title: Bayesian inference for stochastic kinetic models using a diffusion approximation.
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.
authors.affiliation: School of Mathematics and Statistics, University of Newcastle, Newcastle Upon Tyne, NE1 7RU, UK. a.golightly@ncl.ac.uk
authors.forename: A
authors.initials: A
authors.lastname: Golightly
authors.forename: D J
authors.initials: DJ
authors.lastname: Wilkinson
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D004058:Diffusion; D007700:Kinetics; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D013269:Stochastic Processes