Dendrite structure: Data-Driven Models to Bridge from Molecules to Morphology

树突结构：数据驱动模型连接分子和形态学

• 批准号: 10533281
• 负责人: Jonathon Howard
• 金额: $ 41.88万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10533281
• 关键词: Affect; Binding; Biological; Birth; Cells; Cessation of life; Cytoskeletal Proteins; Data; Dendrites; Development; Differential Equation; Dimensions; Disease; Drosophila genus; Etiology; Fractals; Functional disorder; Genetic; Genetic Diseases; Genotype; Goals; Growth; Image; Kinetics; Lead; Length; Markov Chains; Measurement; Measures; Microtubule-Associated Proteins; Modeling; Molecular; Morphogenesis; Morphology; Motor; Mutation; Nervous System; Neurologic; Neurons; Outcome; Output; Phenotype; Positioning Attribute; Process; Reporting; Research; Resolution; Shapes; Structure; Testing; Time; Traction; cell type; data-driven model; dopaminergic neuron; experimental study; fly; genetic manipulation; information processing; insight; kinetic model; live cell imaging; mathematical model; multi-scale modeling; multiscale data; novel; predictive modeling; simulation; temporal measurement; theories; tool

PROJECT SUMMARY Dendrite structure: Data-Driven Models to Bridge from Molecules to Morphology The highly branched structures of dendritic arbors enable the extraordinary connectivity and information-processing power of the nervous system. Altered dendritic morphologies are often associated with neurological conditions and diseases. While we know many molecular components underlying dendritic growth and structure through genetic and cell biological studies, we still do not understand how molecular interactions generate dendritic arbors, which are thousands to millions of times larger than the constituent molecules. The overall goal of this application is to develop data-driven models that predict, quantitatively, dendritic growth in Drosophila Class IV da neurons. These cells are chosen because their dendrites can be imaged with outstanding spatial and temporal resolution, and the genetic tools in flies facilitate molecular manipulations. Our central hypothesis is that the growing and shrinking tips of dendrites constitute an intermediate level of organization between molecules and morphology. This allows us to divide the large gap between genotype and phenotype into two parts: the first is from molecules to dendrite tips, and the second is from dendrite tips to morphology. The second part will be bridged using models. To attain our overall objective, we will pursue the following three specific aims: (i) We will formulate kinetic rules underlying the dynamics of dendritic tips using high-resolution, live-cell imaging to measure the birth and death of tips through branching and retraction, and the transition rates between different velocity states. (ii) We will develop multi-scale mathematical models that take as input the data such as obtained in Aim 1 and predict morphologies, which will be compared to real dendritic arbors. (iii) We will genetically perturb cytoskeletal proteins and use the models to test whether the effects on tip dynamics account for the altered dendrite structures. The expected outcome is mechanistic understanding of how morphological phenotypes emerge from molecular processes occurring at the level of dendrite tips. These results will positively impact the field by bridging genotype to phenotype and by providing insight into the pathophysiology of genetic disorders that affect neuronal structures.

项目摘要 树突结构：从分子到形态桥接的数据驱动模型 树突状乔木的高度分支结构可以使非凡的连通性和 神经系统的信息处理能力。树突状形态的改变通常是 与神经系统疾病和疾病有关。虽然我们知道许多分子 通过遗传和细胞生物学研究的树突生长和结构的组成部分， 我们仍然不了解分子相互作用如何产生树突状乔木，这是 比组成分子大数百万到数百万倍。 该应用程序的总体目标是开发以数据驱动的模型来预测， 定量地，果蝇IV类神经元的树突状生长。选择这些细胞 因为他们的树突可以通过出色的空间和时间分辨率进行成像，并且 果蝇中的遗传工具有助于分子操纵。我们的中心假设是增长 树突的缩小尖端构成了分子之间组织的中间水平 和形态。这使我们能够将基因型和表型之间的较大差距分为 两个部分：第一个是从分子到树突尖端，第二部分是从树突尖到 形态学。第二部分将使用模型桥接。 为了实现我们的整体目标，我们将追求以下三个具体目标：（i）我们将 使用高分辨率，活细胞制定树突状尖端动力学基础的动力学规则 成像通过分支和缩回来测量尖端的出生和死亡，以及过渡 不同速度状态之间的速率。 （ii）我们将开发多尺度的数学模型 作为输入，例如在AIM 1中获得的数据并预测形态，这将被比较 真正的树突状乔木。 （iii）我们将在遗传上扰动细胞骨架蛋白，并使用模型 测试对尖端动力学的影响是否解释了改变的树突结构。这 预期结果是对形态表型如何从形态表型中出现的理解 分子过程发生在树突尖端的水平上。这些结果将对 通过将基因型桥接到表型中，并提供对病理生理学的洞察力 影响神经元结构的遗传疾病。