ABSTRACT
Systems biology aims to capture the fundamental molecular and cellular processes underlying living systems. Iteration between experiment and theoretical analysis and prediction has become a hallmark of systems biology studies. Most computational analyses in systems biology can be divided into two classes: those aiming to describe and analyse the structure of biological networks; and those that model explicitly the temporal behaviour of a biological system, such as a signalling or gene regulation network. This chapter illustrates an example of approximate Bayesian computation (ABC)-sequential Monte Carlo (SMC) for model selection by returning to the gene regulation model and the generated data describing the time course of mRNA. It illustrates how ABC aids the analysis of cell migration data using the example of immune cell migration of macrophages in response to acute injury. Parameter estimation and especially model selection, which is possible for dynamical systems as no summary statistics are used, are central to many problems in systems biology.
