Transcriptional dynamics of gene regulatory networks are regulated in highly precise manner, despite a fluctuating environment and mutations. We model these dynamics as those of a coupled logistic map on a network 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  . 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 genotypic perturbations is required. This emergence of the edge of chaos is a URLanization phenomenon and does not need a fine tuning of parameters.
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 URLanization phenomenon and does not need a fine tuning of parameters.
R
Transcriptional dynamics of gene regulatory networks are regulated in highly precise manner, despite a fluctuating environment and mutations. We model these dynamics as those of a coupled logistic map on a network
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
A
method
R
genotypic
network