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Bioinformatics Advance Access originally published online on March 20, 2009
Bioinformatics 2009 25(11):1453-1454; 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 systems biology models

Frédéric Y. Bois

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


  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 non-linear differential context. Starting with version 5.3.0, GNU MCSim reads in and simulates Systems Biology Markup Language 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.

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. Supplementary Material is available online at http://www.gnu.org/software/mcsim.

Contact: frederic.bois{at}ineris.fr

Associate Editor: Jonathan Wren

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

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