The horizontal axis shows mutational robustness *R*_{μ}, which is the fraction of a viable network's neighbors (networks differing from it in only one regulatory interaction) that arrive at the same equilibrium state **S** _{∞} given the initial state **S**(0). The vertical axes show two different measures of robustness to noise. The left vertical axis (+, solid line) shows *R*_{ν,} _{1}, the probability that a change in *one* gene's expression state in the initial expression pattern **S**(0) leaves the network's equilibrium expression pattern **S** _{∞} unchanged. The right vertical axis (circles, dashed line) shows *R*_{ν,} _{*}, the fraction of genes whose expression state in **S**(0) has to change at random, such that the probability that a network arrives at the equilibrium state **S** _{∞} falls below a value of ½. In a network with large *R*_{ν,} _{*}, perturbation of the expression states of a large fraction of genes affects the equilibrium pattern only rarely. *R*_{μ} is highly associated with both *R*_{ν,} _{1} (Spearman's *s* = 0.70) and *R*_{ν,} _{*} (Spearman's *s* = 0.69, *p* < 10^{−15}; 10^{3} networks for both). The sample is obtained from a Monte Carlo simulation as described in Methods (*N* = 20, *M* ≈ 0.25 *N*^{2} regulatory interactions, *d* = 0.5, *w*_{ij} = ±1).

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