Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors, and are closer to criticality when chaotic dynamics would be expected, than classical RBNs.
There are studies where RBNs are generated in cells of a 2D lattice, similar to a cellular automaton, where each RBN is weakly coupled with its von Neumann neighbors @cite_35 @cite_29 @cite_48 . The goal is to model intercellular signaling in a tissue.
@cite_35 @cite_29 @cite_48
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Random boolean networks (shortly, RBN) have proven useful in describing complex phenomena occurring at the unicellular level It is therefore interesting to investigate how their dynamical behavior is affected by cell-cell interactions, which mimics those occurring in tissues in multicellular organisms It has also been suggested that evolution may tend to adjust the parameters of the genetic network so that it operates close to a critical state, which should provide evolutionary advantage ; this hypothesis has received intriguing, although not definitive support from recent findings It is therefore particularly interesting to consider how the tissue-like organization alters the dynamical behavior of the networks close to a critical state In this paper we define a model tissue, which is a cellular automaton each of whose cells hosts a full RBN, and we report preliminary studies of the way in which the dynamics is affected. Deciphering the influence of the interaction among the constituents of a complex system on the overall behaviour is one of the main goals of complex systems science. The model we present in this work is a 2D square cellular automaton whose of each cell is occupied by a complete random Boolean network. Random Boolean networks are a well-known simplified model of genetic regulatory networks and this model of interacting RBNs may be therefore regarded as a simplified model of a tissue or a monoclonal colony. The mechanism of cell-to-cell interaction is here simulated letting some nodes of a particular network being influenced by the state of some nodes belonging to its neighbouring cells. One possible means to investigate the overall dynamics of a complex system is studying its response to perturbations. Our analyses follow this methodological approach. Even though the dynamics of the system is far from trivial we could show in a clear way how the interaction affects the dynamics and the global degree of order. Information processing and information flow occur at many levels in the course of an organism's development and throughout its lifespan. Biological networks inside cells transmit information from their inputs (e.g. the concentrations of proteins or other signaling molecules) to their outputs (e.g. the expression levels of various genes). Moreover, cells do not exist in isolation, but they constantly interact with one another. We study the information flow in a model of interacting genetic networks, which are represented as Boolean graphs. It is observed that the information transfer among the networks is not linearly dependent on the amount of nodes that are able to influence the state of genes in surrounding cells.