The present project has health relevance due to the important role of receptor tyrosine kinases (RTK) in control of pivotal cellular processes, including proliferation, differentiation, metabolism, survival, and apoptosis. Aberrant signaling by the epidermal growth factor receptor (EGFR) and insulin receptor (IR) is a leading cause of many human diseases that range from diabetes to chronic inflammatory syndromes and cancer. Despite a rapid growth of our knowledge of protein and lipid components involved in RTK signaling networks, an integrative, quantitative picture of their dynamic behavior and interpathway crosstalk remains elusive. Computational models, including EGFR pathway models developed by our group, have emerged as a novel tool to provide insights into the intricate relationships between external stimuli and cellular responses and reveal mechanisms that enable RTK networks to amplify and process signals, reduce noise and generate bistable dynamics or oscillations. Recent discoveries have changed our perception of the nature of signal specificity and showed that distinct spatio-temporal activation profiles of the same repertoire of proteins involved in the EGFR and IR signaling networks result in different gene activation patterns and diverse physiological responses. During the previous period of support, our work has demonstrated the considerable potential of a novel cross-disciplinary approach that combines experimental studies, nonlinear systems analysis and interactive computational models to achieve a quantitative understanding of RTK signaling networks. In this application we will determine the molecular and kinetic factors controlling the different response patterns to EGF and insulin, validate hypotheses about the regulation of the spatio- temporal dynamics and EGFR-IR crosstalk suggested by computational modeling, and examine how cells interpret signals in a context-dependent manner. Together with temporal responses, the spatial pattern of the EGFR- and IR-mediated signaling will be analyzed to comprehend critical cell fate decisions. The findings will provide a powerful tool to predict cellular responses to growth factors and insulin, and this analysis is the key to understanding the mechanisms causing cancer and diabetes.
The Specific Aims are: (1) To extend the quantitative analysis of the EGFR network and elucidate functional interactions between mitogenic (Ras/ERK) and survival (PI3K/Akt) signaling pathways; (2) To determine the consequences of the spatio- temporal dynamics of the MARK cascade, including subcellular localization and potential testability, on transcription factor responses; (3) To elucidate the potentiation by insulin of EGF mitogenic signaling and integrate the EGFR and IR networks into our experimental and computational analyses. ? ? ?
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