Systems analysis and prediction of endothelial cross-family signaling

项目总结/摘要 血管内皮生长因子（VEGF）在肥胖和至少70种其他疾病中指导关键信号传导过程。 然而，仅仅关注这个信号节点并没有实现可预测的血管生成控制的承诺。现款车型 是不完整的，因为除了VEGF之外，其他生长因子也有助于血管疾病的进展， 无法通过瞄准系统中的一个节点进行可预测的调节。因此，有必要继续说明 对于附加的多分量信令网络的复杂性，可以通过数据驱动， 计算系统生物学与信号和功能反应的实验分析密切相关。 为了实现这一目标，我们的目标是研究一种新的网络调节范式，称为跨家族信号传导，其中 来自一个生长因子家族的成员[例如，血小板衍生生长因子（PDGFs）]结合并通过成员发出信号 另一个家庭（例如，VEGF）。我们假设，系统的蛋白质结构和下游的检查， 通过模拟，配体工程，网络量化和计算， 建模可以揭示控制血管生成的新机制。我们将通过三个目标来检验这一假设， 灵敏地定量交叉家族结合的受体活化速率和功能反应（例如，扩散， 迁移和屏障功能）;预测和测量跨家族结合的结构特性，通过分子 模拟和定向进化;以及开发交叉作用的经验证的确定性模型（质量作用动力学模型）， 家族信号传导，并将其应用于研究和控制人类细胞系统中跨家族信号传导的动力学， silico。 我们已经准备好领导这项新的研究，因为我们是第一批追求这一重要理论范式的人， 我们领导这项事业，通过基于结构/功能的计算建模来理解细胞信号传导。这项工作将 促进细胞信号传导、系统生物学和肥胖预测设计领域的观点转变和创新- 集中治疗。

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.