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Bioinformatics Advance Access published online on March 20, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp162
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

GNU MCSim: Bayesian statistical inference for SBML-coded sys-tems biology models

Frédéric Y. Bois 1,*

1Direction des Risques Chroniques, INERIS, Parc ALATA, BP2, F-60550, Verneuil en Halatte, France.

*To whom correspondence should be addressed. Dr. Frederic Bois, E-mail: frederic.bois{at}ineris.fr


  Abstract

Summary: Statistical inference about the parameter values of complex models, such as the ones routinely developed in systems biology, is efficiently performed through Bayesian numerical techniques. In that framework, prior information and multiple levels of uncertainty can be seamlessly integrated. GNU MCSim was precisely developed to achieve those aims, in a general nonlinear differential context. Starting with version 5.3.0, GNU MCSim reads in and simulates SBML models. Markov chain Monte Carlo simulations can be used to generate samples from the joint posterior distribution of the model parameters, given a dataset and prior distributions. Hierarchical statistical models can be used. Optimal design of experiments can also be investigated. Supplementary material is available online at http://www.gnu.org/software/mcsim.

Availability and Implementation: The GNU GPL source is available at http://savannah.gnu.org/projects/mcsim. A distribution package is at http://www.gnu.org/software/mcsim. GNU MCSim is written in standard C and runs on any platform supporting a C compiler.

Contact: frederic.bois{at}ineris.fr

Associate Editor: Dr. Jonathan Wren

Received on January 17, 2009; revised on February 26, 2009; accepted on March 16, 2009

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