Dr Ben Swallow, University of Warwick: Efficient Bayesian parameter inference for high-dimensional stochastic biological systems using the phase-corrected linear noise approximation.
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 required for likelihood-based inference, approximations are frequently necessary, but these come with associated drawbacks in accuracy. Recent advances in both analytically tractable stochastic simulation algorithms and efficient Bayesian parameter estimation methodologies enable much larger systems, such as those found in cell signalling applications, to be analysed and their parameters accurately inferred. I will discuss how these recent advances can be implemented and present results on simulated data from a high-dimensional model of NF-kappaB regulation.