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

线粒体网络时空动力学的建模与分析

• 批准号: 10568586
• 负责人: Johannes Schoeneberg
• 金额: $ 29.6万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10568586
• 关键词: 3-Dimensional; Acceleration; Adoption; Algorithms; Alzheimer' s Disease; Automobile Driving; Biogenesis; Biology; Biophysics; Cardiomyopathies; Cell Volumes; Cells; Cellular Metabolic Process; Cellular biology; Communities; Computer software; Data; Data Analyses; Data Set; Defect; Development; Diffusion; Disease; Equilibrium; Event; Evolution; Fluorescence; Fluorescence Microscopy; Four-dimensional; Goals; Grant; Graph; Heart; Heart Diseases; Homeostasis; Image; Image Analysis; Imaging technology; Impairment; Kidney Bean; Knowledge; Lead; Light; Link; Location; Malignant Neoplasms; Measures; Methodology; Methods; Microscopy; Mission; Mitochondria; Modeling; Modernization; Morphology; Nerve Degeneration; Neurosciences; Organelles; Polymers; Process; Public Health; Reading; Research; Research Personnel; Resolution; Role; Seizures; Shapes; Stress; Stroke; Testing; Textbooks; Time; United States National Institutes of Health; Vertebral column; Work; cell motility; computerized tools; data modeling; deep learning; deep neural network; experimental analysis; fluorescence imaging; human disease; image processing; microscopic imaging; models and simulation; neural network architecture; novel therapeutics; particle; segmentation algorithm; simulation; software development; spatiotemporal; terabyte; tool

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.

项目摘要/摘要 线粒体提供我们90％的能量；线粒体缺陷导致多种疾病 癫痫发作，中风，心脏病，神经变性和癌症。远离他们的静态肾豆形 在许多教科书中的描述中，线粒体形成一个动态的三维网络，跨越整个 细胞的体积。该网络通过裂变和融合，运动，生物发生进行连续重塑 和清除。在压力或疾病条件下，线粒体网络碎片并改变 动态平衡。了解这种平衡及其对疾病的变化和调整是 定量细胞细胞器生物学中的原型问题。到目前为止，动态线粒体网络具有 逃避的实验询问和建模作为线粒体太小，太快了，无法容纳 荧光显微镜。幸运的是，成像技术的最新进展，即晶格灯页 显微镜（LLSM）已将其改变。本应用程序中的大量初步数据支持工作 假设定量LLSM图像处理和基于粒子的空间建模的组合可以 成功创建了线粒体网络的第一个四维（4D）时空模型。目标 拟议的工作是阐明线粒体网络的基本生物物理原理 稳态。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。 AIM 1将检验以下假设，即深度学习的线粒体分割将显示更多 与传统方法相比，从LLSM数据中准确提取4D线粒体网络。新的 将开发深层神经网络体系结构来检验这一假设。预计工具将是 交付的，可以跨越各种成像条件以及各种线粒体形式和功能 条件受损。 AIM 2将检验以下假设：基于图的拓扑链接将证明第一个时间跟踪 4D线粒体网络。新的线性分配基于问题的算法将被开发为精确 跟踪线粒体网络骨干及其裂变/融合事件。预计工具将是 可以在各种成像条件和线粒体形式和线粒体形式和 功能受损条件。 AIM 3将测试线粒体网络的形态，动力学和功能的假设 并且可以预测。将基于4D图开发一个新的基于粒子的聚合物仿真模型 实验数据的时间分析。预计线粒体的第一个4D时空模型 将开发可以预测形式和功能可观察到的网络及其从第一 原则。