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

基本信息

批准号：
10308521
负责人：
Jonathon Howard
金额：
$ 41.88万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10308521
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 structure Neurologic Neurons Outcome Output Phenotype Positioning Attribute Process Reporting Research Resolution Shapes Structure Testing Time base cell type data-driven model density 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

项目摘要

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.

项目概要树突结构：数据驱动模型连接分子和形态学树突乔木的高度分支结构实现了非凡的连接性和神经系统的信息处理能力。树突形态的改变通常是与神经系统状况和疾病有关。虽然我们知道许多分子通过遗传和细胞生物学研究，树突生长和结构的基础成分，我们仍然不明白分子相互作用如何产生树突乔木，这是比组成分子大数千至数百万倍。该应用程序的总体目标是开发数据驱动模型来预测，定量地，果蝇 IV 类 da 神经元中的树突生长。这些细胞被选择因为它们的树突可以以出色的空间和时间分辨率成像，并且果蝇的遗传工具促进了分子操作。我们的中心假设是，不断增长的树突的收缩尖端构成了分子之间的中间组织水平和形态。这使我们能够将基因型和表型之间的巨大差距分为两部分：第一部分是从分子到树突尖端，第二部分是从树突尖端到形态学。第二部分将使用模型来桥接。为实现我们的总体目标，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尖端动力学的动力学规则成像通过分支和回缩以及过渡来测量尖端的诞生和死亡不同速度状态之间的速率。 (ii) 我们将开发多尺度数学模型将目标 1 中获得的数据作为输入并预测形态，并将其进行比较到真正的树突乔木。 (iii) 我们将从基因上扰乱细胞骨架蛋白并使用模型测试对尖端动力学的影响是否可以解释树突结构的改变。这预期结果是对形态表型如何产生的机械理解分子过程发生在树突尖端水平。这些结果将对通过将基因型与表型联系起来并提供对病理生理学的深入了解影响神经元结构的遗传性疾病。