Effect of model selection on prediction of periodic behavior in gene regulatory networks
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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.
T. Gedeon, G. Cummins, and J. J. Heys, “Effect of Model Selection on Prediction of Periodic Behavior in Gene Regulatory Networks,” Bull Math Biol, vol. 74, no. 8, pp. 1706–1726, May 2012.