{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T18:20:24Z","timestamp":1774376424152,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2016,10,1]],"date-time":"2016-10-01T00:00:00Z","timestamp":1475280000000},"content-version":"vor","delay-in-days":2529,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Contact: \u00a0ttoni@imperial.ac.uk; m.stumpf@imperial.ac.uk<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp619","type":"journal-article","created":{"date-parts":[[2009,10,31]],"date-time":"2009-10-31T00:13:10Z","timestamp":1256947990000},"page":"104-110","source":"Crossref","is-referenced-by-count":212,"title":["Simulation-based model selection for dynamical systems in systems and population biology"],"prefix":"10.1093","volume":"26","author":[{"given":"Tina","family":"Toni","sequence":"first","affiliation":[{"name":"1 Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW7 2AZ and 2 Institute of Mathematical Sciences, Imperial College London, 53 Prince's Gate, London SW7 2PG, UK"},{"name":"1 Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW7 2AZ and 2 Institute of Mathematical Sciences, Imperial College London, 53 Prince's Gate, London SW7 2PG, UK"}]},{"given":"Michael P. H.","family":"Stumpf","sequence":"additional","affiliation":[{"name":"1 Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW7 2AZ and 2 Institute of Mathematical Sciences, Imperial College London, 53 Prince's Gate, London SW7 2PG, UK"},{"name":"1 Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW7 2AZ and 2 Institute of Mathematical Sciences, Imperial College London, 53 Prince's Gate, London SW7 2PG, UK"}]}],"member":"286","published-online":{"date-parts":[[2009,10,29]]},"reference":[{"key":"2023012507533809600_B1","doi-asserted-by":"crossref","first-page":"961","DOI":"10.2307\/2532652","article-title":"A generalized stochastic model for the analysis of infectious disease final size data","volume":"47","author":"Addy","year":"1991","journal-title":"Biometrics"},{"key":"2023012507533809600_B2","doi-asserted-by":"crossref","first-page":"2605","DOI":"10.1242\/dev.02411","article-title":"JAK\/STAT signalling in drosophila: insights into conserved regulatory and cellular functions","volume":"133","author":"Arbouzova","year":"2006","journal-title":"Development"},{"key":"2023012507533809600_B3","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1093\/genetics\/162.4.2025","article-title":"Approximate Bayesian computation in population genetics","volume":"162","author":"Beaumont","year":"2002","journal-title":"Genetics"},{"key":"2023012507533809600_B4","article-title":"Non-linear regression models for approximate Bayesian computation","author":"Blum","year":"2009","journal-title":"Stat. 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