ePrints

Bayesian inference for stochastic kinetic models using a diffusion approximation

Lookup NU author(s): Dr Andrew Golightly, Professor Darren Wilkinson

Downloads

• Accepted version [.pdf]

Abstract

This article is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular processes. The underlying discrete stochastic kinetic model is replaced by a diffusion approximation (or stochastic differential equation approach) where a white noise term models stochastic behavior and the model is identified using equispaced time course data. The estimation framework involves the introduction of m - 1 latent data points between every pair of observations. MCMC methods are then used to sample the posterior distribution of the latent process and the model parameters. The methodology is applied to the estimation of parameters in a prokaryotic autoregulatory gene network.

Publication metadata

Author(s): Golightly A, Wilkinson DJ

Publication type: Article

Publication status: Published

Journal: Biometrics

Year: 2005

Volume: 61

Issue: 3

Pages: 781-894

Print publication date: 01/09/2005

ISSN (print): 0006-341X

ISSN (electronic): 1541-0420

Publisher: Wiley-Blackwell Publishing Ltd.

URL: http://dx.doi.org/10.1111/j.1541-0420.2005.00345.x

DOI: 10.1111/j.1541-0420.2005.00345.x

PubMed id: 16135029

Altmetrics

Share