Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study.
BACKGROUND
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 MCMC 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.
Heron
Elizabeth A
EA
Warwick Systems Biology Centre, University of Warwick, Coventry CV4 7AL, UK.
Finkenstädt
Bärbel
B
Rand
David A
DA
D001185:Artificial Intelligence; D051792:Basic Helix-Loop-Helix Proteins / Q000235:genetics*; D001499:Bayes Theorem; D001683:Biological Clocks / Q000235:genetics*; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000235:genetics*; D018398:Homeodomain Proteins / Q000235:genetics*; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D000072056:Transcription Factor HES-1; D014157:Transcription Factors / Q000235:genetics*; D014158:Transcription, Genetic / Q000235:genetics*
D051792:Basic Helix-Loop-Helix Proteins; D018398:Homeodomain Proteins; D000072056:Transcription Factor HES-1; D014157:Transcription Factors; C078493:HES1 protein, human