Simulation of Parameterized Stochastic Boolean Networks

A Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) , stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) , and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) . This package includes source code derived from the 'BoolNet' package, which is licensed under the Artistic License 2.0.