4.6 Article

Structural Thermokinetic Modelling

期刊

METABOLITES
卷 12, 期 5, 页码 -

出版社

MDPI
DOI: 10.3390/metabo12050434

关键词

metabolic model; structural kinetic modelling; dependence schema; reaction elasticity; model ensemble

资金

  1. German Research Foundation [Ll 1676/2-1, Ll 1676/2-2]

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The Structural Thermokinetic Modelling (STM) framework is a method for translating metabolic networks into dynamic models, utilizing a dependence schema and probability distributions for probabilistic predictions, emphasizing the importance of biological variables and the effects of consistent reversible rate laws.
To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.

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