Journal Article

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

Frédéric Y. Bois

in Bioinformatics

Volume 25, issue 11, pages 1453-1454
Published in print June 2009 | ISSN: 1367-4803
Published online March 2009 | e-ISSN: 1460-2059 | DOI:
GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology


Show Summary Details


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 A distribution package is at GNU MCSim is written in standard C and runs on any platform supporting a C compiler. Supplementary Material is available online at


Journal Article.  1351 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.