Effect of model selection on prediction of periodic behavior in gene regulatory networks

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2012-08

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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.

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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.
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