title: Random Boolean network models and the yeast transcriptional network.
abstract: The recently measured yeast transcriptional network is analyzed in terms of simplified Boolean network models, with the aim of determining feasible rule structures, given the requirement of stable solutions of the generated Boolean networks. We find that, for ensembles of generated models, those with canalyzing Boolean rules are remarkably stable, whereas those with random Boolean rules are only marginally stable. Furthermore, substantial parts of the generated networks are frozen, in the sense that they reach the same state, regardless of initial state. Thus, our ensemble approach suggests that the yeast network shows highly ordered dynamics.
authors.affiliation: Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131.
authors.forename: Stuart
authors.initials: S
authors.lastname: Kauffman
authors.forename: Carsten
authors.initials: C
authors.lastname: Peterson
authors.forename: Björn
authors.initials: B
authors.lastname: Samuelsson
authors.forename: Carl
authors.initials: C
authors.lastname: Troein
mesh_terms: D019295:Computational Biology; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D013997:Time Factors; D014158:Transcription, Genetic