  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.
