title: Asynchronous random Boolean network model based on elementary cellular automata rule 126.
abstract: This paper considers a simple Boolean network with N nodes, each node's state at time t being determined by a certain number k of parent nodes, which is fixed for all nodes. The nodes, with randomly assigned neighborhoods, are updated based on various asynchronous schemes. We make use of a Boolean rule that is a generalization of rule 126 of elementary cellular automata. We provide formulas for the probability of finding a node in state 1 at a time t for the class of asynchronous random Boolean networks (ARBN) in which only one node is updated at every time step, and for the class of generalized ARBNs (GARBN) in which a random number of nodes can be updated at each time point. We use simulation methods to generate consecutive states of the network for both the real system and the models under the various schemes. The results match well. We study the dynamics of the models through sensitivity of the orbits to initial values, bifurcation diagrams, and fixed point analysis. We show, both theoretically and by example, that the ARBNs generate an ordered behavior regardless of the updating scheme used, whereas the GARBNs have behaviors that range from order to chaos depending on the type of random variable used to determine the number of nodes to be updated and the parameter combinations.
authors.affiliation: Department of Mathematics, University of Nebraska at Omaha, Omaha, Nebraska 68182-0243, USA. dmatache@mail.unomaha.edu
authors.forename: Mihaela T
authors.initials: MT
authors.lastname: Matache
authors.forename: Jack
authors.initials: J
authors.lastname: Heidel
mesh_terms: D000818:Animals; D032701:Biomimetics / Q000379:methods*; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology*