Integrated visualization, control, and analysis of GEF – GTPase networks in living cells

少数Rho家族GTP酶参与广泛的基本细胞行为。 特异性可能是由于空间和时间控制的GT3“激活”;鸟嘌呤交换因子 （GEF）产生具有精确定时和定位的激活的GTP结合的GTP酶， 与粘附分子、膜结构域和其他局部结构的相互作用指定GEF-GTdR 交互. GEF/GT回路复杂，具有局部反馈，多个GEF控制一个 GTTG，反之亦然。要剖析这种时空调节电路需要成像，新的 可以从成像数据中剖析因果关系的分析技术。根据PAR的意图- 19-158（生物工程研究赠款），我们提出了一个多学科合作，利用有机 化学，蛋白质工程，成像和计算机科学，从根本上推动信号转导 成像和分析。作为一个生物学实验平台，我们将探索GEF-GTdR相互作用在细胞中的作用。 突起、单细胞迁移和集体迁移。我们将为全球环境基金制定一个可推广的办法 GTdR生物传感器，并调整我们的成熟的GTdR生物传感器，以图像GEF和GTdR活动在同一个细胞。 由于GEF-GT3相互作用是异质和复杂的，因此需要多重成像， 量化它们的相对动态。然而，当使用 两个生物传感器在同一个细胞中。因此，我们将开发新的生物传感器设计，大大减少细胞 扰动即使是最精确的重叠分子激活成像也没有揭示因果关系 关系。因此，我们将采用格兰杰因果关系推断的框架，这是最初的 设计用于金融市场分析，从成像中提取因果关系和反馈相互作用 数据要将现有的格兰杰因果关系概念转化为分析，需要采取许多步骤 空间和时间分布的分子过程。最重要的是，我们将为 多变量时间序列模型中的格兰杰因果关系推断将捕获空间关系，我们将 联合收割机将高维统计回归的原理与控制理论的方法相结合， 通过强反馈耦合的变量之间的信息流。我们还将开发一部小说 一种聚类方法，在高维特征空间中保留数据的邻域拓扑， 在细胞轮廓的欧几里德空间中识别信号微区。最后，为了验证我们的 假设，我们将使用新的光活化和光降解类似物的GEF与GT3一起使用 生物传感器来控制一种蛋白质，同时观察另一种。这项研究计划将生产生物传感器， 减少扰动、生物传感器/光遗传学多路复用能力和图像分析/建模 揭示非线性时空控制信令网络拓扑结构的必要方法 途径。所有工具都将有效地部署到社区。

A small number of Rho family GTPases participate in a broad array of fundamental cellular behaviors. Specificity is possible due to spatial and temporal control of GTPase “activation”; Guanine exchange factors (GEFs) generate activated, GTP-bound GTPases with precise timing and localization, while specialized interactions with adhesion molecules, membrane domains and other localized structures specify GEF-GTPase interactions. GEF/GTPase circuits are complex, with localized feedbacks, multiple GEFs controlling one GTPase, and vice versa. To dissect this spatiotemporally regulated circuitry requires imaging, and new analytical techniques that can dissect causal relationships from imaging data. Following the intentions of PAR- 19-158 (Bioengineering Research Grants), we propose a multidisciplinary collaboration leveraging organic chemistry, protein engineering, imaging, and computer science to fudnamentally advance signal transduction imaging and analysis. As a biological testbed we will explore the role of GEF-GTPase interactions in cell protrusion, single cell migration and collective migration. We will develop a generalizable approach to GEF biosensors, and adapt our proven GTPase biosensors to image GEF and GTPase activities in the same cell. Because GEF-GTPase interactions are heterogeneous and complex, multiplexed imaging is necessary to quantify their relative dynamics. However, perturbation of cell behavior is especially problematic when using two biosensors in the same cell. We will therefore develop new biosensor designs that greatly reduce cell perturbation. Even the most precise imaging of overlapping molecular activations has not revealed causal relationships. We will therefore adopt the framework of Granger Causality inference, which was originally devised for financial market analysis, to extract causal connections and feedback interactions from imaging data. Numerous steps will be necessary to translate the existing concepts of Granger causality to the analysis of spatially and temporally distributed molecular processes. Most importantly, we will implement a schema for Granger causality inference in multivariate time series models that will capture spatial relations, and we will combine principles of high-dimensional statistical regression with approaches from control theory to estimate information flows between variables that are coupled by strong feedbacks. We will also develop a novel clustering approach that preserves the neighborhood topology of data in a high-dimensional feature space and in the Euclidian space of the cell outline to identify signaling microdomains. Finally, to test and confirm our hypotheses, we will use new photo-activatable and photo-inhibitable analogs of GEFs together with GTPase biosensors to control one protein while observing another. This research plan will produce biosensors with reduced perturbation, biosensor/optogenetic multiplexing capabilities, and image analysis/modeling approaches necessary to shed light on the network topology of nonlinear, spatiotemporally controlled signaling pathways. All tools will efficiently deployed to the community.