ePrints

Bayesian inference for a discretely observed stochastic kinetic model

Lookup NU author(s): Professor Richard Boys, Professor Darren Wilkinson, Emeritus Professor Thomas Kirkwood

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

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. © 2007 Springer Science+Business Media, LLC.

Publication metadata

Author(s): Boys RJ, Wilkinson DJ, Kirkwood TBL

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2008

Volume: 18

Issue: 2

Pages: 125-135

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer

URL: http://dx.doi.org/10.1007/s11222-007-9043-x

DOI: 10.1007/s11222-007-9043-x

Altmetrics

Share