BoolNet (version 1.44)

BoolNet-package: Analyze Boolean networks

Description

Tools for reconstruction, analysis and visualization of synchronous, asynchronous, and probabilistic Boolean networks, in particular for the identification of attractors in gene-regulatory networks

Details

ll{ Package: BoolNet Type: Package Version: 1.44 Date: 2011-04-11 License: Artistic-2.0 LazyLoad: yes } This package provides useful methods for the construction and generation of Boolean networks and for their analysis. In particular, it is designed for the analysis of gene-regulatory networks. The software supports three types of networks: [object Object],[object Object],[object Object]

Networks can be assembled in several ways using BoolNet: The reconstructNetwork function infers Boolean networks from time series of measurements using several popular reconstruction algorithms. binarizeTimeSeries provides a means of binarizing real-valued time series for these reconstruction algorithms. Boolean networks (synchronous, asynchronous, and probabilistic networks) can also be expressed in a description language and loaded from files using loadNetwork. Furthermore, networks can be imported from BioTapestry using loadBioTapestry. Via generateRandomNKNetwork and perturbNetwork, the package supports various methods of generating random networks and perturbing existing networks for robustness analysis.

The getAttractors function identifies attractor cycles in a synchronous or asynchronous Boolean network. Attractors can be identified by exhaustive search or heuristic methods.

The markovSimulation function identifies relevant states in probabilistic Boolean networks by performing a Markov chain simulation.

The package also provides methods to visualize state transitions and basins of attraction latex{ } (plotPBNTransitions, plotStateGraph), to plot the wiring of a network latex{ }(plotNetworkWiring), to plot the attractor state table (plotAttractors), and to export them to LaTeX (attractorsToLaTeX) and Pajek (toPajek).

Transition tables of the network can be analyzed using getTransitionTable.

References

S. A. Kauffman (1969), Metabolic stability and epigenesis in randomly constructed nets. J. Theor. Biol. 22:437--467.

S. A. Kauffman (1993), The Origins of Order. Oxford University Press.

Further references are listed in the corresponding help sections.

Examples

library(BoolNet)

##################################
# Example 1: identify attractors #
################################## 

# load example data
data(cellcycle)

# get all synchronous attractors by exhaustive search
attractors <- getAttractors(cellcycle)

# plot attractors side by side
par(mfrow=c(2,length(attractors$attractors)))
plotAttractors(attractors)

# identifies asynchronous attractors
attractors <- getAttractors(cellcycle, 
                            type="asynchronous", startStates=100)
plotAttractors(attractors, mode="graph")

####################################
# Example 2: reconstruct a network #
####################################

# load example data
data(yeastTimeSeries)

# perform binarization with k-means
bin <- binarizeTimeSeries(yeastTimeSeries)

# reconstruct networks from transition table
net <- reconstructNetwork(bin$binarizedMeasurements, 
                          method="bestfit", maxK=3)

# analyze the network using a Markov chain simulation
print(markovSimulation(net, returnTable=FALSE))