MMNetReduce

Description

This project aim to reduce the number of reactions in a metabolic network by applying Michaelis-Menten reduction. The program take as input a SBML file and return a SBML file if there's a Michaelis-Menten reduction applied.

Content

1. Installation
2. Usage
3. Project structure
4. Data
5. Scripts
6. Authors
7. References

Installation

To install the program, you need to have python3 installed on your computer.

1. Clone the repository

git clone https://github.com/ClementRasp/MMNetReduce.git

or you can download the zip file and extract it.

2. Install the requirements

pip install -r requirements.txt

Usage

To run the program, there's mutliple options:

1. Run the program with windows for selecting files in input and the directory for output.

python3 MMNetReduce.py

2. Run the program with options for input and output files.

• input_file_or_directory: specify the input file, can be a file or a directory (in this case the program gonna take all .xml files in the directory)
• -o: specify the output directory (Optional)

python3 MMNetReduce.py [-o output_directory] input_file_or_directory_1 ... input_file_or_directory_*

Project structure

• MMNetReduce.py : Main file of the program from the paper
• MMNetReduce_2.py : A general methode to compute Michaelis-Menten Reductions
• useful.py : Methods for manipulating files
• merge_reaction.py : Methods for reductiong biomodel irreversible reactions into reversible
• requirements.txt : File containing the required libraries

Data

The data used for the project are manually curated biomodel from EBI.

Scripts

• MMNetReduce.py : contains the code for applying Michaelis-Menten reduction following the method described in the paper.
• MMNetReduce_2.py : contains the code for applying Michaelis-Menten reduction following the general method of computing the intermediate of the reactions by using the rates of the reactions.
• merge_reaction.py : contains the code for merging the irreversible reactions into reversible reactions.
• useful.py : contains functions used by MMNetReduce.py and MMNetReduce_2.py.

Authors

• Manvel Gasparyan - manvelgasparyan
• Clément Raspail - ClementRasp

References

• Nisha Ann Viswan, Alexandre Tribut, Manvel Gasparyan, Ovidiu Radulescu, Upinder S Bhalla. Hierarchical Optimization of Biochemical Networks. 2024. hal-04593669