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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 r...

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Detalles Bibliográficos
Autores principales: Mahamkali, Vishnuvardhan, McCubbin, Tim, Beber, Moritz Emanuel, Noor, Elad, Marcellin, Esteban, Nielsen, Lars Keld
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479682/
https://www.ncbi.nlm.nih.gov/pubmed/33682879
http://dx.doi.org/10.1093/bioinformatics/btab151
Descripción
Sumario: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.