Bayesian inference for a discretely observed stochastic kinetic model

Authors:
R. J. Boys;D. J. Wilkinson;T. B. Kirkwood
Affiliations:
School of Mathematics and Statistics, University of Newcastle upon Tyne, Newcastle upon Tyne, UK NE1 7RU;School of Mathematics and Statistics, University of Newcastle upon Tyne, Newcastle upon Tyne, UK NE1 7RU;School of Mathematics and Statistics, University of Newcastle upon Tyne, Newcastle upon Tyne, UK NE1 7RU
Venue:
Statistics and Computing
Year:
2008

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Abstract

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.