Motivation: 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.
© The Author(s) 2021. Published by Oxford University Press.