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SYSBIONS: nested sampling for systems biology.

Rob Johnson1, Paul Kirk1, Michael P H Stumpf1

  • 1Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.

Bioinformatics (Oxford, England)
|November 16, 2014
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Summary
This summary is machine-generated.

We present SYSBIONS, a GPU-accelerated C implementation of nested sampling for systems biology. This tool aids in model selection and parameter inference by efficiently computing model evidence and posterior distributions.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Model selection is crucial in systems biology for choosing hypotheses that best explain data.
  • Bayesian inference uses Bayes factors to compare models, where evidence quantifies data support.
  • Nested sampling is a method for computing model evidence and posterior parameter distributions.

Purpose of the Study:

  • To introduce SYSBIONS, a novel C-based, GPU-accelerated implementation of nested sampling.
  • To provide a computational tool for model selection and parameter inference in biological systems.
  • To facilitate the application of Bayesian methods in systems biology research.

Main Methods:

  • Implementation of nested sampling algorithm in C with GPU acceleration.
  • Inclusion of standard nested sampling routines with optional extensions.
  • Development of methods for prior sampling under likelihood constraints.

Main Results:

  • A functional C-based, GPU-accelerated nested sampling software (SYSBIONS) is presented.
  • The software is designed specifically for applications in biological systems.
  • The implementation supports efficient computation of model evidence and posterior distributions.

Conclusions:

  • SYSBIONS offers an efficient computational approach for Bayesian model selection in systems biology.
  • The GPU acceleration significantly enhances the performance of nested sampling for complex biological models.
  • The software provides a valuable resource for researchers in computational biology and related fields.