multiTFA: A python package for multi-variate thermodynamics-based flux analysis

Vishnuvardhan Mahamkali, Tim McCubbin, Moritz Emanuel Beber, Elad Noor, Esteban Marcellin, Lars Keld Nielsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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
Original languageEnglish
Issue number18
Pages (from-to)3064-3066
Number of pages3
Publication statusPublished - 2021


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