Could mathematics help uncover new ways to fight Alzheimer’s disease? A researcher at Mississippi State University is using mathematical models to investigate how common metals influence the buildup of proteins associated with Alzheimer’s and whether potential treatments could interfere with this process.
Shantia Yarahmadian, an associate professor in Mississippi State’s Department of Mathematics and Statistics, is studying how metals such as copper and zinc may affect the behavior of amyloid-beta, a protein that can clump together and form plaques linked to Alzheimer’s disease.
His research, published in the journal Bulletin of Mathematical Biology, builds on his ongoing efforts to use mathematics to better understand the complex biological processes involved in Alzheimer’s.
Using Mathematics To Understand Alzheimer’s Disease
Mathematical modeling allows scientists to explore complicated biological interactions by representing them through equations and simulations. Researchers can then test how different factors influence a process and predict what might happen when certain conditions change.
“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.”
This approach could be particularly valuable for Alzheimer’s disease, which involves numerous interacting biological processes that are difficult to study individually.
How Copper and Zinc May Influence Alzheimer’s Plaques
To investigate how Alzheimer’s-related plaques develop, Yarahmadian created a mathematical framework that simulates the chemical reactions involved when metals such as copper and zinc interact with amyloid-beta.
Amyloid-beta proteins can stick together in a process known as aggregation. Over time, these clusters can contribute to the formation of plaques, one of the characteristic features of Alzheimer’s disease.
The model examines how metal ions, which are electrically charged forms of metals, may influence this protein buildup. It also enables researchers to explore two potential therapeutic approaches designed to interfere with aggregation.
Rather than relying entirely on laboratory experiments, scientists can use the simulations to investigate how changes in these interactions might affect plaque formation and identify promising directions for further research.
Mathematical Predictions Match Laboratory Results
A mathematical model is most useful when its predictions reflect what happens in actual experiments. To evaluate his framework, Yarahmadian and his collaborators compared its results with laboratory measurements obtained through atomic force microscopy.
This imaging technique uses an extremely fine probe to examine structures at the microscopic scale, allowing researchers to study tiny clusters of proteins and other materials.
The mathematical model successfully reproduced the patterns observed in the experiments. This agreement suggests that the framework captures important aspects of how amyloid-beta proteins aggregate under the conditions studied.
The results provide evidence that mathematical simulations can help researchers investigate the mechanisms behind protein clumping and explore ways to influence the process. Although the findings do not establish an effective Alzheimer’s treatment, they offer a promising tool for guiding future laboratory studies.
For Yarahmadian, the motivation behind the research extends beyond the mathematical challenge. He hopes that a better understanding of the disease’s complex biology will eventually help scientists develop more effective strategies against it.
“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.”
