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Zbl 1079.62110
Golightly, A.; Wilkinson, D.J.
Bayesian inference for stochastic kinetic models using a diffusion approximation. (English)
[J] Biometrics 61, No. 3, 781-788 (2005). ISSN 0006-341X; ISSN 1541-0420/e

Summary: 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.

MSC 2000:: *62P10 Appl. of statistics to biology
92C37 Cell biology
62F15 Bayesian inference
60J70 Appl. of diffusion theory

Keywords: Markov chain Monte Carlo; missing data; nonlinear diffusion; stochastic differential equations; nonlinear Fokker-Planck equation

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Zentralblatt MATH Berlin [Germany]