Abstract
MotivationWe 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.ResultsWe 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 implementationOur framework along with documentation is available on https://github.com/biosustain/multitfa.Supplementary informationSupplementary data are available at Bioinformatics online.
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