Abstract

The intracellular transport process plays an important role in delivering essential materials throughout branched geometries of neurons for their survival and function. Many neurodegenerative diseases have been associated with the disruption of transport. Therefore, it is essential to study how neurons control the transport process to localize materials to necessary locations. Here, we develop a novel optimization model to simulate the traffic regulation mechanism of material transport in complex geometries of neurons. The transport is controlled to avoid traffic jam of materials by minimizing a pre-defined objective function. The optimization subjects to a set of partial differential equation (PDE) constraints that describe the material transport process based on a macroscopic molecular-motor-assisted transport model of intracellular particles. The proposed PDE-constrained optimization model is solved in complex tree structures by using isogeometric analysis (IGA). Different simulation parameters are used to introduce traffic jams and study how neurons handle the transport issue. Specifically, we successfully model and explain the traffic jam caused by reduced number of microtubules (MTs) and MT swirls. In summary, our model effectively simulates the material transport process in healthy neurons and also explains the formation of a traffic jam in abnormal neurons. Our results demonstrate that both geometry and MT structure play important roles in achieving an optimal transport process in neuron.

Details

Title
Modeling material transport regulation and traffic jam in neurons using PDE-constrained optimization
Author
Li, Angran 1 ; Zhang, Yongjie Jessica 2 

 Carnegie Mellon University, Department of Mechanical Engineering, Pittsburgh, USA (GRID:grid.147455.6) (ISNI:0000 0001 2097 0344) 
 Carnegie Mellon University, Department of Mechanical Engineering, Pittsburgh, USA (GRID:grid.147455.6) (ISNI:0000 0001 2097 0344); Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, USA (GRID:grid.147455.6) (ISNI:0000 0001 2097 0344) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2637832766
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.