Journal Article

BoolNet—an R package for generation, reconstruction and analysis of Boolean networks

Christoph Müssel, Martin Hopfensitz and Hans A. Kestler

in Bioinformatics

Volume 26, issue 10, pages 1378-1380
Published in print May 2010 | ISSN: 1367-4803
Published online April 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq124
BoolNet—an R package for generation, reconstruction and analysis of Boolean networks

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Motivation: As the study of information processing in living cells moves from individual pathways to complex regulatory networks, mathematical models and simulation become indispensable tools for analyzing the complex behavior of such networks and can provide deep insights into the functioning of cells. The dynamics of gene expression, for example, can be modeled with Boolean networks (BNs). These are mathematical models of low complexity, but have the advantage of being able to capture essential properties of gene-regulatory networks. However, current implementations of BNs only focus on different sub-aspects of this model and do not allow for a seamless integration into existing preprocessing pipelines.

Results: BoolNet efficiently integrates methods for synchronous, asynchronous and probabilistic BNs. This includes reconstructing networks from time series, generating random networks, robustness analysis via perturbation, Markov chain simulations, and identification and visualization of attractors.

Availability: The package BoolNet is freely available from the R project at http://cran.r-project.org/ or http://www.informatik.uni-ulm.de/ni/mitarbeiter/HKestler/boolnet/ under Artistic License 2.0.

Contact: hans.kestler@uni-ulm.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1348 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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