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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 |
REPOSITORIES: biostudies-literature