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Abstract
Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), which have also been widely studied as abstract models of complex systems and have been used to simulate different phenomena. We define the “common sea” (CS) as the set of nodes that take the same value in all the attractors of a given network realization, and the “specific part” (SP) as the set of all the other nodes, and we study their properties in different ensembles, generated with different parameter values. Both the CS and of the SP can be composed of one or more weakly connected components, which are emergent intermediate-level structures. We show that the study of these sets provides very important information about the behavior of the model. The distribution of distances between attractors is also examined. Moreover, we show how the notion of a “common sea” of genes can be used to analyze data from single-cell experiments.
Highlights
Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), proposed by one of us (Kauffman) more than 50 years ago, which have been widely studied as abstract models of complex systems, thanks to the fact that their dynamical behavior can be tuned from ordered to disordered by modifying a few key parameters
In the last part of this section, we look at the distribution of distances between pairs of pseudo-attractors
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Summary
Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), proposed by one of us (Kauffman) more than 50 years ago, which have been widely studied as abstract models of complex systems, thanks to the fact that their dynamical behavior can be tuned from ordered to disordered by modifying a few key parameters They have been applied to different biological phenomena, such as, e.g., cell differentiation [1,2,3], as well as to different fields, including robotics [4,5,6], the study of evolutionary processes [7,8,9] and the simulation of social systems [10,11,12,13]. This hypothesis has been strengthened by works showing that they can reproduce available data on HeLa cells [17] and on the distribution of avalanches of gene perturbations in yeast [18,19,20]
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