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.