Cell-Level Model to Predict the Spatiotemporal Dynamics of Neurodegenerative Disease

Abstract

A central challenge in modeling neurodegenerative diseases is connecting cellular-level mechanisms to tissue-level pathology, in particular to determine whether pathology is driven primarily by cell-autonomous triggers or by propagation from cells that are already in a pathological, runaway aggregation state. To bridge this gap, we develop here a bottom-up physical model that explicitly incorporates these two fundamental cell-level drivers of protein aggregation dynamics. We show that our model naturally explains the characteristic long, slow development of pathology followed by a rapid acceleration, a hallmark of many neurodegenerative diseases. Furthermore, the model reveals the existence of a critical switch point at which the system's dynamics transition from being dominated by slow, spontaneous formation of diseased cells to being driven by fast propagation. We explore the different limiting behaviours of this model, derive approximate analytical solutions for several observables, and demonstrate the parallels to models used in population epidemiology. This framework offers a method to simulate disease propagation and intervention in silico and provides a robust physical foundation for interpreting pathological data.