A cell-level model to predict the spatiotemporal dynamics of neurodegenerative disease

TL;DR

This paper introduces a cell-level physical model that explains the progression of neurodegenerative diseases, highlighting a critical switch from spontaneous pathology to propagation-driven dynamics, aiding in therapy strategy prediction.

Contribution

The paper develops a novel bottom-up physical model linking cellular mechanisms to tissue pathology, revealing a critical transition point in disease progression.

Findings

Model explains slow development and rapid acceleration of pathology.

Identifies a critical switch point in disease dynamics.

Provides a framework for predicting effective therapeutic strategies.

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 here develop 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. This framework provides a robust physical foundation for interpreting pathological data and offers a method to predict which class of therapeutic strategies is best matched to the underlying drivers of a specific disease.