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How biochemical networks encode biological specificity: Modulation of cell migration by isoform specific ERK and Akt signaling (BioSpec)
Start date: Mar 2, 2009, End date: Mar 1, 2011 PROJECT  FINISHED 

Understanding how biochemical signaling networks encode biological specificity is a fundamental challenge for biologists in the 21st century. Genome sequences by providing the “parts list” for such signaling networks, were expected to advance insight. However, it has become clear that to understand specificity analyzing only the “parts list” is not enough; it is the complex orchestration of these parts’s expression, interactions, activation, and deactivation in both space and time that encode biological specificity. Thus, understanding the biological specificity code requires investigating the spatiotemporal dynamics of biochemical signaling networks. This proposal focuses on understanding how the spatiotemporal dynamics of epidermal growth factor (EGF) gradient-induced signaling and associated feedback loops encode a cell’s decision to migrate and invade. A particular focus is on the isoform-specific roles of ERK1, ERK2, Akt1, and Akt2. Traditionally, these protein isoforms have been assumed to have very similar roles in the phenotypic response to EGF signaling. However, our preliminary data show that these isoforms have very distinct, opposing roles for control of EGF gradient-induced cell migration in an aggressively migrating mammalian cell line. To measure, interpret, and understand EGF gradient-induced signaling and the resultant cell migration, we propose using an interdisciplinary, systems biology approach combining modern biochemistry, quantitative mass spectrometry, live-cell imaging, spatially-resolved mechanistic modeling, and empirical data-driven modeling. Model-based experimental design will be the hub that connects modeling with experiments through an iterative model building cycle. As result we hope to gain a mechanistic understanding of how EGF gradient signals are spatially propagated through a cell, coupled with the ability to predict cell migration outcomes based on the spatiotemporal signaling patterns.

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