ABC-SysBio--approximate Bayesian computation in Python with GPU support.
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
Liepe
Juliane
J
Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London, UK.
Barnes
Chris
C
Cule
Erika
E
Erguler
Kamil
K
Kirk
Paul
P
Toni
Tina
T
Stumpf
Michael P H
MP
D001499:Bayes Theorem; D012984:Software; D049490:Systems Biology / Q000379:methods*