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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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目总结/文摘