title: Effect of model selection on prediction of periodic behavior in gene regulatory networks.
abstract: One of the current challenges for cell biology is understanding of the system level cellular behavior from the knowledge of a network of the individual subcellular agents. We address a question of how the model selection affects the predicted dynamic behavior of a gene network. In particular, for a fixed network structure, we compare protein-only models with models in which each transcriptional activation is represented both by mRNA and protein concentrations. We compare linear behavior near equilibria for both cyclic feedback systems and a general system. We show that, in general, explicit inclusion of the mRNA in the model weakens the stability of equilibria. We also study numerically dynamics of a particular gene network and show significant differences in global dynamics between the two types of models.
authors.affiliation: Department of Mathematics, Montana State University, Bozeman, 59715, USA. gedeon@math.montana.edu
authors.forename: Tomáš
authors.initials: T
authors.lastname: Gedeon
authors.forename: Graham
authors.initials: G
authors.lastname: Cummins
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Heys
mesh_terms: D003198:Computer Simulation; D005246:Feedback; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D011506:Proteins / Q000235:genetics; D012333:RNA, Messenger / Q000235:genetics; D014158:Transcription, Genetic
chemical_list: D011506:Proteins; D012333:RNA, Messenger