SPATIAL DOMAINS IN SIGNALING NETWORKS

这个子项目是许多利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 本项目的总体目标是研究形态和空间各向异性在调节细胞内信号传导中的作用。 细胞内信号转导过程通常涉及小分子信使的局部产生和蛋白激酶、蛋白磷酸酶和其他信号组分的局部活化。长期以来，人们一直认为，在使用相似或相同的信号传导途径的信号的作用中观察到的一些特异性来自细胞内信号传导组分的这种空间域。亚膜胞质理解空间域的起源和动态对于解开细胞复杂性非常重要，因为细胞内对信息流的反应直接依赖于空间规范。 该项目的总体方法是开发空间特定的局部基于微分方程的模型，使用真实的细胞形状和相关组件的位置。 这些模型正在使用虚拟细胞开发，以探索参数空间和拟合实验数据，然后分析各种因素（信号网络连接图，个体反应动力学，扩散约束，形状等）如何影响细胞的生长。影响空间域的动态。 从这些模拟产生的预测，然后在模型建立/完善的迭代周期中进行实验测试  模拟预测  实验

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The overall goal of this project is to study the role of morphology and spatial anisotropy in regulating intracellular signaling. The intracellular signal transducing process often involves the local production of small molecule messengers and the local activation of protein kinases, protein phosphatases and other signaling components. It has long been thought that some of the specificity observed in the effects of signals that use similar or identical signaling pathways arise from such spatial domains of signaling components within the cells. submembrane cytosolic. Understanding the origins and dynamics of spatial domains is very important for unraveling cellular complexity, since response to information flow within the cell is directly dependent on spatial specifications. The overall approach for this project is to developed spatially-specified partialdifferential equation-based models using realistic cell shape and location of relevant components. These models are being developed using the Virtual Cell to explore the parameter space and fit experimental data, followed by analysis of how the various factors (signaling network connectivity map, individual reaction kinetics, diffusion constraints, shape, etc.) affect the dynamics of spatial domains. The predictions generated from these simulations are then being tested experimentally in an iterative cycle of model building/refinement  simulation predictions  experiment.