Asynchronous random Boolean network model based on elementary cellular automata rule 126

Mihaela T. Matache, Jack Heidel

Research output: Contribution to journalArticle

19 Citations (Scopus)

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.

Original languageEnglish (US)
Article number026232
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume71
Issue number2
DOIs
StatePublished - Feb 1 2005

Fingerprint

Random Boolean Networks
Boolean Model
cellular automata
Cellular Automata
Network Model
Model-based
Vertex of a graph
random numbers
random variables
chaos
diagrams
orbits
sensitivity
Boolean Networks
Random number
Bifurcation Diagram
Simulation Methods
Updating
simulation
Consecutive

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

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