Literature DB >> 33682879

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

Vishnuvardhan Mahamkali1, Tim McCubbin1, Moritz Emanuel Beber2, Elad Noor3, Esteban Ma1, Lars Keld Marcellin1, Lars Keld Nielsen1,2.   

Abstract

SUMMARY: 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.

Entities:  

Year:  2021        PMID: 33682879     DOI: 10.1093/bioinformatics/btab151

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  2 in total

1.  eQuilibrator 3.0: a database solution for thermodynamic constant estimation.

Authors:  Moritz E Beber; Mattia G Gollub; Dana Mozaffari; Kevin M Shebek; Avi I Flamholz; Ron Milo; Elad Noor
Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

Review 2.  Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges.

Authors:  Xinyu Bi; Yanfeng Liu; Jianghua Li; Guocheng Du; Xueqin Lv; Long Liu
Journal:  Biomolecules       Date:  2022-05-19
  2 in total

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