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Abstract:
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This talk will give an overview of one of the key problems in the new science of systems biology: inference for the rate parameters underlying complex stochastic kinetic biochemical network models, using partial, discrete, and noisy time-course measurements of the system state. The basic problem will be introduced, highlighting the importance of stochastic modeling for effective estimation. Then, a range of approaches to Bayesian inference will be reviewed and compared. Some approaches recognize the discrete nature of the underlying molecular dynamics. Other techniques use a diffusion approximation to the true underlying process in order to give a nonlinear, multivariate stochastic differential equation (SDE) representation. Inference for this multivariate SDE can be carried out using MCMC techniques similar to those adopted for similar models in econometrics.
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