Thursday, September 12, 2024
9/12/2024
PROJECT SUMMARY/ABSTRACT The vascular endothelial growth factors (VEGFs) direct key signaling processes in obesity and at least 70 other diseases. However, focus on this signaling node alone has not achieved the promise of predictable angiogenic control. Current models are incomplete as other growth factors, besides VEGF, contribute to vascular disease progression, presenting a complexity that cannot be predictably regulated by targeting one node in this system. Therefore, there is a continuing need to account for the complexity of additional, multi-component signaling networks, a goal that can be achieved via data-driven, computational systems biology in close concert with experimental analysis of signaling and functional response. Toward this goal, we aim to examine a novel paradigm of network regulation called cross-family signaling, in which members from one growth factor family [e.g., platelet derived growth factors (PDGFs)] bind to and signal through members of another family (e.g., VEGFRs). We hypothesize that systematic examination of protein structure and downstream signaling within the cross-family paradigm via simulation, ligand-engineering, network quantification, and computational modeling can uncover novel mechanisms to control angiogenesis. We will test this hypothesis through three aims, sensitively quantifying receptor activation rates and functional responses of cross-family binding (e.g., proliferation, migration, and barrier function); predicting and measuring the structural properties of cross-family binding via molecular simulations and directed evolution; and developing validated deterministic models (mass-action kinetic modeling) of cross- family signaling and applying them to study and control the dynamics of cross-family signaling in human cell systems, in silico. We are primed to lead this new research because we are among the first to pursue this important theoretical paradigm, and we lead this cause to understand cell signaling via structure/function–based computational modeling. This work will catalyze a shift in perspective and innovation in the areas of cell signaling, systems biology, and predictive design of obesity- focused therapies.
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