Model of signaling networks
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The units forming the system are placed on the nodes of a one-dimensional lattice and establish bi-directional nearest-neighbor connections. With some probability we add long-range unidirectional connections until there are keN of such excess links, being ke the average excess connectivity, and N the number of units. Each unit processes a set of input signals in the way that it is described in the text. The signal received from one of its neighbors is replaced with a random value. |
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Quantification of the correlations in the state of Boolean signaling networks. (a) For a small number of random links, the time correlations display trivial long-range correlations such as found for Brownian noise. (b) For an intermediate value of ke, long-range correlations emerge and the power spectrum displays a power-law behavior, 1/f. b1 and b2 display the state of the system according to different definitions. (c) For a large number of random links, ke 0.90, the dynamics are less correlated. (d) Estimation of temporal autocorrelations of the state of the system by the detrended fluctuation analysis method (5). We show the log–log plot of the fluctuations F(n) inthe state of the system versus time scalenfor the time series shown in a--c. In such a plot, a straight line indicates a power-law dependence. The slope of the lines yields the scaling exponent, which for a number of physiologic signals from free-running, healthy, and mature systems takes values close to 1. |
Albert Díaz-Guilera