Model to predict neurodegenerative brain cell protein clustering — and therapeutic efficacy

When brain cell proteins malfunction and clump together, it can lead quickly to neurodegenerative disorders, such as Alzheimer’s disease. So, cells spend considerable energy removing these protein “aggregates.” In vitro experiments and theoretical analyses in recent decades have provided good insight into physical mechanisms and aggregation rates related to these malfunctions. But their predictive capacities do not easily transfer to living systems, where active clearance mechanisms keep cells in a metastable state by removing aggregates at the same rates they are produced.

Cotton et al. developed a model for predicting in vivo protein aggregation that can provide a picture that helps to gauge both disease onset and therapeutic efficacy.

“This paper builds a simple, universal model that captures that tug-of-war and explains why a cell can sit ‘healthy’ for decades, and then tip into runaway aggregation,” said author Georg Meisl. “The central idea is to map cell behavior onto a two-dimensional phase plot to describe the cell state as a function of protein concentration, aggregate amounts, and clearance efficacy. We achieve this by turning the fundamental principles of aggregate formation and removal into a set of differential equations and use mathematical techniques to study their behavior.”

Ultimately, the model offers an easy-to-understand picture to unify various aspects of disease onset, interpret the effect of introducing different amounts of aggregates to healthy organisms, explain the importance of aging in disease emergence, and predict how different therapeutic interventions may perform.

“Our model forms the foundation for future computational tools that will simulate the disease in a human brain, to predict progression and develop new therapies,” said Meisl.

Source: “A universal phase-plane model for in vivo protein aggregation,” by Matthew W. Cotton, Alain Goriely, David Klenerman, and Georg Meisl, Journal of Chemical Physics (2026). The article can be accessed at https://doi.org/10.1063/5.0312752 .