Ben Swallow (University of St Andrews) – Bayesian inference for oscillatory systems using the pcLNA algorithm

Likelihood-based inference in stochastic non-linear dynamical systems, such as those found in chemical reaction networks and biological clock systems, is inherently complex and has largely been limited to small and unrealistically simple systems. Recent advances in analytically tractable approximations to the underlying conditional probability distributions enable long-term dynamics to be accurately modelled and making Bayesian inference much more feasible. We use preliminary analyses based on the Fisher Information Matrix of the model to guide the implementation of Bayesian inference. We show that this parameter sensitivity analysis can predict which parameters are practically identifiable. An asymptotically exact inference process based on Markov chain Monte Carlo methods is then implemented to accurately estimate the reaction rate parameters.