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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $295,999

## Abstract

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.

## Key facts

- **NIH application ID:** 10568586
- **Project number:** 1R01GM148765-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Johannes Schoeneberg
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $295,999
- **Award type:** 1
- **Project period:** 2023-09-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10568586

## Citation

> US National Institutes of Health, RePORTER application 10568586, Modeling and Analysis of the Spatio-Temporal Dynamics of the Mitochondrial Network (1R01GM148765-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10568586. Licensed CC0.

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