Resort workers: the role of social media in connecting youth travellers and mediating the neo-tribe

<div><p>The detection of the singleton attractors is of great significance for the systematic study of genetic regulatory network. In this paper, we design an algorithm to compute the singleton attractors and pre-images of the strong-inhibition Boolean networks which is a biophysically plausible gene model. Our algorithm can not only identify accurately the singleton attractors, but also find easily the pre-images of the network. Based on extensive computational experiments, we show that the computational time of the algorithm is proportional to the number of the singleton attractors, which indicates the algorithm has much advantage in finding the singleton attractors for the networks with high average degree and less inhibitory interactions. Our algorithm may shed light on understanding the function and structure of the strong-inhibition Boolean networks.</p></div

An Algorithm for Finding the Singleton Attractors and Pre-Images in Strong-Inhibition Boolean Networks

<div><p>The detection of the singleton attractors is of great significance for the systematic study of genetic regulatory network. In this paper, we design an algorithm to compute the singleton attractors and pre-images of the strong-inhibition Boolean networks which is a biophysically plausible gene model. Our algorithm can not only identify accurately the singleton attractors, but also find easily the pre-images of the network. Based on extensive computational experiments, we show that the computational time of the algorithm is proportional to the number of the singleton attractors, which indicates the algorithm has much advantage in finding the singleton attractors for the networks with high average degree and less inhibitory interactions. Our algorithm may shed light on understanding the function and structure of the strong-inhibition Boolean networks.</p></div

The flow chart for detecting all singleton attractors of the cell-cycle network of budding yeast.

<p>The flow chart for detecting all singleton attractors of the cell-cycle network of budding yeast.</p

The flow chart for determing the pre-images of target state <i>S</i> = (0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0).

<p>The flow chart for determing the pre-images of target state <i>S</i> = (0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0).</p

An Algorithm for Finding the Singleton Attractors and Pre-Images in Strong-Inhibition Boolean Networks - Fig 3

(a) The CPU computational time assumed of the algorithm for finding the singleton attractors of M (M = 500) random genetic regulatory network with N = 50, 〈k〉 = 3, r = 0.4. (b) Semi-logarithmic plot of the averaged CPU computational time (avT) as a function of the network size N. For each N, the value of avT is averaged over M (M ≥ 500) samples. And the error bars denote the range of CPU computational time, while the upper and lower ends of bars represent the maximum and minimum values, respectively. The straight dashed line is linearly fit of the data, indicative of the correlation avT ∝ 1.34N.</p

Experiment results of five classical networks.

<p>Experiment results of five classical networks.</p

The basin of the singleton attractor (0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0) is displayed hierarchically.

<p>The basin of the singleton attractor (0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0) is displayed hierarchically.</p