Structural thermokinetic modelling

TL;DR

This paper introduces Structural Thermokinetic Modelling (STM), a method that incorporates thermodynamics into metabolic network models to enable probabilistic predictions and analyze metabolic dynamics and fluctuations.

Contribution

The paper proposes a novel STM approach that accounts for reversible reactions and thermodynamics, improving upon existing models by integrating physical constraints and probabilistic analysis.

Findings

STM provides consistent reversible rate laws for metabolic networks.

Thermodynamics enhances predictions of flux control and enzyme synergies.

Model comparisons reveal effects of network structure and regulation.

Abstract

Translating metabolic networks into dynamic models is difficult if kinetic constants are unknown. Structural Kinetic Modelling (SKM) replaces reaction elasticities by independent random numbers. Here I propose a variant that accounts for reversible reactions and thermodynamics: in Structural Thermokinetic Modelling (STM), correlated elasticities are computed from enzyme saturation values and thermodynamic forces, which are physically independent. STM relies on a dependency schema in which basic variables can be sampled, fitted to data, or optimised, while all other variables are computed from them. Probability distributions in the dependency schema define a model ensemble, which leads to probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies and covariances of biological variables. By choosing or sampling the basic variables, we can convert metabolic networks 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 as well as enzyme synergies, metabolite correlations, and metabolic fluctuations arising from chemical noise. By comparing model variants with different network structures, fluxes, thermodynamic forces, regulation, or types of rate laws, we can quantify the effects of these model features. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Thermodynamics can be used to obtain more precise predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and of the emergence and propagation of metabolic noise.