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

项目总结 树枝状结构：从分子到形态的数据驱动模型 树枝的高度分枝结构使非凡的连通性和 神经系统的信息处理能力。改变的树枝状形态通常是 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树突状细胞生长和结构的基础成分， 我们仍然不知道分子相互作用是如何产生树枝的，这是 比组成分子大几千到几百万倍。 此应用程序的总体目标是开发数据驱动模型，以预测、 果蝇第IV类DA神经元的树突生长。这些单元格被选中 因为它们的树突可以以出色的空间和时间分辨率成像，并且 苍蝇中的基因工具促进了分子操作。我们的中心假设是不断增长的 而枝晶尖端的收缩构成了分子之间的中间组织水平 和形态。这使得我们能够将基因型和表型之间的巨大差距划分为 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。 为了达到我们的总体目标，我们将追求以下三个具体目标： 利用高分辨率活细胞研究树突尖端的动力学规律 通过分支和回缩以及过渡来测量尖端的出生和死亡的成像 不同速度状态之间的速率。(Ii)我们将发展多尺度数学模型，以 将目标1中获得的数据作为输入，并预测形态，然后进行比较 真正的树枝乔木。(Iii)我们将对细胞骨架蛋白进行基因扰动，并使用这些模型 以测试对尖端动力学的影响是否解释了树枝晶结构的变化。这个 预期的结果是机械地理解形态表型是如何产生的 发生在枝晶尖端水平上的分子过程。这些结果将对 通过在基因型和表型之间架起桥梁，并通过提供对疾病病理生理学的洞察 影响神经元结构的遗传性疾病。

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.