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

少量的Rho家族GTP酶参与了一系列基本的细胞行为。 特异性可能是由于GTP酶“激活”的空间和时间控制；鸟嘌呤交换因子 (GEF)生成激活的、GTP结合的GTP酶，具有精确的时间和定位，同时专门 与黏附分子、膜结构域和其他局部结构的相互作用 互动。全球环境基金/GTPase回路很复杂，有局部反馈，多个GEF控制一个 GTP酶，反之亦然。要剖析这种时空调节的电路，需要成像，而新的 可以从成像数据中剖析因果关系的分析技术。遵循PAR的意图- 19-158(生物工程研究拨款)，我们建议利用有机技术进行多学科合作 化学、蛋白质工程、成像和计算机科学，极大地促进了信号转导 成像和分析。作为一个生物试验台，我们将探索环境基金-GTP酶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种可推广的方法 此外，我们还可以使用生物传感器，并改装我们成熟的GTPase生物传感器，以成像同一细胞中的全环基金和GTPase活性。 因为全球环境基金-GTP酶相互作用是异质的和复杂的，所以多路成像是必要的 量化它们的相对动力学。然而，在使用时，细胞行为的扰动尤其成问题 同一细胞中的两个生物传感器。因此，我们将开发新的生物传感器设计，大大减少细胞 微扰。即使是最精确的重叠分子激活成像也没有揭示原因 两性关系。因此，我们将采用格兰杰因果关系推理的框架，该框架最初是 专为金融市场分析而设计，从成像中提取因果联系和反馈交互 数据。要将格兰杰因果关系的现有概念转化为分析，需要许多步骤 时空分布的分子过程。最重要的是，我们将为 多变量时间序列模型中的Granger因果关系推断将捕捉空间关系，我们将 高维统计回归原理与控制论方法相结合进行估计 由强反馈耦合的变量之间的信息流。我们还将开发一部小说 一种在高维特征空间中保持数据的邻域拓扑的聚类方法 在细胞轮廓的欧几里得空间中识别信号微域。最后，为了测试和确认我们的 假设，我们将使用新的GEF的光激活和光抑制类似物以及GTP酶 生物传感器在观察一种蛋白质的同时控制另一种蛋白质。这项研究计划将生产生物传感器和 减少扰动、生物传感器/光遗传多路传输能力和图像分析/建模 阐明非线性时空受控信令的网络拓扑所需的方法 小路。所有工具都将高效地部署到社区。

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.