Mathematics may offer a new way to investigate the complex chemistry behind Alzheimer’s-related plaque formation.
Plaques associated with Alzheimer’s disease begin with molecular interactions that are difficult to observe directly as they unfold. Shantia Yarahmadian, an associate professor at Mississippi State University, is using mathematics to simulate part of that process, focusing on how metals such as copper and zinc may influence the behavior of amyloid-beta proteins.
His latest model follows a chain of reactions that can lead amyloid-beta to aggregate, or clump together, and form plaque. It also allows researchers to test how two potential therapeutic approaches might interfere with those reactions. The work was published in Bulletin of Mathematical Biology and extends Yarahmadian’s earlier efforts to model Alzheimer’s disease mathematically.
Turning Molecular Reactions Into Equations
“Every biological phenomenon occurs in the physical world—in space and time—and involves changes in shape, quantity and matter,” said Yarahmadian, a faculty member in MSU’s Department of Mathematics and Statistics. “Because of its abstract power, mathematics allows us to uncover patterns, test hypotheses and make predictions that may not be possible through observation alone. Mathematics does not replace laboratory or clinical research; it complements it by helping us understand the larger system, identify the most influential mechanisms and guide future experiments.”
In the new framework, the model represents how metal ions may affect amyloid-beta aggregation over time. Copper and zinc can interact with amyloid-beta, and the model is designed to examine how those interactions may alter the sequence of events that produces larger protein aggregates.
The same system can simulate two possible ways of disrupting that process. One involves chelation, in which molecules bind metal ions and reduce their availability for other chemical interactions. The other examines inhibitory therapies intended to interfere with aggregation.
Testing the Model Against Laboratory Data
A mathematical model is useful only if its predictions correspond with measurable behavior. Yarahmadian and his collaborators therefore compared the model’s output with experimental observations made using atomic force microscopy, a technique capable of examining microscopic protein aggregates.
The simulated patterns matched those observed in the laboratory. That agreement indicates that the model can reproduce key features of the aggregation process under the conditions studied, giving researchers a framework for testing how changes in metal interactions or potential therapies might alter the system.
“What drew me to Alzheimer’s research is the combination of its profound human impact and its extraordinary biological complexity,” he said. “My goal is to use mathematical modeling to identify important mechanisms and generate insights that may help guide future experimental and therapeutic research.”
Reference: “Metal-Ion-Mediated Amyloid-B Aggregation in Alzheimer’s Disease: A Mathematical Model of Chelation and Inhibitory Therapies” by Shantia Yarahmadian, Yasser Alzahrani and Vaghawan Prasad Ojha, 20 August 2026, Bulletin of Mathematical Biology.
DOI: 10.1007/s11538-026-01732-1
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