Abstract: We describe a workflow to translate a given metabolic network into a
kinetic model; the model summarises kinetic information collected from
different data sources. All reactions are modelled by convenience kinetics;
where detailed kinetic laws are known, they can also be incorporated.
Confidence intervals and correlations of the resulting model parameters are
obtained from Bayesian parameter estimation; they can be used to sample
parameter sets for Monte-Carlo simulations. The integration method ensures that
the resulting parameter distributions are thermodynamically feasible. Here we
summarise different previous works on this topic: we give an overview over the
convenience kinetics, thermodynamic criteria for parameter sets, Bayesian
parameter estimation, the collection of kinetic data, and different machine
learning techniques that can be used to obtain prior distributions for kinetic
parameters. All methods have been assembled into a workflow that facilitates
the integration of biochemical data and the modelling of metabolic networks
from scratch.
