Modeling intracellular transport and traffic jam in 3D neurons using PDE-constrained optimization

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 three-dimensional complex geometries of neurons. The transport is controlled to avoid traffic jams of materials by minimizing a predefined 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 the isogeometric analysis. 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 the 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 neurons.

Modeling neuron growth using isogeometric collocation based phase field method

“…To improve model efficiency, we aim to carry out local refinement using truncated hierarchical B-splines to reduce computational cost while preserving geometry smoothness 46 . We plan to simulate material transport 47 and study traffic jam 48 , 49 during neuron growth in order to better model and understand neurodegenerative diseases. We also plan to investigate efficient prediction of neuron growth using data-driven approaches 50 , 51 .…”