evolution of discrete gene regulatory models
Gene regulatory networks (GRNs) are complex control systems that govern the interaction of genes, which ultimately control cellular processes at the protein level. GRNs can be represented using abstract models such as random Boolean networks (RBNs), where gene activities and their interactions are captured as nodes with associated Boolean functions, which receive activation or repressor signals from other nodes. We have developed an evolutionary model of gene regulatory networks using RBNs to study the dynamic behavior of these control systems. We explore a range of different network parameters such as excess graph, sensitivity, basin entropy, number of attractors and maximum length of attractors in RBNs. We investigate the effects of mutations and crossover on the fitness of RBNs, and we show that over the course of evolution, networks with a low level of damage spreading and a high tolerance to random perturbations can be produced. We also demonstrate that these networks are able to adapt to a range of different perturbations obtaining a high level of stability.
gene regulatory network evolutionary design random boolean model systems biology