Parameter estimation for stocastic biological models

Computational modelling is an essential component to achieving a systems biology level of understanding of complex biological processes. In recent years, it has become well understood that stochasticity within finite populations can produce dynamics profoundly different from the predictions of corresponding deterministic models. Unfortunately, analytic solutions to stochastic time-evolution equations are often intractable, while numerical solutions are often computationally infeasible. This makes parameter estimation, the process of finding model parameters that best satisfy experimental data, a difficult problem and one that is often a bottleneck in the modelling process. In this talk I will outline work that has been done toward accelerating direct simulation approaches to the parameter estimation problem, inspired by the cross-entropy approach - a method originating from the field of rare-event simulation.