SYSBIONS

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.

SYSBIONS: nested sampling for systems biology.

The software tool 'SYSBIONS' is designed to perform model selection in systems biology, comparing competing hypotheses via Bayes factors and inferring the distribution of the parameters that define a model. The tool presents a C-based, GPU-accelerated implementation of nested sampling, which is a method for computing a model's evidence and generating samples from the posterior parameter distribution. The software offers a number of methods for sampling from the prior subject to a likelihood constraint.

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