title: BoolNet--an R package for generation, reconstruction and analysis of Boolean networks.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  hans.kestler@uni-ulm.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Neural Information Processing, University of Ulm and Department of Internal Medicine I, University Hospital Ulm, 89081 Ulm, Germany.
authors.forename: Christoph
authors.initials: C
authors.lastname: Müssel
authors.forename: Martin
authors.initials: M
authors.lastname: Hopfensitz
authors.forename: Hans A
authors.initials: HA
authors.lastname: Kestler
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D012984:Software