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 枝晶结构：从分子到形态的数据驱动模型 树枝状乔木的高度分支结构使其具有非凡的连通性， 神经系统的信息处理能力。改变的枝晶形态通常 与神经系统疾病有关。虽然我们知道许多分子 通过遗传学和细胞生物学研究， 我们仍然不明白分子间的相互作用是如何产生树状结构的， 比组成分子大几千到几百万倍。 该应用程序的总体目标是开发数据驱动的模型， 定量地，果蝇IV类da神经元的树突生长。这些细胞被选中 因为它们的树突可以以出色的空间和时间分辨率成像， 果蝇的遗传工具促进了分子操作。我们的核心假设是， 而收缩的枝晶尖端构成了分子之间的中间层次的组织 和形态学。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枝晶尖端，第二部分是从枝晶尖端到 形态学第二部分将使用模型进行衔接。 为达致整体目标，我们会致力达致以下三个具体目标： 使用高分辨率的活细胞技术， 成像来测量通过分支和收缩的尖端的出生和死亡，以及过渡 不同速度状态之间的速率。(ii)我们将开发多尺度数学模型， 将目标1中获得的数据作为输入，并预测形态，将进行比较 到真实的树枝状乔木。(iii)我们将从基因上扰乱细胞骨架蛋白， 以测试对尖端动力学的影响是否解释了改变的枝晶结构。的 预期的结果是机械的理解形态表型如何出现， 在枝晶尖端水平发生的分子过程。这些成果将对 通过将基因型与表型联系起来，并通过提供对 影响神经结构的遗传疾病。