SYSBIONS: nested sampling for systems biology.
BACKGROUND
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.


METHODS
The software SYSBIONS is available from http://www.theosysbio.bio.ic.ac.uk/resources/sysbions/


BACKGROUND
m.stumpf@imperial.ac.uk, robert.johnson11@imperial.ac.uk.
Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
Rob
R
Johnson
Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
Paul
P
Kirk
Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
Michael P H
MP
Stumpf
D000465:Algorithms; D001499:Bayes Theorem; D008954:Models, Biological; D011336:Probability; D012984:Software; D049490:Systems Biology / Q000379:methods*