Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

TLDR: Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but it is found here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem.

Abstract: Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network.