Doc: Update references (#1292)

Papers using pypesto
ICB-DCM · May 14, 2024 · 1206dc0 · 1206dc0
1 parent 9d5c13b
commit 1206dc0
Showing 1 changed file with 87 additions and 11 deletions.
diff --git a/doc/using_pypesto.bib b/doc/using_pypesto.bib
@@ -79,17 +79,19 @@ @Article{FroehlichSor2022
 }
 
 @Article{FroehlichGer2022,
-  author       = {Fr{\"o}hlich, Fabian and Gerosa, Luca and Muhlich, Jeremy and Sorger, Peter K.},
-  journal      = {bioRxiv},
-  title        = {Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance},
-  year         = {2022},
-  abstract     = {BRAFV600E is prototypical of oncogenic mutations that can be targeted therapeutically and treatment of BRAF-mutant melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring causes BRAFV600E melanoma cells to rapidly acquire a drug-adapted state. In patients this is thought to promote acquisition or selection for resistance mutations and disease recurrence. In this paper we use an energy-based implementation of ordinary differential equations in combination with proteomic, transcriptomic and imaging data from melanoma cells, to model the precise mechanisms responsible for adaptive rewiring. We demonstrate the presence of two parallel MAPK (RAF-MEK-ERK kinase) reaction channels in BRAFV600E melanoma cells that are differentially sensitive to RAF and MEK inhibitors. This arises from differences in protein oligomerization and allosteric regulation induced by oncogenic mutations and drug binding. As a result, the RAS-regulated MAPK channel can be active under conditions in which the BRAFV600E-driven channel is fully inhibited. Causal tracing demonstrates that this provides a sufficient quantitative explanation for initial and acquired responses to multiple different RAF and MEK inhibitors individually and in combination.HighlightsA thermodynamic framework enables structure-based description of allosteric interactions in the EGFR and MAPK pathwaysCausal decomposition of efficacy of targeted drugs elucidates rewiring of MAPK channelsModel-based extrapolation from type I{\textonehalf} RAF inhibitors to type II RAF inhibitorsA unified mechanistic explanation for adaptive and genetic resistance across BRAF-cancersCompeting Interest StatementPKS is a member of the SAB or Board of Directors of Glencoe Software, Applied Biomath, and RareCyte Inc. and has equity in these companies; PKS is also a member of the SAB of NanoString and a consultant for Montai Health and Merck. LG is currently an employee of Genentech. PKS and LG declare that none of these relationships are directly or indirectly related to the content of this manuscript.},
-  creationdate = {2023-01-26T11:32:12},
-  doi          = {10.1101/2022.02.17.480899},
-  elocation-id = {2022.02.17.480899},
-  eprint       = {https://www.biorxiv.org/content/early/2022/02/18/2022.02.17.480899.full.pdf},
-  publisher    = {Cold Spring Harbor Laboratory},
-  url          = {https://www.biorxiv.org/content/early/2022/02/18/2022.02.17.480899},
+  author           = {Fr{\"o}hlich, Fabian and Gerosa, Luca and Muhlich, Jeremy and Sorger, Peter K.},
+  journal          = {bioRxiv},
+  title            = {Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance},
+  year             = {2022},
+  abstract         = {BRAFV600E is prototypical of oncogenic mutations that can be targeted therapeutically and treatment of BRAF-mutant melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring causes BRAFV600E melanoma cells to rapidly acquire a drug-adapted state. In patients this is thought to promote acquisition or selection for resistance mutations and disease recurrence. In this paper we use an energy-based implementation of ordinary differential equations in combination with proteomic, transcriptomic and imaging data from melanoma cells, to model the precise mechanisms responsible for adaptive rewiring. We demonstrate the presence of two parallel MAPK (RAF-MEK-ERK kinase) reaction channels in BRAFV600E melanoma cells that are differentially sensitive to RAF and MEK inhibitors. This arises from differences in protein oligomerization and allosteric regulation induced by oncogenic mutations and drug binding. As a result, the RAS-regulated MAPK channel can be active under conditions in which the BRAFV600E-driven channel is fully inhibited. Causal tracing demonstrates that this provides a sufficient quantitative explanation for initial and acquired responses to multiple different RAF and MEK inhibitors individually and in combination.HighlightsA thermodynamic framework enables structure-based description of allosteric interactions in the EGFR and MAPK pathwaysCausal decomposition of efficacy of targeted drugs elucidates rewiring of MAPK channelsModel-based extrapolation from type I{\textonehalf} RAF inhibitors to type II RAF inhibitorsA unified mechanistic explanation for adaptive and genetic resistance across BRAF-cancersCompeting Interest StatementPKS is a member of the SAB or Board of Directors of Glencoe Software, Applied Biomath, and RareCyte Inc. and has equity in these companies; PKS is also a member of the SAB of NanoString and a consultant for Montai Health and Merck. LG is currently an employee of Genentech. PKS and LG declare that none of these relationships are directly or indirectly related to the content of this manuscript.},
+  creationdate     = {2023-01-26T11:32:12},
+  doi              = {10.1101/2022.02.17.480899},
+  elocation-id     = {2022.02.17.480899},
+  eprint           = {https://www.biorxiv.org/content/early/2022/02/18/2022.02.17.480899.full.pdf},
+  modificationdate = {2024-05-13T09:29:21},
+  publisher        = {Cold Spring Harbor Laboratory},
+  ranking          = {rank1},
+  url              = {https://www.biorxiv.org/content/early/2022/02/18/2022.02.17.480899},
 }
 
 @Article{GerosaChi2020,
@@ -207,4 +209,78 @@ @Article{FischerHolzhausenRoe2023
   url          = {https://www.biorxiv.org/content/early/2023/01/19/2023.01.17.523407},
 }
 
+@Article{KissVen2024,
+  author           = {Kiss, Anna E and Venkatasubramani, Anuroop V and Pathirana, Dilan and Krause, Silke and Sparr, Aline Campos and Hasenauer, Jan and Imhof, Axel and Müller, Marisa and Becker, Peter B},
+  journal          = {Nucleic Acids Research},
+  title            = {{Processivity and specificity of histone acetylation by the male-specific lethal complex}},
+  year             = {2024},
+  issn             = {0305-1048},
+  month            = {02},
+  pages            = {gkae123},
+  abstract         = {{Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus. We used targeted mass spectrometry to examine the activity of MOF in the male-specific lethal core (4-MSL) complex on nucleosome array substrates. This complex is part of the Dosage Compensation Complex (DCC) that activates X-chromosomal genes in male Drosophila. During short reaction times, MOF acetylated H4K16 efficiently and with excellent selectivity. Upon longer incubation, the enzyme progressively acetylated lysines 12, 8 and 5, leading to a mixture of oligo-acetylated H4. Mathematical modeling suggests that MOF recognizes and acetylates H4K16 with high selectivity, but remains substrate-bound and continues to acetylate more N-terminal H4 lysines in a processive manner. The 4-MSL complex lacks non-coding roX RNA, a critical component of the DCC. Remarkably, addition of RNA to the reaction non-specifically suppressed H4 oligo-acetylation in favor of specific H4K16 acetylation. Because RNA destabilizes the MSL-nucleosome interaction in vitro we speculate that RNA accelerates enzyme-substrate turn-over in vivo, thus limiting the processivity of MOF, thereby increasing specific H4K16 acetylation.}},
+  creationdate     = {2024-02-28T18:27:01},
+  doi              = {10.1093/nar/gkae123},
+  eprint           = {https://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkae123/56756494/gkae123.pdf},
+  modificationdate = {2024-02-28T18:27:01},
+  url              = {https://doi.org/10.1093/nar/gkae123},
+}
+
+@Article{DoresicGre2024,
+  author           = {Domagoj Dore{\v s}i{\'c} and Stephan Grein and Jan Hasenauer},
+  journal          = {bioRxiv},
+  title            = {Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data},
+  year             = {2024},
+  abstract         = {Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a non-linear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared. Here, we propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods. Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO).Competing Interest StatementThe authors have declared no competing interest.},
+  creationdate     = {2024-04-20T13:06:42},
+  doi              = {10.1101/2024.01.26.577371},
+  elocation-id     = {2024.01.26.577371},
+  eprint           = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371.full.pdf},
+  modificationdate = {2024-04-20T13:06:42},
+  publisher        = {Cold Spring Harbor Laboratory},
+  url              = {https://www.biorxiv.org/content/early/2024/01/30/2024.01.26.577371},
+}
+
+@Article{ArrudaSch2023,
+  author           = {Jonas Arruda and Yannik Sch{\"a}lte and Clemens Peiter and Olga Teplytska and Ulrich Jaehde and Jan Hasenauer},
+  journal          = {bioRxiv},
+  title            = {An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation},
+  year             = {2023},
+  abstract         = {Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in various fields, including biology, medicine, economics, and engineering. However, fitting these models to data is computationally challenging if the description of individuals is complex and the population is large. To address this issue, we propose a novel machine learning-based approach: We exploit neural density estimation based on normalizing flows to approximate individual-specific posterior distributions in an amortized fashion, thereby allowing for an efficient inference of population parameters. Applying this approach to problems from cell biology and pharmacology, we demonstrate its scalability to large data sets in an unprecedented manner. Moreover, we show that it enables accurate uncertainty quantification and extends to stochastic models, which established methods, such as SAEM and FOCEI are unable to handle. Thus, our approach outperforms state-of-the-art methods and improves the analysis capabilities for heterogeneous populations.Competing Interest StatementThe authors have declared no competing interest.},
+  creationdate     = {2024-04-22T12:56:00},
+  doi              = {10.1101/2023.08.22.554273},
+  elocation-id     = {2023.08.22.554273},
+  eprint           = {https://www.biorxiv.org/content/early/2023/08/23/2023.08.22.554273.full.pdf},
+  modificationdate = {2024-04-22T12:56:00},
+  publisher        = {Cold Spring Harbor Laboratory},
+  url              = {https://www.biorxiv.org/content/early/2023/08/23/2023.08.22.554273},
+}
+
+@Article{MerktAli2024,
+  author           = {Merkt, Simon and Ali, Solomon and Gudina, Esayas Kebede and Adissu, Wondimagegn and Gize, Addisu and Muenchhoff, Maximilian and Graf, Alexander and Krebs, Stefan and Elsbernd, Kira and Kisch, Rebecca and Betizazu, Sisay Sirgu and Fantahun, Bereket and Bekele, Delayehu and Rubio-Acero, Raquel and Gashaw, Mulatu and Girma, Eyob and Yilma, Daniel and Zeynudin, Ahmed and Paunovic, Ivana and Hoelscher, Michael and Blum, Helmut and Hasenauer, Jan and Kroidl, Arne and Wieser, Andreas},
+  journal          = {Nature Communications},
+  title            = {Long-term monitoring of SARS-CoV-2 seroprevalence and variants in Ethiopia provides prediction for immunity and cross-immunity},
+  year             = {2024},
+  issn             = {2041-1723},
+  month            = apr,
+  number           = {1},
+  volume           = {15},
+  creationdate     = {2024-04-29T08:32:16},
+  doi              = {10.1038/s41467-024-47556-2},
+  modificationdate = {2024-04-29T08:32:16},
+  publisher        = {Springer Science and Business Media LLC},
+}
+
+@Article{FalcoCoh2024a,
+  author           = {Falcó, Carles and Cohen, Daniel J. and Carrillo, José A. and Baker, Ruth E.},
+  journal          = {Biophysical Journal},
+  title            = {Quantifying cell cycle regulation by tissue crowding},
+  year             = {2024},
+  issn             = {0006-3495},
+  month            = may,
+  creationdate     = {2024-05-13T09:29:26},
+  doi              = {10.1016/j.bpj.2024.05.003},
+  modificationdate = {2024-05-13T09:29:26},
+  publisher        = {Elsevier BV},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}