SYSBIONS: nested sampling for systems biology

Motivation: Model selection is a fundamental part of the scientific process in systems biology. Given a set of competing hypotheses, we routinely wish to choose the one that best explains the observed data. In the Bayesian framework, models are compared via Bayes factors (the ratio of evidences), wh...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) Vol. 31; no. 4; pp. 604 - 605
Main Authors: Johnson, Rob, Kirk, Paul, Stumpf, Michael P. H.
Format: Journal Article
Language:English
Published: England Oxford University Press 15.02.2015
Subjects:
Algorithms
Applications Notes
Bayes Theorem
Models, Biological
Probability
Software
Systems Biology - methods
ISSN:1367-4803, 1367-4811, 1367-4811
Online Access:Get full text
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Summary:Motivation: Model selection is a fundamental part of the scientific process in systems biology. Given a set of competing hypotheses, we routinely wish to choose the one that best explains the observed data. In the Bayesian framework, models are compared via Bayes factors (the ratio of evidences), where a model’s evidence is the support given to the model by the data. A parallel interest is inferring the distribution of the parameters that define a model. Nested sampling is a method for the computation of a model’s evidence and the generation of samples from the posterior parameter distribution. Results: We present a C-based, GPU-accelerated implementation of nested sampling that is designed for biological applications. The algorithm follows a standard routine with optional extensions and additional features. We provide a number of methods for sampling from the prior subject to a likelihood constraint. Availability and implementation: The software SYSBIONS is available from http://www.theosysbio.bio.ic.ac.uk/resources/sysbions/ Contact:  m.stumpf@imperial.ac.uk, robert.johnson11@imperial.ac.uk
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Associate Editor: Janet Kelso
The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btu675