Statistics and Computing

, Volume 18, Issue 2, pp 125–135

Bayesian inference for a discretely observed stochastic kinetic model

  • R. J. Boys
  • D. J. Wilkinson
  • T. B. L. Kirkwood
Article

DOI: 10.1007/s11222-007-9043-x

Cite this article as:
Boys, R.J., Wilkinson, D.J. & Kirkwood, T.B.L. Stat Comput (2008) 18: 125. doi:10.1007/s11222-007-9043-x

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.

Keywords

Biochemical networksBlock updatingLotka-Volterra modelMarkov jump processMCMC methodsParameter estimationReversible jumpSystems biology

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • R. J. Boys
    • 1
  • D. J. Wilkinson
    • 1
  • T. B. L. Kirkwood
    • 1
  1. 1.School of Mathematics and StatisticsUniversity of Newcastle upon TyneNewcastle upon TyneUK