Fixed vignette compilation errors on Solaris 10.
getAttractors() now allows for plotting all attractors inside one plot. Fixed minor bugs.
Fixed compilation errors on Windows.
simulateSymbolicModel() now support the identification of attractors in large networks based on a formulation as a satisfiability (SAT) problem.
plotSequence() now plot the genes in the order of the network instead of the reverse order by default. This behaviour can be controlled using a new parameter
reverse which is also available in
Bugfix regarding negated temporal predicates in
Fixed undefined behaviour in
Fixed memory misalignment in
loadSBML() now accepts nodes that are constant, but have no initial value.
Support of temporal networks, and inclusion of a new simulator
simulateSymbolicModel() to simulate these networks. Related functions include
symbolicToTruthTable() to convert networks between the symbolic representation used by the new simulator and the truth table representation employed by the standard simulator.
perturbTrajectories() to assess the robustness of networks to noise in the states.
loadNetwork() can now load networks in a symbolic representation and with temporal extensions.
loadBioTapestry() can load symbolic networks without temporal extensions.
Most functions of the package have been adapted to work either with the symbolic representation or with the truth table representation of networks.
sequenceToLaTeX() now visualize the attractor.
reconstructNetwork() now supports the specification of prior knowledge in form of required or excluded dependencies. Furthermore, it can now reconstruct networks from perturbation time series. By default, the function now returns incomplete functions with "don't care" values" instead of enumerating all possible functions.
generateTimeSeries() can now generate artificial perturbation data with simulated knock-out or overexpression of genes.
generateRandomNKNetwork() can now be supplied with a user-defined generation function for the transition functions. Generation functions
generateNestedCanalyzing() for canalyzing functions and nested canalyzing functions are included in the package.
testNetworkProperties() supports several new tests that perturb the network states instead of the networks themselves. These are available in the test functions
Fixed issues preventing the use of BoolNet on Big Endian systems.
Eliminated some bad style code.
Fixed some valgrind notes.
Minor bugfixes in
Fixed undefined behaviour warnings for GCC 4.9.
Fixed compatibility issues with R 3.0 alpha.
Support for SBML:
toSBML() import from and export to SBML documents with the
sbml-qual extension package.
saveNetwork() stores networks in the BoolNet file format.
The DNF generator employed by
simplifyNetwork() (as well as by the new functions
toSBML()) now supports minimizing the canonical DNFs.
BoolNet now supports the modified interface of igraph 0.6 in all plotting functions, but is still compatible with older versions of igraph.
loadNetwork() supports comment lines in the network files.
generateTimeSeries() generates random state sequences from an existing network.
sequenceToLaTeX() plot and export sequences of states similar to
getAttractorSequence() extracts the states of a single synchronous attractor from an attractor information structure as a data frame.
generateState() provides a simple way to specify network states using partial assignments.
getPathToAttractor() has an additional parameter
includeAttractorStates specifying which attractor states should be included in the path. The default behaviour has been changed to include all attractor states.
generateRandomNKNetwork() now supports the generation of networks using specific classes of functions. For this purpose, it has two new parameters
loadNetwork() no longer changes gene names to lower case. If this behaviour is desired, it can be reactivated using the new
stateTransition() now names the state vector using the gene names.
plotAttractors() has a new parameter
drawLegend to disable the legend.
randomChainLength parameter of
getAttractors() now defaults to 10000.
markovSimulation() can now be interrupted by the user.