title: On the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell types.
abstract: The asymptotic dynamics of random Boolean networks subject to random fluctuations is investigated. Under the influence of noise, the system can escape from the attractors of the deterministic model, and a thorough study of these transitions is presented. We show that the dynamics is more properly described by sets of attractors rather than single ones. We generalize here a previous notion of ergodic sets, and we show that the Threshold Ergodic Sets so defined are robust with respect to noise and, at the same time, that they do not suffer from a major drawback of ergodic sets. The system jumps from one attractor to another of the same Threshold Ergodic Set under the influence of noise, never leaving it. By interpreting random Boolean networks as models of genetic regulatory networks, we also propose to associate cell types to Threshold Ergodic Sets rather than to deterministic attractors or to ergodic sets, as it had been previously suggested. We also propose to associate cell differentiation to the process whereby a Threshold Ergodic Set composed by several attractors gives rise to another one composed by a smaller number of attractors. We show that this approach accounts for several interesting experimental facts about cell differentiation, including the possibility to obtain an induced pluripotent stem cell from a fully differentiated one by overexpressing some of its genes.
authors.affiliation: Department of Social, Cognitive and Quantitative Sciences, University of Modena and Reggio Emilia, viale A. Allegri 9, I-42100 Reggio Emilia, Italy.
authors.forename: R
authors.initials: R
authors.lastname: Serra
authors.forename: M
authors.initials: M
authors.lastname: Villani
authors.forename: A
authors.initials: A
authors.lastname: Barbieri
authors.forename: S A
authors.initials: SA
authors.lastname: Kauffman
authors.forename: A
authors.initials: A
authors.lastname: Colacci
mesh_terms: D002454:Cell Differentiation; D002477:Cells; D008954:Models, Biological