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Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study
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Heron, Elizabeth A., Finkenstädt, Bärbel and Rand, D. A. (David A.) (2007) Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study. Bioinformatics, Vol.23 (No.19). pp. 2596-2603. doi:10.1093/bioinformatics/btm367 ISSN 1367-4803.
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Official URL: http://dx.doi.org/10.1093/bioinformatics/btm367
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 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.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre |
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| Journal or Publication Title: | Bioinformatics | ||||
| Publisher: | Oxford University Press | ||||
| ISSN: | 1367-4803 | ||||
| Official Date: | 2007 | ||||
| Dates: |
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| Volume: | Vol.23 | ||||
| Number: | No.19 | ||||
| Number of Pages: | 8 | ||||
| Page Range: | pp. 2596-2603 | ||||
| DOI: | 10.1093/bioinformatics/btm367 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Funder: | EPSRC, BBS, EU, EPSRC | ||||
| Grant number: | GR/S29256/01, BIOSIM Network Contract No. 005137, EP/C544587/1 |
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