ArticleBioinformatics (Oxford, England)2019
pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
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- Metabolic control analysis enabled the improvement of the L-cysteine production process with Escherichia coli.Applied microbiology and biotechnology · 2024Article
- Rational strain design with minimal phenotype perturbation.Nature communications · 2024Article
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- From microbiome composition to functional engineering, one step at a time.Microbiology and molecular biology reviews : MMBR · 2023Review
- Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO.Microbiology spectrum · 2023Article
- Growth in fluctuating light buffers plants against photorespiratory perturbations.Nature communications · 2023Article
- Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering.ACS synthetic biology · 2023Article
- Exploring the Feasibility of Cell-Free Synthesis as a Platform for Polyhydroxyalkanoate (PHA) Production: Opportunities and Challenges.Polymers · 2023Review
- Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations.Nature communications · 2023Article
- Novel context-specific genome-scale modelling explores the potential of triacylglycerol production by Chlamydomonas reinhardtii.Microbial cell factories · 2023Article
- Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models.Bioinformatics (Oxford, England) · 2023Article
- Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression.Nature communications · 2022Article
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6 authors.
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Abstract
Summary: pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements. Motivation: High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions. Results: We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli., and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA. Availability and implementation: Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/. Supplementary information: Supplementary data are available at Bioinformatics online.
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