  Background. Dynamical models of gene regulatory networks (GRNs) are highly
effective in describing complex biological phenomena and processes, such as
cell differentiation and cancer development. Yet, the topological and
functional characterization of real GRNs is often still partial and an
exhaustive picture of their functioning is missing.
  Motivation. We here introduce CABeRNET, a Cytoscape app for the generation,
simulation and analysis of Boolean models of GRNs, specifically focused on
their augmentation when a only partial topological and functional
characterization of the network is available. By generating large ensembles of
networks in which user-defined entities and relations are added to the original
core, CABeRNET allows to formulate hypotheses on the missing portions of real
networks, as well to investigate their generic properties, in the spirit of
complexity science.
  Results. CABeRNET offers a series of innovative simulation and modeling
functions and tools, including (but not being limited to) the dynamical
characterization of the gene activation patterns ruling cell types and
differentiation fates, and sophisticated robustness assessments, as in the case
of gene knockouts. The integration within the widely used Cytoscape framework
for the visualization and analysis of biological networks, makes CABeRNET a new
essential instrument for both the bioinformatician and the computational
biologist, as well as a computational support for the experimentalist. An
example application concerning the analysis of an augmented T-helper cell GRN
is provided.
