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Bioinformatics Advance Access published online on July 28, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm367
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study.

Elizabeth A. Heron a,b, Bärbel Finkenstädt b,* and David A. Rand a,c

aWarwick Systems Biology Centre, bDepartment of Statistics, cDepartment of Mathematics, University of Warwick, Coventry CV4 7AL, UK

*To whom correspondence should be addressed. Dr. Bärbel Finkenstädt, E-mail: B.F.Finkenstadt{at}Warwick.ac.uk


  Abstract

Motivation: In this study we address the problem of estimating the parameters of regulatory networks and provide the first application of Markov chain Monte Carlo (MCMC) methods to experimental data. As a case study we consider a stochastic model of the Hes1 system expressed in terms of stochastic differential equations (SDEs) to which rigorous likelihood methods of inference can be applied. When fitting continuous-time stochastic models to discretely observed time series the lengths of the sampling intervals are important, and much of our study addresses the problem when the data are sparse.

Results: We estimate the parameters of an autoregulatory network providing results both for simulated and real experimental data from the Hes1 system. We develop an estimation algorithm using Markov chain Monte Carlo techniques which are flexible enough to allow for the imputation of latent data on a finer time scale and the presence of prior information about parameters which may be informed from other experiments as well as additional measurement error.

Availability:Supplementary information is submitted with the paper.

Contact: B.F.Finkenstadt{at}Warwick.ac.uk

Associate Editor: Dr. Limsoon Wong

Received on June 2, 2007; revised on July 2, 2007; accepted on July 7, 2007

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