Simulation-based model selection for dynamical systems in systems and population biology

Item Type Journal Article
Author Tina Toni
Author Michael P H Stumpf
URL http://www.ncbi.nlm.nih.gov/pubmed/19880371
Volume 26
Issue 1
Pages 104-110
Publication Bioinformatics (Oxford, England)
ISSN 1367-4811
Date Jan 1, 2010
Extra PMID: 19880371
Journal Abbr Bioinformatics
DOI 10.1093/bioinformatics/btp619
Accessed 2009-12-30 14:23:21
Library Catalog NCBI PubMed
Abstract MOTIVATION: Computer simulations have become an important tool across the biomedical sciences and beyond. For many important problems several different models or hypotheses exist and choosing which one best describes reality or observed data is not straightforward. We therefore require suitable statistical tools that allow us to choose rationally between different mechanistic models of, e.g. signal transduction or gene regulation networks. This is particularly challenging in systems biology where only a small number of molecular species can be assayed at any given time and all measurements are subject to measurement uncertainty. RESULTS: Here, we develop such a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling. We show that our approach can be applied across a wide range of biological scenarios, and we illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway. Bayesian model selection strikes a balance between the complexity of the simulation models and their ability to describe observed data. The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.
Title Simulation-based model selection for dynamical systems in systems and population biology
Date Added 2009-12-30 09:23
Date Modified 2009-12-30 09:23