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


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

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