ABC-SysBio: A tool for parameter inference and model selection
Developed by Chris Barnes, Juliane Liepe, Erika Cule, Sarah Filippi, Delphine Rolando, Siobhan McMahon, Beata Lisowska, Paul Kirk, Kamil Erguler, Tina Toni, Michael Stumpf
Download from Sourceforge
- 21/12/2011: Version 2.05 released.
- Minimum number of particles for calculating adaptive kernels set to 100
- Bug fixes in scatter plots. Multiple histogram files now produced
- Report epsilon used in the rate file
- Python, C, CUDA consistent with regards to simulations. All simulations start at t=0
If you would like to be notified of changes to cuda-sim or ABC-SysBio then please email email@example.com to be added to the mailing list.
The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. One established problem in the modeling of biological processes is the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to estimate parameters given biological data and a proposed model.
ABC-SysBio implements likelihood free parameter inference and model selection in dynamical systems. It is designed to work with both stochastic and deterministic models written in Systems Biology Markup Language (SBML). ABC-SysBio is a Python package that combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection.
“Bayesian design of synthetic biological systems.”
C.P. Barnes, D. Silk, X. Sheng, M.P.H. Stumpf
Proc Natl Acad Sci U S A. 2011 Sep 13;108(37):15190-5.
“GPU accelerated biochemical network simulation.”
Y. Zhou, J. Liepe, X. Sheng, M.P.H. Stumpf, C. Barnes
Bioinformatics. 2011 Mar 15;27(6):874-6.
J. Liepe, C. Barnes, E. Cule, K. Erguler, P. Kirk, T. Toni, M. P.H. Stumpf
“ABC-SysBio – Approximate Bayesian Computation in Python with GPU support”
Bioinformatics. 2010 Jul 15;26(14):1797-9.
T. Toni and M.P.H. Stumpf
“Simulation-based model selection for dynamical systems in systems and population biology”
Bioinformatics. 2010 Jan 1;26(1):104-10.
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, M.P.H. Stumpf
“Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems”
J R Soc Interface. 2009 Feb 6;6(31):187-202.
J. Pritchard, M.T. Seielstad, A. Perez-Lezaun, M.W. Feldman
“Population growth of human Y chromosomes: a study of Y chromosome microsatellites”
Mol Biol Evol. 1999 Dec;16(12):1791-8.
The packages in the project are still under development. If you want help, think you have found a bug or have any other enquiries about a package it should be directed towards one of the authors. There are no restrictions on the use of this code except that the developers take no liability for any problems that may arise from its use. The software is provided as is, without warranty of any kind.