Mechanistic Modeling and Analysis of Microtubule Dynamics Supporting Neuronal Function and Regeneration

Abstract

Healthy neuronal function and neuronal regeneration are dependent on robust microtubule dynamics and organization. The microtubule cytoskeleton is comprised of dynamic, polarized filaments that facilitate transport within long range cells such as neurons through organized, polar arrays. Microtubules also undergo dynamic instability, where the plus and minus ends of the filaments switch between growth and shrinking phases, leading to frequent microtubule turnover. Although microtubules often completely disassemble and new filaments nucleate, microtubule arrays have been observed to maintain distinct biased orientations in different regions of the cell throughout the cell lifetime. Moreover, microtubule rearrangement is key to two forms of neuronal regeneration identified as adaptive responses to preserve neuronal function after injury. Motivated by experiments in neurons, we employ a continuous-time Markov chain model of microtubule growth dynamics to explore characteristics of microtubule behavior and mechanisms of microtubule organization in neurons. Using stochastic hitting-time analysis, we analyze this model to answer experimentally-intractable questions regarding \textit{in vivo} microtubule characteristics. We also develop spatially-explicit, agent-based models of microtubule arrays to investigate how experimentally hypothesized mechanisms could generate the healthy and injury response behavior of microtubules observed in neurons. We identify mechanisms which are sufficient to establish and maintain biased microtubule arrangement observed in healthy dendrites and axons. We use data-driven modeling and parameterization to connect our theoretical framework with experimental data. Further, we validate mechanisms hypothesized to induce axonal regeneration observed in both dendrites and axons. This work explores the relationship between microtubule dynamics, mechanisms of filament organization, and spatial domain interactions to understand the resilience of neuronal systems. Results of this work contribute analytic insight and mechanistic understanding of microtubule behaviors beyond what can be ascertained through biological experiments alone.