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

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.