Date of Graduation

7-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Mechanical Engineering

Advisor/Mentor

Arun K. Nair

Committee Member

Min Zou

Second Committee Member

Shannon L. Servoss

Third Committee Member

Paul Millett

Fourth Committee Member

Uche Wejinya

Keywords

Extracellular Matrix, Multiscale Modeling, Neurite Outgrowth, Neuronal Cell, Non-linear FEA, Peptoid

Abstract

The focus of regeneration therapy for traumatic brain injuries and Alzheimer's disease is on the promotion and growth of neuronal cells. In vitro research attempts to improve this by modifying the stiffness and topography of the extracellular matrix (ECM). However, the limitations of in vitro experiments make it difficult to control the individual factors influencing neuronal cell growth. A computational model can be used to decouple individual factors and study them individually to gain a better understanding of the mechanics between the neuronal cell and ECM, which will aid in the design of in vitro experimental studies.

This study develops a multi-scale method to analyze the effect of ECM stiffness and topography on neuronal cell deformation behavior by coupling neuronal cell receptor atomistic behavior to neuronal cell interactions with the ECM at the microscale. Non-linear finite element analysis is utilized to model the interaction between neuronal cell and ECM, and molecular dynamics approach is utilized to predict the atomistic behavior of integrin and neural cellular adhesion molecule (NCAM). Results show that by increasing the ECM stiffness, the neuronal cell transfers higher stress to the ECM, known as mechanosensing. The mechanosensing, however, approaches saturation at a threshold ECM stiffness and is impacted by ECM thickness, topography, integrin and NCAM clustering. Furthermore, a cylindrical ECM asperity results in asymmetric stress and deformation within the neuronal cell.

Next, we investigate peptoid as an ECM ligand, as it is gaining popularity due to its biocompatibility, self-assembling structure, and neurodegenerative therapeutic potential, but the peptoid interaction with neuronal cell receptors for mechanosensing need to be quantified. We use molecular dynamics method in this research to study the adhesion force and interaction between peptoid and neuronal cell receptors, as well as the influence of peptoid bundles. The results reveal that peptoid bundles have a higher affinity for neuronal cell receptors, which also increases with the size of the peptoid bundles; this increased adhesion force is due to the hydrophobic residue clustering area in the binding region. Additionally, this study demonstrates that peptoid adherence is comparable to that of the positive control ECM ligand, Type I collagen.

Another challenge for in vitro research is improving neurite outgrowth (extensibility) and directionality, which would encourage and mature the neuronal cell into a functional neuron. In this research, we develop a computational model to predict neurite extension as a function of ECM stiffness and topography. Internal forces, growth cone dynamics, and receptor dynamics are the key factors controlling neurite extension, and all these factors have been considered in this model. The results indicate that by increasing the stiffness and width of patterned topography increases neurite extension, although the extent of the increase varies according to the growth cone and receptor’s dynamic behavior.

This study establishes a computational model for analyzing the relationship between neuronal cell deformation and neurite outgrowth and the stiffness and topography of the ECM. Further, the model predicts how enhanced neuronal cell growth can be accomplished by selecting an ECM stiffness and patterned topographical properties.

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