Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling


Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.

SEEK ID: https://fairdomhub.org/publications/272

PubMed ID: 25905717

Projects: SBEpo - Systems Biology of Erythropoietin

Journal: PLoS Comput Biol

Citation: PLoS Comput Biol. 2015 Apr 23;11(4):e1004192. doi: 10.1371/journal.pcbi.1004192. eCollection 2015 Apr.

Date Published: 23rd Apr 2015

Authors: L. A. D'Alessandro, R. Samaga, T. Maiwald, S. H. Rho, S. Bonefas, A. Raue, N. Iwamoto, A. Kienast, K. Waldow, R. Meyer, Marcel Schilling, J. Timmer, S. Klamt, U. Klingmuller

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Created: 11th Oct 2016 at 11:47

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