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Coevolution of Information Processing and Topology in Hierarchical Adaptive Random Boolean Networks (1502.03338v1)
Published 9 Feb 2015 in physics.soc-ph, cs.SI, nlin.AO, and q-bio.MN
Abstract: Random Boolean networks (RBNs) are frequently employed for modelling complex systems driven by information processing, e.g. for gene regulatory networks (GRNs). Here we propose a hierarchical adaptive RBN (HARBN) as a system consisting of distinct adaptive RBNs - subnetworks - connected by a set of permanent interlinks. Information measures and internal subnetworks topology of HARBN coevolve and reach steady-states that are specific for a given network structure. We investigate mean node information, mean edge information as well as a mean node degree as functions of model parameters and demonstrate HARBN's ability to describe complex hierarchical systems.