title: Evolving sensitivity balances Boolean Networks.
abstract: We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman's Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. In silico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks.
authors.affiliation: Centre for Complexity Science, University of Warwick, Coventry, West Midlands, United Kingdom.
authors.forename: Jamie X
authors.initials: JX
authors.lastname: Luo
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Turner
mesh_terms: D002454:Cell Differentiation / Q000235:genetics; D003198:Computer Simulation; D015870:Gene Expression; D053263:Gene Regulatory Networks; D008962:Models, Theoretical; D009154:Mutation; D013234:Stem Cells