ArticleBiophysical journal2013
Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models.
Article in Biophysical journal, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Thermodynamic metabolic modeling of growth and bioproduction potential of the acetogenSynthetic and systems biotechnology · 2026Article
- The Gibbs free energy landscape based on liver metabolome revealed thermodynamic robustness against fasting and obesity.iScience · 2026Article
- Growth in fluctuating light buffers plants against photorespiratory perturbations.Nature communications · 2023Article
- Article
- Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis.Scientific reports · 2021Article
- Quantum Mechanical Methods Predict Accurate Thermodynamics of Biochemical Reactions.ACS omega · 2021Article
- The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale.Microorganisms · 2020Review
- LK-DFBA: a linear programming-based modeling strategy for capturing dynamics and metabolite-dependent regulation in metabolism.BMC bioinformatics · 2020Article
- Large-scale kinetic metabolic models ofBiotechnology for biofuels · 2020Article
- Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light.Plant physiology · 2019Article
- Temperature-Dependent Estimation of Gibbs Energies Using an Updated Group-Contribution Method.Biophysical journal · 2018Article
- Theoretical Studies of Intracellular Concentration of Micro-organisms' Metabolites.Scientific reports · 2017Article
- Thermodynamic Constraints Improve Metabolic Networks.Biophysical journal · 2017Article
- MapMaker and PathTracer for tracking carbon in genome-scale metabolic models.Biotechnology journal · 2016Article
- Thermodynamics and H2 Transfer in a Methanogenic, Syntrophic Community.PLoS computational biology · 2015Article
- Predicting internal cell fluxes at sub-optimal growth.BMC systems biology · 2015Article
- Metabolomics integrated elementary flux mode analysis in large metabolic networks.Scientific reports · 2015Article
- Network thermodynamic curation of human and yeast genome-scale metabolic models.Biophysical journal · 2014Article
- Variability of metabolite levels is linked to differential metabolic pathways in Arabidopsis's responses to abiotic stresses.PLoS computational biology · 2014Article
- Metabolic network discovery by top-down and bottom-up approaches and paths for reconciliation.Frontiers in bioengineering and biotechnology · 2014Review
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3 authors.
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Abstract
Constraint-based methods provide powerful computational techniques to allow understanding and prediction of cellular behavior. These methods rely on physiochemical constraints to eliminate infeasible behaviors from the space of available behaviors. One such constraint is thermodynamic feasibility, the requirement that intracellular flux distributions obey the laws of thermodynamics. The past decade has seen several constraint-based methods that interpret this constraint in different ways, including those that are limited to small networks, rely on predefined reaction directions, and/or neglect the relationship between reaction free energies and metabolite concentrations. In this work, we utilize one such approach, thermodynamics-based metabolic flux analysis (TMFA), to make genome-scale, quantitative predictions about metabolite concentrations and reaction free energies in the absence of prior knowledge of reaction directions, while accounting for uncertainties in thermodynamic estimates. We applied TMFA to a genome-scale network reconstruction of Escherichia coli and examined the effect of thermodynamic constraints on the flux space. We also assessed the predictive performance of TMFA against gene essentiality and quantitative metabolomics data, under both aerobic and anaerobic, and optimal and suboptimal growth conditions. Based on these results, we propose that TMFA is a useful tool for validating phenotypes and generating hypotheses, and that additional types of data and constraints can improve predictions of metabolite concentrations.
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