Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data

By William W. Chen, Birgit Schoeberl, Paul J. Jasper, Mario Niepel, Ulrik B. Nielsen, Douglas A. Lauffenburger, Peter K. Sorger. Molecular Systems Biology, 2009

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The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB‐activated pathways we have constructed, trained and analyzed a mass action model of immediate‐early signaling involving ErbB1–4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log‐linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the Kd for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch‐like responses. Thus, key system‐wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.

Subfields: Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/dfbf73b4d67dd34c855cf8e556071b7e092bb704

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Citations in the corpus

Causal network inference using biochemical kinetics (2014, positive): “On the other hand, when the network topology is known, non-linear ordinary differential equations (ODEs) are widely used to model biochemical dynamics (Chen et al. , 2009; Kholodenko, 2006).”
Systems biology approaches for advancing the discovery of effective drug combinations (2015, background): “For example, Chen et al. measured parameter sensitivity for several independent fits and saw that the rank order of the most sensitive parameters was nearly the same across the fits for a given output, therefore parameter uncertainty did not affect major model predictions [24].”