title: Dynamical properties of a boolean model of gene regulatory network with memory.
abstract: Classical random Boolean networks (RBN) are not well suited to describe experimental data from time-course microarray, mainly because of the strict assumptions about the synchronicity of the regulatory mechanisms. In order to overcome this setback, a generalization of the RBN model is described and analyzed. Gene products (e.g., regulatory proteins) are introduced, with each one characterized by a specific decay time, thereby introducing a form of memory in the system. The dynamics of these networks is analyzed, and it is shown that the distribution of the decay times has a strong effect that can be adequately described and understood. The implications for the dynamical criticality of the networks are also discussed.
authors.affiliation: European Centre for Living Technology (ECLT), University Cá Foscari of Venice, Italy. alex.graudenzi@ecltech.org
authors.forename: Alex
authors.initials: A
authors.lastname: Graudenzi
authors.forename: Roberto
authors.initials: R
authors.lastname: Serra
authors.forename: Marco
authors.initials: M
authors.lastname: Villani
authors.forename: Chiara
authors.initials: C
authors.lastname: Damiani
authors.forename: Annamaria
authors.initials: A
authors.lastname: Colacci
authors.forename: Stuart A
authors.initials: SA
authors.lastname: Kauffman
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D056004:Molecular Dynamics Simulation; D013995:Time