Biochemical cOmplex data Integration in Metabolic Models at Genome-scale (BOIMMG)

Description

Several ontologies were designed to represent the structural relationships between chemical species. However, they fail in capturing the functional relationships between compounds and their biosynthetic precursors. This is highly relevant in the context of GSM modelling, as it eases the process of converting generic biosynthetic pathways into specific ones and vice-versa. Moreover, to the best of our knowledge, there is not any computational approach to integrate such information.

BOIMMG is a novel and modular approach aiming at tackling several issues in the representation of lipids in GSM models.

• Figure 1 - BOIMMG's pipeline - 1 - Integration of several databases; 2 -Semi-automated knowledge expansion 3 - integration of those ontologies into GSM models

However, a web-service is available here.

Table of contents:

• Requirements

• Installation

  • Pip
  • Docker

• Getting Started

  • Access to the database
  • LipidGranulator
    • Granulate only with lipids from specific sources
  • CofactorSwapper
  • Write reports

• About Us

• Citing BOIMMG

Requirements

• rdkit
• cobra
• pandas
• numpy
• python-libsbml
• requests~=2.22.0
• biopython
• lxml
• openpyxl
• biocyc~=0.2.0
• sympy~=1.7.1
• neo4j~=4.2.1
• cobrababel~=0.1.3
• setuptools~=52.0.0

Installation

Pip

Install BOIMMG via pip or conda:

pip install boimmg

Docker

(IN PREPARATION - NOT FUNCTIONAL YET!)

1. Install docker.
2. Pull an existing image (X.XGb to download) from DockerHub:

docker pull XXX

or clone the repository and build it manually:

git clone 
docker build ...

3. Create a container:

docker run ...

Manually

Alternatively, install dependencies and BOIMMG manually.

1. Clone the repository:

git clone xx

3. Install dependencies:

python setup.py install

Getting Started

BOIMMG is a package that compiles representation problems solutions for Genome Scale Metabolic Models (GSMM). Currently, we provide tools to granulate lipids (LipidGranulator) and swap co-factors (CofactorSwapper).

BOIMMG framework is still under development and it is currently at a pre-release version. New models and features will be added in the future.

Access to the database

BOIMMG provides an integrated database that compiles LIPID MAPS, Swiss Lipids and ModelSEED information. As for the access to the database, you can either use our rest API (default) or use a local database. The latter can be set up following the instructions bellow.

Set up a local BOIMMG's database from the command line

1. Download and install neo4j in https://neo4j.com/download/.
2. Download the database dump in https://zenodo.org/record/5163250/files/boimmg.dump.
3. Open neo4j desktop and create a new project.

4. Create a local DBMS in the created project.

5. Define the password and choose version 3.5.19.
6. Go to the database folder using the help of neo4j desktop GUI.

7. Now copy the folder path of the "bin" folder and go there in the command line.

cd <folder path of "bin" folder>

8. Load BOIMMG's database using neo4j-admin.

neo4j-admin load --from=<path to dump file> --database=graph.db

9. Go to your project in neo4j Desktop and start your database.

10. Press the database box and information will be displayed on the right. Copy the "bolt port".

11. The database is already set up, however, if you want to improve the performance of your database you should increase the heap maximum size. For that you have to access to the database settings as in the figure bellow and change the parameter dbms.memory.heap.max_size to the memory you want to allocate.

12. Finally, you are ready to set up the database in the package!

from boimmgpy.database.accessors.compounds_database_accessor import set_database_information

set_database_information(uri="bolt://localhost:<bolt port>", user="neo4j", password="<your password>")

LipidGranulator

The LipidGranulator allows you to convert your model's lipids from a generic to a structurally defined version

import cobra
from boimmgpy.representation_changers import LipidGranulator
from boimmgpy.database.accessors.compounds_database_accessor import CompoundsDBAccessor

model = cobra.io.read_sbml_model("iJR904_mapped.xml")

# specify the components (fatty acids or alcohol chains) for the granulation 
# the lipids that the LipidGranulator will generate will directly depend on this list
# note that the identifiers do not have to be the ones in the model,
# just ModelSEED, BiGG, KEGG or BOIMMG identifiers so that the compounds can be identified
components = ["cpd00214", "cpd03847", "cpd05274", "cpd25615", "cpd05237"]

# function call
solver = LipidGranulator(model, "BiGG")

#funtion call to use a local database
accessor = CompoundsDBAccessor()
solver = LipidGranulator(model, "BiGG",db_accessor=accessor)

# Map the model so that internal processes can recognize which metabolites are in the model 
solver.map_model()

# swap from generic: the first parameter is a list of generic lipids to be granulated,
# the second is a list with the components and the last is a boolean that tells whether 
# you want to combine components or use the same without combinations in each lipid
solver.swap_from_generic(["cpd22513", "cpd15649"], components, True)

# generate ISA reactions for each biomass contributor lipid
solver.generate_isa_reactions()

cobra.io.write_sbml_model(solver.model, "granulated_iJR904.xml")

Granulate only with lipids from specific sources

import cobra
from boimmgpy.representation_changers import LipidGranulator

model = cobra.io.read_sbml_model("iJR904_mapped.xml")

# specify the components (fatty acids or alcohol chains) for the granulation 
# the lipids that the LipidGranulator will generate will directly depend on this list
# note that the identifiers do not have to be the ones in the model,
# just ModelSEED, BiGG, KEGG or BOIMMG identifiers so that the compounds can be identified
components = ["cpd00214", "cpd03847", "cpd05274", "cpd25615", "cpd05237"]

# function call
solver = LipidGranulator(model, "BiGG")

# Map the model so that internal processes can recognize which metabolites are in the model 
solver.map_model()

# here we will only granulate a generic lipid with the ones from LIPID MAPS
solver.swap_from_generic(["cpd22513", "cpd15649"], components, False, sources=["LIPID MAPS"])

#here we will only granulate a generic lipid with the ones from LIPID MAPS AND Swiss Lipids
solver.swap_from_generic(["cpd22513", "cpd15649"], components, False, sources=["LIPID MAPS", "SwissLipids"])

CofactorSwapper

import cobra
from boimmgpy.representation_changers import CofactorSwapper
from boimmgpy.database.accessors.compounds_database_accessor import CompoundsDBAccessor

model = cobra.io.read_sbml_model("/models/iMM904.xml")

# The second parameter is the database format of the model: it can be ModelSEED, BiGG and KEGG
rep_case_solver = CofactorSwapper(model, "ModelSEED")

#funtion call to use a local database
accessor = CompoundsDBAccessor()
solver = CofactorSwapper(model, "ModelSEED",db_accessor=accessor)


# Map the model so that internal processes can recognize which metabolites are in the model 
rep_case_solver.map_model()
# Swap cofactor: the first parameter is the database identifier of the cofactor 
# already in the model and the second is the database identifier of the cofactor that will replace the first
rep_case_solver.swap_compound("cpd15290", "cpd11669")

Write reports

import cobra
from boimmgpy.representation_changers import CofactorSwapper

model = cobra.io.read_sbml_model("/models/iMM904.xml")

rep_case_solver = CofactorSwapper(model, "ModelSEED")
rep_case_solver.map_model()
rep_case_solver.swap_compound("cpd15290", "cpd11669")

rep_case_solver.write_report("new_report.txt")

import cobra
from boimmgpy.representation_changers import LipidGranulator

model = cobra.io.read_sbml_model("iJR904_mapped.xml")

components = ["cpd00214", "cpd03847", "cpd05274", "cpd25615", "cpd05237"]

solver = LipidGranulator(model, "BiGG")
solver.map_model()
solver.swap_from_generic(["cpd22513", "cpd15649"], components, True)
solver.generate_isa_reactions()

# write the report
solver.write_report("new_report.txt")

cobra.io.write_sbml_model(solver.model, "granulated_iJR904.xml")

The report will look like this:

--Metabolites--
metabolite in model,replacer metabolite
q6_m,cpd23449_m
2hpmhmbq_m,BMGC749195_m
2hpmmbq_m,BMGC749194_m
2hp6mbq_m,BMGC749193_m
2hp6mp_m,BMGC749192_m
3hph5mb_m,BMGC749243_m
3dh5hpb_c,BMGC749242_c
3dh5hpb_m,BMGC749242_m
q6h2_m,BMGC749241_m
--Reactions--
reaction in model,replacer reaction
2HMHMBQMTm,BMGR_BMGC749195_m_ahcys_m_amet_m_cpd23449_m_h_m
CYOR_u6m,BMGR_BMGC749241_m_cpd23449_m_ficytc_m_focytc_m_h_c_h_m
DHORD4i,BMGR_BMGC749241_m_cpd23449_m_dhor__S_c_orot_c
DXHPScm,BMGR_13dampp_c_4abutn_c_BMGC749241_m_cpd23449_m_h2o_c_spmd_c
FDNG,BMGR_BMGC749241_m_co2_c_cpd23449_m_for_c_h_c
NADH2_u6cm,BMGR_BMGC749241_m_cpd23449_m_h_c_nad_c_nadh_c
NADH2_u6m,BMGR_BMGC749241_m_cpd23449_m_h_m_nad_m_nadh_m
SUCD2_u6m,BMGR_BMGC749241_m_cpd23449_m_fum_m_succ_m
SUCD3_u6m,BMGR_BMGC749241_m_cpd23449_m_fad_m_fadh2_m
2HPMMBQMOm,BMGR_BMGC749194_m_BMGC749195_m_o2_m
2HPMBQMTm,BMGR_BMGC749193_m_BMGC749194_m_ahcys_m_amet_m_h_m
2HP6MPMOm,BMGR_BMGC749192_m_BMGC749193_m_h2o_m_o2_m
3HPH5MBDCm,BMGR_BMGC749192_m_BMGC749243_m_co2_m
3DH5HPBMTm,BMGR_BMGC749242_m_BMGC749243_m_ahcys_m_amet_m_h_m
3DH5HPBtm,BMGR_BMGC749242_c_BMGC749242_m
3OPHB5Hm,BMGR_3ophb_5_c_BMGC749242_c_o2_c

About Us

Citing BOIMMG

Manuscript under preparation.

Licensing

BSD-2-Clause License.