Statistical Modeling of Biochemical Pathways

We examine the usefulness of Bayesian statistical methods for the modeling of biochemical reactions. With simulated data, it is shown that these methods can effectively fit mechanistic models of sequences of enzymatic reactions to experimental data. These methods have the advantages of being relatively easy to use and producing probability distributions for the model parameters rather than point estimates, allowing more informative inferences to be drawn. Three Markov chain Monte Carlo algorithms are used to fit models to data from a sequence of 4 enzymatic reactions. The algorithms are evaluated with respect to the goodness-of-fit of the fitted models and the time to completion. It is shown that the algorithms produce essentially the same parameter distributions but the time to completion varies.