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Bioinformatics Advance Access published online on October 29, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp619
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

Tina Toni 1,2,* and Michael P. H. Stumpf 1,2,*

1Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW7 2AZ, UK
2Institute of Mathematical Sciences, Imperial College London, 53 Prince's Gate, London, SW7 2PG, UK

*To whom correspondence should be addressed.Tina Toni ttoni{at}imperial.ac.uk Prof. Michael Stumpf, E-mail: m.stumpf{at}imperial.ac.uk


  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.

Contact: ttoni{at}imperial.ac.uk, m.stumpf{at}imperial.ac.uk

Supplementary Information: Tutorial on ABC rejection and ABC SMC for parameter estimation and model selection. Derivation of ABC SMC model selection algorithms. Supplementary figures and datasets.

Associate Editor: Dr. Jonathan Wren

Received on September 28, 2009; revised on October 23, 2009; accepted on October 25, 2009

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