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MultiTFA: a python package for multi-variate thermodynamics-based flux analysis.

ABSTRACT:

Summary

We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables.

Results

We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible.

Availability and implementation

Our framework along with documentation is available on https://github.com/biosustain/multitfa.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Mahamkali V

PROVIDER: S-EPMC8479682 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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multiTFA: a Python package for multi-variate thermodynamics-based flux analysis.

Mahamkali Vishnuvardhan V McCubbin Tim T Beber Moritz Emanuel ME Noor Elad E Marcellin Esteban E Nielsen Lars Keld LK

Bioinformatics (Oxford, England) 20210901 18

<h4>Motivation</h4>We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables.<h4>Results</h4>We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli mod ...[more]

PMID: 33682879

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