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A systems-level study of signaling networks and differentiation

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? DESCRIPTION (provided by applicant): This proposal is a revised version of a new application entitled A systems-level study of signaling networks and differentiation. The question of how cell fates are accurately specified is of fundamental importance to understanding both normal developmental progression and disease mechanisms that alter cell fates. However we currently have very limited understanding of how gene expression is coordinated across time and space in a multicellular tissue so that cells accurately and reproducibly negotiate the transition from a multipotent to a differentiated state. The goal of thi proposal is to investigate how regulatory networks downstream of receptor tyrosine kinases (RTKs) coordinate expression of key genes needed for multipotency and accurate cell differentiation in the Drosophila eye. The Drosophila visual system provides a superbly tractable and well-defined experimental model with which to address this question. In particular, the stereotyped patterning and architecture of the fly retina facilitates the identification and trackig of individual cell types over space and time. Because signaling mechanisms have proven to be extraordinarily conserved, our exploration of the molecular networks and signaling interactions that drive differentiation in the fly are likely to be relevant to mammalian development and disease. Thus our discoveries could help identify new strategies for therapeutic intervention for diseases such as cancer in which fundamental network properties are disrupted. Aim 1 will explore how stimuli transduced by the EGFR and Notch signaling pathways are integrated with cell autonomous Yan network dynamics to ensure accurate cell differentiation in the developing Drosophila eye. We will quantify and compare the expression dynamics of core Yan network factors in thousands of individual retinal cells at each stage in their development in wild type versus perturbed conditions. The quantitative datasets will be used to construct a stochastic model that integrates information about Yan network molecules across both time and space. Aim 2 will investigate how specific features of Yan network topology contribute to the accuracy of cell differentiation in the developing Drosophila eye. Using a combination of modeling and molecular-genetic experimentation, we will test specific hypotheses regarding how interlocking feed forward and feedback loops can combine to enable accurate differentiation in a multicellular tissue.
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