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

Sunnåker, Mikael; Zamora-Sillero, Elias; Dechant, Reinhard; Ludwig, Christina; Busetto, Alberto Giovanni; Wagner, Andreas; Stelling, Joerg

doi:10.1126/scisignal.2003621

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Sunnåker, M., Zamora-Sillero, E., Dechant, R., Ludwig, C., Busetto, A. G., Wagner, A., & Stelling, J. (2013). Automatic Generation of Predictive Dynamic Models Reveals Nuclear Phosphorylation as the Key Msn2 Control Mechanism. Science Signaling, 6(277), ra41. https://doi.org/10.1126/scisignal.2003621

Abstract

Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. Here, we describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model, and automatically generates a set of simpler models compatible with observational data. As a proof-of-principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharo