Efficient Bayesian parameter inference for high-dimensional stochastic biological systems using an approximation

BEn Swallow (University of Glasgow)


Abstract:
Simulation of stochastic dynamical models, frequently studied in systems biology, has historically been too slow to enable parameter inference for all but the simplest systems. In order to calculate the probability distributions associated with these models, approximations to the likelihoods are frequently necessary, but these come with associated drawbacks in accuracy. Recent advances in both stochastic simulation algorithms and efficient Bayesian parameter estimation methodology enable much larger systems, such as those found in cell signalling and circadian clock applications, to be analysed and their parameters inferred. I will discuss how these recent advances can be used and present results on simulated data from a high-dimensional models of cell regulation.

BACK