Structural Thermokinetic Modelling

Abstract

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.

Control Analysis of Cooperativity and Complementarity in Metabolic Regulations: The Case of NADPH Homeostasis

“…This thermodynamic requirement coincides with recent experimental results that emphasize a near-equilibrium activity of PGI enzymes in various contexts [47], including oxidative bursts [48]. This latter result motivates us to refine our framework to integrate thermodynamic constraints as already implemented in some previous studies [5,7,9]. We consider the conservation relation s n + s nh = 1, defining a concentration unit.…”

“…The synergistic effect described here rather characterizes a cooperative scheme where a control coefficient associated with a given metabolic functionality is enhanced congruently by first-order effects of regulations and second-order effects where one regulation triggers concentration changes in effectors of other regulations. Such an effect falls within higher-order approaches of metabolic control analysis where, for instance, second-order control coefficients describe synergies between enzyme pairs [9,38]. The careful analysis of the cooperation between NADPH-dependent inhibition of G6PD and 6PG-dependent inhibition of PGI depicts the set of requirements for efficient synergy.…”

“…Metabolic control analysis (MCA) provides a rigorous theoretical framework to study the sensitivity of metabolic networks with respect to biochemical and environmental variations [1,2]. MCA has been further developed and expanded in several directions related to regulation, thermodynamics, or statistical analysis [3][4][5][6][7][8][9]. These developments contribute to more comprehensive analysis of control properties of metabolic networks with the challenging goal to decipher the logic of complex regulation pattern, such as those involving direct metabolite-enzyme interactions and coupling distal parts of a network [10][11][12].…”

Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

“…coli. With this in mind, we use the structural thermokinetic modeling framework to generate kinetic models that are consistent with stoichiometric and thermodynamic constraints, as well as a biomass optimization objective often used in constraint-based models. − We start by adding a pathway to the stoichiometry of the core model and add thermodynamic information from a provided database to impose a flux directionality profile. ,, Additional constraints on the steady-state metabolite concentrations of the pathway are imposed (see the Supporting Information). Based on these constraints, the biomass objective that was provided is optimized, and a sample of fluxes, concentrations, and equilibrium constants is taken from the feasible solution space.…”