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Structural Thermokinetic Modelling.

Wolfram Liebermeister1

  • 1Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France.

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|May 28, 2022
PubMed
Summary
This summary is machine-generated.

Structural Thermokinetic Modelling (STM) introduces thermodynamics and reversible reactions for dynamic metabolic models. This framework enables probabilistic predictions and reveals how thermodynamics shapes metabolic control and fluctuations.

Keywords:
dependence schemametabolic modelmodel ensemblereaction elasticitystructural kinetic modelling

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Biophysics

Background:

  • Traditional Structural Kinetic Modelling (SKM) uses random elasticities, neglecting thermodynamics and reversibility.
  • Accurate dynamic modeling of metabolic networks is crucial for understanding cellular functions.
  • Existing models often lack the ability to incorporate thermodynamic constraints and reversible reactions.

Purpose of the Study:

  • To introduce Structural Thermokinetic Modelling (STM), a novel framework for dynamic metabolic network modeling.
  • To integrate thermodynamics and reversible reactions into kinetic models.
  • To enable probabilistic predictions and analyze the impact of thermodynamic forces on metabolic behavior.

Main Methods:

  • Developed STM, a variant of SKM that incorporates reversible reactions and thermodynamic forces.
  • Utilized a dependence schema for sampling and computing model variables, enabling probabilistic predictions.
  • Analyzed metabolic control, fluctuations, and enzyme synergies by varying network parameters and thermodynamic forces.

Main Results:

  • STM allows for the computation of correlated elasticities from enzyme saturation and thermodynamic forces.
  • The framework generates model ensembles for probabilistic predictions, even with limited data.
  • Thermodynamics significantly shapes metabolic control, enzyme synergies, and the propagation of metabolic noise.

Conclusions:

  • STM provides a robust method for converting metabolic networks into kinetic models with consistent reversible rate laws.
  • Incorporating thermodynamics improves predictions of flux control, enzyme synergies, and metabolic fluctuations.
  • STM highlights the importance of variability, dependencies, and covariances in biological systems.