This is A431 IERMv1.0 model described in the article
Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.
William W Chen, Birgit Schoeberl, Paul J Jasper, Mario Niepel, Ulrik B Nielsen, Douglas A Lauffenburger and Peter K Sorger. Molecular Systems Biology 2009; 5:239. PMID: 19156131 , DOI: 10.1038/msb.2008.74

Abstract:
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 K(d) 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.

The sbml model is available as supplemental material to the article and at http://www.cdpcenter.org/resources/models/chen-et-al-2008/ . It was slightly changed to make it valid SBML and to incorporate the step functions, described in the readme file and needed for inhibitor preincubation. the equilibration processes end at 1800 sec, so to reproduce the dynamics shown in the publication and supplemental material, only the time points after 1800 need to be considered. The parameter set is the hand fitted one used for Sfigure 3 in the supplemental materials. All species are in molecules, apart from HRG, EGF and Inh, which are in M.

The results shown in SFigure 3 can be calculated dividing the parameters ERK_PP , AKT_PP and ERB_B1_P_tot by ERK_t , AKT_t and EGFR_t , respectively. Somehow we did not find the right scaleing factor for the phosphorylated ErbB1 receptor. Therefore the model does only qualitatively reproduces the timecourses shown in the first row of Sfigure 3.

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