title: ABC-SysBio--approximate Bayesian computation in Python with GPU support.
abstract: BACKGROUND
  The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions.
  
  
  RESULTS
  Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio.
  
  
  BACKGROUND
  http://abc-sysbio.sourceforge.net
authors.affiliation: Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London, UK.
authors.forename: Juliane
authors.initials: J
authors.lastname: Liepe
authors.forename: Chris
authors.initials: C
authors.lastname: Barnes
authors.forename: Erika
authors.initials: E
authors.lastname: Cule
authors.forename: Kamil
authors.initials: K
authors.lastname: Erguler
authors.forename: Paul
authors.initials: P
authors.lastname: Kirk
authors.forename: Tina
authors.initials: T
authors.lastname: Toni
authors.forename: Michael P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D001499:Bayes Theorem; D012984:Software; D049490:Systems Biology / Q000379:methods*