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BioModTool: from biomass composition data to structured biomass objective functions for genome-scale metabolic

Clémence Dupont Thibert^1,2, Sylvaine Roy¹, Gilles Curien¹

¹Laboratoire de Physiologie Cellulaire et Végétale, Interdisciplinary Research Institute of Grenoble, Université Grenoble Alpes, Grenoble 38000, France.

Bioinformatics Advances

|March 11, 2025

Summary

BioModTool simplifies creating biomass objective functions for genome-scale metabolic models. This Python program aids researchers in reconstructing and analyzing metabolic models using user-provided biomass data.

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Area of Science:

Metabolic Engineering
Computational Biology
Systems Biology

Background:

Genome-scale metabolic models (GEMs) are crucial for understanding cellular metabolism.
Accurate biomass objective functions are essential for GEMs but can be challenging to generate.
Existing methods for biomass function creation may require specialized programming skills.

Purpose of the Study:

To develop BioModTool, a user-friendly Python program for generating biomass objective functions for GEMs.
To streamline the process of updating metabolic models with accurate biomass compositions.
To facilitate the use of GEMs for a wider range of researchers, including non-modelers.

Main Methods:

BioModTool accepts user-defined biomass composition data in an Excel file format.
The program normalizes input data into model-compatible units (mmol.gDW^-1).
It generates a structured biomass objective function for updating GEMs, compatible with COBRApy.

Main Results:

BioModTool provides an accessible method for creating and updating biomass objective functions.
The tool supports both programmatic use as a Python module and a graphical user interface.
It accelerates the reconstruction and improvement of GEMs.

Conclusions:

BioModTool simplifies the generation of biomass objective functions, enhancing GEM usability.
The software facilitates the analysis of biomass-specific experimental data with GEMs.
It democratizes the use of metabolic modeling tools for diverse research applications.