Modeling and Analysis of the Spatio-Temporal Dynamics of the Mitochondrial Network

项目摘要/摘要 线粒体提供了我们90%的能量；线粒体缺陷会导致一系列疾病，包括 癫痫、中风、心脏病、神经变性和癌症。远离它们静止的菜豆形状 在许多教科书中的描述中，线粒体形成了一个动态的三维网络，横跨整个 单元格的体积。这个网络通过裂变和融合、能动性、生物发生经历了持续的重塑 和通行证。在应激或疾病条件下，线粒体网络断裂并改变其 动态平衡。理解这种平衡，以及它对疾病的变化和调整，是一个 数量细胞器生物学中的典型问题。到目前为止，动态线粒体网络 逃避了实验审问和建模，因为线粒体太小太快，无法进行体积测量 荧光显微镜。幸运的是，成像技术的最新进展，即晶格光片 显微镜(LLSM)改变了这一点。本申请中的大量初步数据支持这项工作 假设定量LLSM图像处理和基于粒子的空间建模的组合可以 成功创建了第一个线粒体网络的四维(4D)时空模型。目标是 拟开展的工作之一是阐明线粒体网络的基本生物物理原理。 动态平衡。我们概述了三个目标，这些目标将使我们能够缩小这一知识差距。 目标1将测试基于深度学习的线粒体分割将显示更多的假设 与传统方法相比，从LLSM数据中准确地提取4D线粒体网络。新的 将开发深度神经网络结构来验证这一假设。预计将有一款工具 可概括不同的成像条件以及不同的线粒体形态和功能 身体状况受损。 目标2将测试基于图的拓扑链接将演示第一个时间跟踪的假设 4D线粒体网络。新的基于线性分配问题的算法将被开发成精确地 跟踪线粒体网络主干及其分裂/融合事件。预计将有一款工具 可以在各种成像条件和线粒体形式下跟踪线粒体网络和 功能受损的情况。 目标3将验证线粒体网络的形态、动力学和功能相关联的假设 而且是可以预测的。提出了一种基于四维图的聚合物粒子模拟模型 实验数据的时序分析。预计第一个线粒体的4D时空模型 将开发能够预测形式和功能可观测物及其时间演化的网络 原则。

PROJECT SUMMARY/ABSTRACT Mitochondria provide 90% of our energy; defects in mitochondria lead to a wide range of diseases including seizures, stroke, heart disease, neurodegeneration, and cancer. Far from their static kidney-bean shaped depiction in many textbooks, mitochondria form a dynamic three-dimensional network that spans the entire volume of the cell. This network undergoes continuous remodeling through fission and fusion, motility, biogenesis and clearance. Under stress or disease conditions, the mitochondrial network fragments and changes its dynamic equilibrium. Understanding this equilibrium, and its changes and adjustments to disease, is an archetypical question in quantitative cellular organelle biology. The dynamic mitochondrial network has so far evaded experimental interrogation and modeling as mitochondria were too small and too fast for volumetric fluorescence microscopy. Fortunately, recent advances in imaging technology, namely lattice light-sheet microscopy (LLSM), have changed that. Substantial preliminary data in this application supports the working hypothesis that a combination of quantitative LLSM image processing, and particle based spatial modeling can succeed in creating the first four-dimensional (4D) spatiotemporal model of the mitochondrial network. The goal of the proposed work is to elucidate the fundamental biophysical principles of mitochondrial network homeostasis. We have outlined three aims that will enable us to close this knowledge gap. Aim 1 will test the hypothesis that deep learning-based mitochondria segmentation will demonstrate more accurate extraction of the 4D mitochondrial network from LLSM data as compared to traditional methods. New deep neural network architectures will be developed to test this hypothesis. It is expected that a tool will be delivered that generalizes across diverse imaging conditions and diverse mitochondrial form and function impaired conditions. Aim 2 will test the hypothesis that graph-based topological linking will demonstrate the first temporal tracking of the 4D mitochondrial network. New linear assignment problem-based algorithms will be developed to precisely track the mitochondrial network backbone as well as its fission/fusion events. It is expected that a tool will be delivered that can track the mitochondrial network in a variety of imaging conditions and mitochondrial form and function impaired conditions. Aim 3 will test the hypothesis that morphology, dynamics, and function of the mitochondrial network are linked and can be predicted. A new particle-based polymer simulation model will be developed based on 4D graph temporal analysis of experimental data. It is expected that the first 4D spatio-temporal model of the mitochondrial network will be developed that can predict form and function observables and their time evolution from first principles.