title: Model selection in systems and synthetic biology.
abstract: Developing mechanistic models has become an integral aspect of systems biology, as has the need to differentiate between alternative models. Parameterizing mathematical models has been widely perceived as a formidable challenge, which has spurred the development of statistical and optimisation routines for parameter inference. But now focus is increasingly shifting to problems that require us to choose from among a set of different models to determine which one offers the best description of a given biological system. We will here provide an overview of recent developments in the area of model selection. We will focus on approaches that are both practical as well as build on solid statistical principles and outline the conceptual foundations and the scope for application of such methods in systems biology.
authors.affiliation: Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Kirk
authors.forename: Thomas
authors.initials: T
authors.lastname: Thorne
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D001499:Bayes Theorem; D016013:Likelihood Functions; D008954:Models, Biological; D058615:Synthetic Biology; D049490:Systems Biology