title: Stochastic simulation in systems biology.
abstract: Natural systems are, almost by definition, heterogeneous: this can be either a boon or an obstacle to be overcome, depending on the situation. Traditionally, when constructing mathematical models of these systems, heterogeneity has typically been ignored, despite its critical role. However, in recent years, stochastic computational methods have become commonplace in science. They are able to appropriately account for heterogeneity; indeed, they are based around the premise that systems inherently contain at least one source of heterogeneity (namely, intrinsic heterogeneity). In this mini-review, we give a brief introduction to theoretical modelling and simulation in systems biology and discuss the three different sources of heterogeneity in natural systems. Our main topic is an overview of stochastic simulation methods in systems biology. There are many different types of stochastic methods. We focus on one group that has become especially popular in systems biology, biochemistry, chemistry and physics. These discrete-state stochastic methods do not follow individuals over time; rather they track only total populations. They also assume that the volume of interest is spatially homogeneous. We give an overview of these methods, with a discussion of the advantages and disadvantages of each, and suggest when each is more appropriate to use. We also include references to software implementations of them, so that beginners can quickly start using stochastic methods for practical problems of interest.
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, United Kingdom.
authors.forename: Tamás
authors.initials: T
authors.lastname: Székely
authors.affiliation: Department of Computer Science, University of Oxford, Oxford, United Kingdom ; Department of Mathematics, Queensland University of Technology, Brisbane, Queensland, Australia.
authors.forename: Kevin
authors.initials: K
authors.lastname: Burrage
keywords: Discrete-state stochastic methods; Heterogeneity; Stochastic simulation