Bayesian sequential inference for stochastic kinetic biochemical network models.

Full Abstract

As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we explore the Bayesian estimation of stochastic kinetic rate constants governing dynamic models of intracellular processes. The underlying model is replaced by a diffusion approximation where a noise term represents intrinsic stochastic behavior and the model is identified using discrete-time (and often incomplete) data that is subject to measurement error. Sequential MCMC methods are then used to sample the model parameters on-line in several data-poor contexts. The methodology is illustrated by applying it to the estimation of parameters in a simple prokaryotic auto-regulatory gene network.

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Author information

Author/s: Golightly, Andrew (A); Wilkinson, Darren J (DJ);

Affiliation: School of Mathematics and Statistics, University of Newcastle upon Tyne, NE1 7RU, UK. a.golightly(-atsign-)ncl.ac.uk

Journal and publication information

Publication Type: Journal Article

Journal: Journal of computational biology : a journal of computational molecular cell biology (J Comput Biol), published in United States. (Language: eng)

Reference: 2006-Apr; vol 13 (issue 3) : pp 838-51

Dates: Created 2006/05/18; Completed 2006/07/07;

PMID: 16706729, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Kinetics
Metabolism

Models, Biological
Stochastic Processes

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