# A software module to assess the metabolic potential of mutant strains of the bacterium Corynebacterium glutamicum

## TL;DR

This paper introduces fluxMicrobiotech, a software tool for analyzing the metabolism of the bacterium Corynebacterium glutamicum using computational models.

## Contribution

The novel contribution is the development of fluxMicrobiotech, a software module for high-performance analysis of C. glutamicum genome-scale metabolic models.

## Key findings

- fluxMicrobiotech is based on Python libraries and allows high-performance analysis of genome-scale flux balance models.
- The tool supports file-in/file-out workflows and enables testing of models under various conditions.
- It includes post-processing tools for visualizing metabolic data through charts and maps.

## Abstract

Technologies for the production of a range of compounds using microorganisms are becoming increasingly popular in industry. The creation of highly productive strains whose metabolism is aimed to the synthesis of a specific desired product is impossible without complex directed modifications of the genome using mathematical and computer modeling methods. One of the bacterial species actively used in biotechnological production is Corynebacterium glutamicum. There are already 5 whole-genome flux balance models for it, which can be used for metabolism research and optimization tasks. The paper presents fluxMicrobiotech, a software module developed at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, which implements a series of computational protocols designed for high-performance computer analysis of C. glutamicum whole-genome flux balance models. The tool is based on libraries from the opencobra community (https://opencobra.github.io)
within the Python programming language (https://www.python.org), using the Pandas (https://pandas.pydata.org) and Escher (https://escher.readthedocs.io) libraries . It is configured to operate on a ‘file-in/file-out’ basis. The model, environmental conditions, and model constraints are specified as separate text table files, which allows one to prepare a series of files for each section, creating databases of available test scenarios for variations of the model. Or vice versa, allowing a single model to be tested under a series of different cultivation conditions. Post-processing tools for modeling data are set up, providing visualization of summary charts and metabolic maps.