A Stochastic Compartmental Model for Fast Axonal Transport

TL;DR

This paper introduces a probabilistic micro-scale compartmental model to analyze axonal transport, linking microscopic interactions to macro-scale properties, and addressing key biological questions about axonal homogeneity, recovery, and delivery times.

Contribution

It develops a novel stochastic compartmental model based on microscopic data to study axonal transport at multiple scales, providing new insights into neuronal cargo movement.

Findings

Quantifies axonal homogeneity at stochastic equilibrium

Analyzes recovery speed after local perturbations

Reevaluates delivery times considering whole-cell dynamics

Abstract

In this paper we develop a probabilistic micro-scale compartmental model and use it to study macro-scale properties of axonal transport, the process by which intracellular cargo is moved in the axons of neurons. By directly modeling the smallest scale interactions, we can use recent microscopic experimental observations to infer all the parameters of the model. Then, using techniques from probability theory, we compute asymptotic limits of the stochastic behavior of individual motor-cargo complexes, while also characterizing both equilibrium and non-equilibrium ensemble behavior. We use these results in order to investigate three important biological questions: (1) How homogeneous are axons at stochastic equilibrium? (2) How quickly can axons return to stochastic equilibrium after large local perturbations? (3) How is our understanding of delivery time to a depleted target region changed by taking the whole cell point-of-view?