ArticleMetabolites2021
Model Balancing: A Search for In-Vivo Kinetic Constants and Consistent Metabolic States.
Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A historical journey of metabolite-protein interaction discovery: from data harmonization to AI-driven prediction.Briefings in bioinformatics · 2026Review
- Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.bioRxiv : the preprint server for biology · 2026Article
- Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks.PLoS computational biology · 2026Article
- hopsy - a methods marketplace for convex polytope sampling in Python.Bioinformatics (Oxford, England) · 2024Article
- Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism.ACS synthetic biology · 2024Review
- Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models.Bioinformatics (Oxford, England) · 2023Article
- Structural Thermokinetic Modelling.Metabolites · 2022Article
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2 authors.
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Abstract
Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models are large. Here we show how consistent kinetic constants, metabolite concentrations, and enzyme concentrations can be determined from data if metabolic fluxes are known. The estimation method, called model balancing, can handle models with a wide range of rate laws and accounts for thermodynamic constraints between fluxes, kinetic constants, and metabolite concentrations. It can be used to estimate in-vivo kinetic constants, to complete and adjust available data, and to construct plausible metabolic states with predefined flux distributions. By omitting one term from the log posterior-a term for penalising low enzyme concentrations-we obtain a convex optimality problem with a unique local optimum. As a demonstrative case, we balance a model of
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