\n]+`/,alias:"keyword"}],title:[{pattern:/\w+.*(?:\r?\n|\r)(?:==+|--+)/,alias:"important",inside:{punctuation:/==+$|--+$/}},{pattern:/(^\s*)#+.+/m,lookbehind:!0,alias:"important",inside:{punctuation:/^#+|#+$/}}],hr:{pattern:/(^\s*)([*-])([\t ]*\2){2,}(?=\s*$)/m,lookbehind:!0,alias:"punctuation"},list:{pattern:/(^\s*)(?:[*+-]|\d+\.)(?=[\t ].)/m,lookbehind:!0,alias:"punctuation"},"url-reference":{pattern:/!?\[[^\]]+\]:[\t ]+(?:\S+|<(?:\\.|[^>\\])+>)(?:[\t ]+(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\)))?/,inside:{variable:{pattern:/^(!?\[)[^\]]+/,lookbehind:!0},string:/(?:"(?:\\.|[^"\\])*"|'(?:\\.|[^'\\])*'|\((?:\\.|[^)\\])*\))$/,punctuation:/^[\[\]!:]|[<>]/},alias:"url"},bold:{pattern:/(^|[^\\])(\*\*|__)(?:(?:\r?\n|\r)(?!\r?\n|\r)|.)+?\2/,lookbehind:!0,inside:{punctuation:/^\*\*|^__|\*\*$|__$/}},italic:{pattern:/(^|[^\\])([*_])(?:(?:\r?\n|\r)(?!\r?\n|\r)|.)+?\2/,lookbehind:!0,inside:{punctuation:/^[*_]|[*_]$/}},url:{pattern:/!?\[[^\]]+\](?:\([^\s)]+(?:[\t ]+"(?:\\.|[^"\\])*")?\)| ?\[[^\]\n]*\])/,inside:{variable:{pattern:/(!?\[)[^\]]+(?=\]$)/,lookbehind:!0},string:{pattern:/"(?:\\.|[^"\\])*"(?=\)$)/}}}}),Prism.languages.markdown.bold.inside.url=Prism.util.clone(Prism.languages.markdown.url),Prism.languages.markdown.italic.inside.url=Prism.util.clone(Prism.languages.markdown.url),Prism.languages.markdown.bold.inside.italic=Prism.util.clone(Prism.languages.markdown.italic),Prism.languages.markdown.italic.inside.bold=Prism.util.clone(Prism.languages.markdown.bold); +Prism.languages.matlab={string:/\B'(?:''|[^'\n])*'/,comment:[/%\{[\s\S]*?\}%/,/%.+/],number:/\b-?(?:\d*\.?\d+(?:[eE][+-]?\d+)?(?:[ij])?|[ij])\b/,keyword:/\b(?:break|case|catch|continue|else|elseif|end|for|function|if|inf|NaN|otherwise|parfor|pause|pi|return|switch|try|while)\b/,"function":/(?!\d)\w+(?=\s*\()/,operator:/\.?[*^\/\\']|[+\-:@]|[<>=~]=?|&&?|\|\|?/,punctuation:/\.{3}|[.,;\[\](){}!]/}; +Prism.languages.python={"triple-quoted-string":{pattern:/"""[\s\S]+?"""|'''[\s\S]+?'''/,alias:"string"},comment:{pattern:/(^|[^\\])#.*/,lookbehind:!0},string:{pattern:/("|')(?:\\\\|\\?[^\\\r\n])*?\1/,greedy:!0},"function":{pattern:/((?:^|\s)def[ \t]+)[a-zA-Z_][a-zA-Z0-9_]*(?=\()/g,lookbehind:!0},"class-name":{pattern:/(\bclass\s+)[a-z0-9_]+/i,lookbehind:!0},keyword:/\b(?:as|assert|async|await|break|class|continue|def|del|elif|else|except|exec|finally|for|from|global|if|import|in|is|lambda|pass|print|raise|return|try|while|with|yield)\b/,"boolean":/\b(?:True|False)\b/,number:/\b-?(?:0[bo])?(?:(?:\d|0x[\da-f])[\da-f]*\.?\d*|\.\d+)(?:e[+-]?\d+)?j?\b/i,operator:/[-+%=]=?|!=|\*\*?=?|\/\/?=?|<[<=>]?|>[=>]?|[&|^~]|\b(?:or|and|not)\b/,punctuation:/[{}[\];(),.:]/}; +Prism.languages.r={comment:/#.*/,string:/(['"])(?:\\?.)*?\1/,"percent-operator":{pattern:/%[^%\s]*%/,alias:"operator"},"boolean":/\b(?:TRUE|FALSE)\b/,ellipsis:/\.\.(?:\.|\d+)/,number:[/\b(?:NaN|Inf)\b/,/\b(?:0x[\dA-Fa-f]+(?:\.\d*)?|\d*\.?\d+)(?:[EePp][+-]?\d+)?[iL]?\b/],keyword:/\b(?:if|else|repeat|while|function|for|in|next|break|NULL|NA|NA_integer_|NA_real_|NA_complex_|NA_character_)\b/,operator:/->?>?|<(?:=|=!]=?|::?|&&?|\|\|?|[+*\/^$@~]/,punctuation:/[(){}\[\],;]/}; diff --git a/media/Aaron.jpg b/static/media/Aaron.jpg similarity index 100% rename from media/Aaron.jpg rename to static/media/Aaron.jpg diff --git a/media/Adrian.jpg b/static/media/Adrian.jpg similarity index 100% rename from media/Adrian.jpg rename to static/media/Adrian.jpg diff --git a/media/Brenda.jpg b/static/media/Brenda.jpg similarity index 100% rename from media/Brenda.jpg rename to static/media/Brenda.jpg diff --git a/media/Cesar.jpg b/static/media/Cesar.jpg similarity index 100% rename from media/Cesar.jpg rename to static/media/Cesar.jpg diff --git a/media/Cristian.jpg b/static/media/Cristian.jpg similarity index 100% rename from media/Cristian.jpg rename to static/media/Cristian.jpg diff --git a/media/Daniel.jpeg b/static/media/Daniel.jpeg similarity index 100% rename from media/Daniel.jpeg rename to static/media/Daniel.jpeg diff --git a/media/David.jpeg b/static/media/David.jpeg similarity index 100% rename from media/David.jpeg rename to static/media/David.jpeg diff --git a/media/Diego.jpg b/static/media/Diego.jpg similarity index 100% rename from media/Diego.jpg rename to static/media/Diego.jpg diff --git a/media/Edgar.png b/static/media/Edgar.png similarity index 100% rename from media/Edgar.png rename to static/media/Edgar.png diff --git a/media/Estrella.jpg b/static/media/Estrella.jpg similarity index 100% rename from media/Estrella.jpg rename to static/media/Estrella.jpg diff --git a/static/media/Gabriela.jpg b/static/media/Gabriela.jpg new file mode 100644 index 0000000..8c3f13f Binary files /dev/null and b/static/media/Gabriela.jpg differ diff --git a/media/IMG-20230228-WA0004.jpg b/static/media/IMG-20230228-WA0004.jpg similarity index 100% rename from media/IMG-20230228-WA0004.jpg rename to static/media/IMG-20230228-WA0004.jpg diff --git a/media/JJ_1.jpg b/static/media/JJ_1.jpg similarity index 100% rename from media/JJ_1.jpg rename to static/media/JJ_1.jpg diff --git a/media/JP.jpg b/static/media/JP.jpg similarity index 100% rename from media/JP.jpg rename to static/media/JP.jpg diff --git a/media/Jiovani.jpg b/static/media/Jiovani.jpg similarity index 100% rename from media/Jiovani.jpg rename to static/media/Jiovani.jpg diff --git a/media/Jorge.jpg b/static/media/Jorge.jpg similarity index 100% rename from media/Jorge.jpg rename to static/media/Jorge.jpg diff --git a/media/Juan.jpg b/static/media/Juan.jpg similarity index 100% rename from media/Juan.jpg rename to static/media/Juan.jpg diff --git a/media/LE_1.jpg b/static/media/LE_1.jpg similarity index 100% rename from media/LE_1.jpg rename to static/media/LE_1.jpg diff --git a/media/Laura.jpg b/static/media/Laura.jpg similarity index 100% rename from media/Laura.jpg rename to static/media/Laura.jpg diff --git a/static/media/Mayavi.jpg b/static/media/Mayavi.jpg new file mode 100644 index 0000000..349b16e Binary files /dev/null and b/static/media/Mayavi.jpg differ diff --git a/media/Mez.jpg b/static/media/Mez.jpg similarity index 100% rename from media/Mez.jpg rename to static/media/Mez.jpg diff --git a/media/Perla.jpg b/static/media/Perla.jpg similarity index 100% rename from media/Perla.jpg rename to static/media/Perla.jpg diff --git a/media/Thelma.jpg b/static/media/Thelma.jpg similarity index 100% rename from media/Thelma.jpg rename to static/media/Thelma.jpg diff --git a/media/Victor.jpg b/static/media/Victor.jpg similarity index 100% rename from media/Victor.jpg rename to static/media/Victor.jpg diff --git a/media/Yael.jpg b/static/media/Yael.jpg similarity index 100% rename from media/Yael.jpg rename to static/media/Yael.jpg diff --git a/media/akram.jpg b/static/media/akram.jpg similarity index 100% rename from media/akram.jpg rename to static/media/akram.jpg diff --git a/media/avazquez.jpg b/static/media/avazquez.jpg similarity index 100% rename from media/avazquez.jpg rename to static/media/avazquez.jpg diff --git a/static/media/background.png b/static/media/background.png new file mode 100644 index 0000000..0c949fe Binary files /dev/null and b/static/media/background.png differ diff --git a/static/media/background2.png b/static/media/background2.png new file mode 100644 index 0000000..c09ebd7 Binary files /dev/null and b/static/media/background2.png differ diff --git a/static/media/benjamin.jpg b/static/media/benjamin.jpg new file mode 100644 index 0000000..305cfea Binary files /dev/null and b/static/media/benjamin.jpg differ diff --git a/media/cancer_a_complex_disease_ebook.png b/static/media/cancer_a_complex_disease_ebook.png similarity index 100% rename from media/cancer_a_complex_disease_ebook.png rename to static/media/cancer_a_complex_disease_ebook.png diff --git a/media/carlos.jpg b/static/media/carlos.jpg similarity index 100% rename from media/carlos.jpg rename to static/media/carlos.jpg diff --git a/static/media/charly.jpg b/static/media/charly.jpg new file mode 100644 index 0000000..6cb0d04 Binary files /dev/null and b/static/media/charly.jpg differ diff --git a/static/media/chris.jpg b/static/media/chris.jpg new file mode 100644 index 0000000..4c725d1 Binary files /dev/null and b/static/media/chris.jpg differ diff --git a/static/media/cris.jpg b/static/media/cris.jpg new file mode 100644 index 0000000..31fbbe4 Binary files /dev/null and b/static/media/cris.jpg differ diff --git a/static/media/encyclopedia.jpg b/static/media/encyclopedia.jpg new file mode 100644 index 0000000..1d8b7d7 Binary files /dev/null and b/static/media/encyclopedia.jpg differ diff --git a/static/media/endophytes.jpg b/static/media/endophytes.jpg new file mode 100644 index 0000000..fb77057 Binary files /dev/null and b/static/media/endophytes.jpg differ diff --git a/static/media/erick.jpg b/static/media/erick.jpg new file mode 100644 index 0000000..c96f75c Binary files /dev/null and b/static/media/erick.jpg differ diff --git a/static/media/erika.jpg b/static/media/erika.jpg new file mode 100644 index 0000000..cbae5fc Binary files /dev/null and b/static/media/erika.jpg differ diff --git a/static/media/favicon.png b/static/media/favicon.png new file mode 100644 index 0000000..d820471 Binary files /dev/null and b/static/media/favicon.png differ diff --git a/static/media/felipe.jpg b/static/media/felipe.jpg new file mode 100644 index 0000000..62f0166 Binary files /dev/null and b/static/media/felipe.jpg differ diff --git a/media/fernanda.jpg b/static/media/fernanda.jpg similarity index 100% rename from media/fernanda.jpg rename to static/media/fernanda.jpg diff --git a/media/frontiers_ebook.jpg b/static/media/frontiers_ebook.jpg similarity index 100% rename from media/frontiers_ebook.jpg rename to static/media/frontiers_ebook.jpg diff --git a/media/group.jpg b/static/media/group.jpg similarity index 100% rename from media/group.jpg rename to static/media/group.jpg diff --git a/static/media/group.png b/static/media/group.png new file mode 100644 index 0000000..f04b84c Binary files /dev/null and b/static/media/group.png differ diff --git a/static/media/group_2014.jpg b/static/media/group_2014.jpg new file mode 100644 index 0000000..8ee7999 Binary files /dev/null and b/static/media/group_2014.jpg differ diff --git a/media/group_2014_2.jpg b/static/media/group_2014_2.jpg similarity index 100% rename from media/group_2014_2.jpg rename to static/media/group_2014_2.jpg diff --git a/media/ixchetl.jpg b/static/media/ixchetl.jpg similarity index 100% rename from media/ixchetl.jpg rename to static/media/ixchetl.jpg diff --git a/media/jarellano.jpg b/static/media/jarellano.jpg similarity index 100% rename from media/jarellano.jpg rename to static/media/jarellano.jpg diff --git a/media/layra.jpg b/static/media/layra.jpg similarity index 100% rename from media/layra.jpg rename to static/media/layra.jpg diff --git a/static/media/logo.png b/static/media/logo.png new file mode 100644 index 0000000..b9c2d50 Binary files /dev/null and b/static/media/logo.png differ diff --git a/static/media/lucy.jpg b/static/media/lucy.jpg new file mode 100644 index 0000000..5d407b1 Binary files /dev/null and b/static/media/lucy.jpg differ diff --git a/static/media/meztli.jpg b/static/media/meztli.jpg new file mode 100644 index 0000000..5a9051c Binary files /dev/null and b/static/media/meztli.jpg differ diff --git a/static/media/neuro.png b/static/media/neuro.png new file mode 100644 index 0000000..4bf32d1 Binary files /dev/null and b/static/media/neuro.png differ diff --git a/static/media/osbaldo.jpg b/static/media/osbaldo.jpg new file mode 100644 index 0000000..575ee24 Binary files /dev/null and b/static/media/osbaldo.jpg differ diff --git a/media/osbaldo.png b/static/media/osbaldo.png similarity index 100% rename from media/osbaldo.png rename to static/media/osbaldo.png diff --git a/static/media/paola.jpg b/static/media/paola.jpg new file mode 100644 index 0000000..5730587 Binary files /dev/null and b/static/media/paola.jpg differ diff --git a/static/media/placeholder.svg b/static/media/placeholder.svg new file mode 100644 index 0000000..1eee807 --- /dev/null +++ b/static/media/placeholder.svg @@ -0,0 +1,78 @@ + + + +image/svg+xml \ No newline at end of file diff --git a/media/quantitative_models.jpg b/static/media/quantitative_models.jpg similarity index 100% rename from media/quantitative_models.jpg rename to static/media/quantitative_models.jpg diff --git a/static/media/twitter.svg b/static/media/twitter.svg new file mode 100644 index 0000000..eea7a03 --- /dev/null +++ b/static/media/twitter.svg @@ -0,0 +1,66 @@ + + + + + + + image/svg+xml + + Twitter_Logo_Blue + + + + + + + Twitter_Logo_Blue + + + diff --git a/media/ugo.jpg b/static/media/ugo.jpg similarity index 100% rename from media/ugo.jpg rename to static/media/ugo.jpg diff --git a/media/vanessa.jpg b/static/media/vanessa.jpg similarity index 100% rename from media/vanessa.jpg rename to static/media/vanessa.jpg diff --git a/tags/index.xml b/tags/index.xml deleted file mode 100644 index 8ebe5fe..0000000 --- a/tags/index.xml +++ /dev/null @@ -1 +0,0 @@ -Tags on Webpage of the Resendis Labhttps://resendislab.github.io/tags/Recent content in Tags on Webpage of the Resendis LabHugo -- gohugo.ioen-us \ No newline at end of file