The Impact of Self-loops in Random Boolean Network Dynamics: A Simulation Analysis

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Marcello Pelillo, Irene Poli, Andrea Roli, Roberto Serra, Debora Slanzi, Marco Villani (eds.)
Artificial Life and Evolutionary Computation, pages 104–115
Springer International Publishing, Cham

Random Boolean Networks (RBNs) are a popular and successful model of gene regulatory networks, especially for analysing emergent properties of cell dynamics. Since completely random networks are unrealistic, some work has been done to extend the original model with structural and functional properties observed in biological networks. Among recurring motifs identified by experimental studies, auto-regulation seems to play a significant role in gene regulatory networks. In this paper we present a model of auto-regulatory mechanisms by introducing self-loops in RBNs. Experiments are performed to analyse the impact of self-loops in the RBNs asymptotic behaviour. Results show that the number of attractors increases with the amount of self-loops, while their robustness and stability decrease.