ArticleMolecular systems biology2006
Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data.
Article in Molecular systems biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers.
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Who cites it
121 citing papers in PubMed.
- Thermo-flux: generation and analysis of thermodynamic-stoichiometric metabolic network models.Molecular systems biology · 2026Article
- Article
- XomicsToModel: omics data integration and generation of thermodynamically consistent metabolic models.Nature protocols · 2026Article
- The return of metabolism: biochemistry and physiology of glycolysis.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Quantitative principles of microbial metabolism shared across scales.Nature microbiology · 2024Review
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- Metabolite interactions in the bacterial Calvin cycle and implications for flux regulation.Communications biology · 2023Article
- The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility.PLoS computational biology · 2022Article
- Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges.Biomolecules · 2022Review
- Structural Thermokinetic Modelling.Metabolites · 2022Article
- Metabolic control analysis of L-tryptophan producing Escherichia coli applying targeted perturbation with shikimate.Bioprocess and biosystems engineering · 2021Article
- Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.Nature methods · 2021Review
- The quantitative metabolome is shaped by abiotic constraints.Nature communications · 2021Article
- New thermodynamic activity-based approach allows predicting the feasibility of glycolysis.Scientific reports · 2021Article
- MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics.PLoS computational biology · 2021Article
- Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions.PLoS computational biology · 2021Article
- The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale.Microorganisms · 2020Review
- Towards scaling elementary flux mode computation.Briefings in bioinformatics · 2020Article
- A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks.PLoS computational biology · 2020Article
- Thermodynamic Limits and Optimality of Microbial Growth.Entropy (Basel, Switzerland) · 2020Review
61 more citing papers are in PubMed but not listed here.
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No grant is acknowledged in the PubMed record.
Abstract
As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.
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