Automatic Generation of Predictive Dynamic Models Reveals Nuclear Phosphorylation as the Key Msn2 Control Mechanism

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Sci. Signal.  28 May 2013:
Vol. 6, Issue 277, pp. ra41
DOI: 10.1126/scisignal.2003621

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Reducing the Options

Quantitative analysis of signaling systems is challenging because limited quantitative data are available and the data can be represented by many network models. Sunnåker et al. developed a computational approach called topological filtering to systematically and automatically integrate modeling and data acquisition to infer the set of mechanistically plausible models, thus vastly reducing the number of potential models. The approach iteratively eliminates reactions from the model to identify only those topological networks that fit the data. Application of their method to an extracellular signal–regulated kinase (ERK) pathway that could be represented by 512 possible network topologies reduced the possibilities to 16 and showed that a set of feedback reactions were necessary to quantitatively represent the results. Topological filtering applied to the regulation of the localization of Msn2, a yeast transcription factor controlled by phosphorylation by PKA (protein kinase A) in response to changes in glucose abundance, identified a single model that fit the data. Comparison of model predictions with experimental data showed that the nuclear phosphorylation rate was key to controlling Msn2 nuclear abundance in response to cAMP (cyclic adenosine monophosphate), a signal produced as cells recover from glucose starvation.