Robustness Leads Close to the Edge of Chaos in Coupled Map Networks: toward the understanding of biological networks

Abstract

Dynamics in biological networks are in general robust against several perturbations. We investigate a coupled map network as a model motivated by gene regulatory networks and design systems which are robust against phenotypic perturbations (perturbations in dynamics), as well as systems which are robust against mutation (perturbations in network structure). To achieve such a design, we apply a multicanonical Monte Carlo method. Analysis based on the maximum Lyapunov exponent and parameter sensitivity shows that systems with marginal stability, which are regarded as systems at the edge of chaos, emerge when robustness against network perturbations is required. This emergence of the edge of chaos is a self-organization phenomenon and does not need a fine tuning of parameters.

14 pages, 5 figures

References in corpus (3)

• Chaotic Gene Regulatory Networks Can Be Robust Against Mutations and Noise
• Multicanonical sampling of rare events in random matrices
• Probability of graphs with large spectral gap by multicanonical Monte Carlo

Cited by in corpus (5)

• Colloquium: Criticality and dynamical scaling in living systems
• Emergence of cooperative bistability and robustness of gene regulatory networks
• Rare-Event Sampling Analysis Uncovers the Fitness Landscape of the Genetic Code
• Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution
• Phenotype selection due to mutational robustness