Random Boolean network models and the yeast transcriptional network.
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.
Kauffman
Stuart
S
Department of Cell Biology and Physiology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131.
Peterson
Carsten
C
Samuelsson
Björn
B
Troein
Carl
C
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