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

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.

Automated summary

The study introduced multivariate treatment and component contribution method to improve thermodynamic-based flux analysis (TFA), greatly reducing the uncertainty of thermodynamic variables. Using the Escherichia coli model, a significant reduction in Gibbs free energy ranges and some reactions changing from reversible to irreversible were achieved.