SFU mathematician finding new insights on evolution through the lens of infectious disease

November 18, 2022

Evolution has produced a wealth of biological diversity in the natural world. Underneath that diversity, Ailene MacPherson sees a complex web of interactions that can be described with mathematical models.

In her role as Assistant Professor at SFU Mathematics and Canada Research Chair of Theoretical Evolutionary Epidemiology, she is working to develop population genetic models to better understand how infectious diseases interact with their hosts.

Her recent research evaluates multiple models used to track the evolution of species and finds that all biological diversity can be described with the same mathematical models. Unifying these frameworks can help us understand past extinctions and earth’s biodiversity; and can inform conservation and public health policies to slow the spread of infectious diseases.

“Mathematics is foundational to our understanding of evolutionary biology,” MacPherson explains. “Theoretical models help us synthesize the genetic, evolutionary, and ecological dynamics that create and maintain biodiversity.”

She first realized mathematics’ potential to describe the natural world during her undergraduate studies. While examining how lizards at White Sands National Monument had evolved a white colouration to avoid predators, she developed a population genetic model of lizard evolution. “I was hooked,” she says.

While at SFU, MacPherson has turned her attention toward modelling the complex interactions between infectious diseases and their hosts. “Infectious diseases are important biologically but also have the potential to be a rich source of knowledge about evolutionary biology as a whole. To do so, however, we must develop a whole new theory and set of methods, re-evaluating long-standing assumptions and constructing new hypotheses.”

One of these assumptions is the Red Queen hypothesis, which maintains that species must continually evolve and proliferate to compete with coexisting species in order to avoid extinction.

“During my PhD, I developed a mathematical model showing that despite its widespread acceptance, the Red Queen hypothesis is not, in fact, true,” MacPherson says. “This has widespread implications for our understanding of the natural world and the role of species interactions in that world.”

In her current research program she is working to develop methods for robustly estimating infectious disease dynamics from viral genomic data, and develop statistical inference methods to examine coevolution between hosts and infectious pathogens. Better modeling of these dynamics will help to inform new strategies for public health and conservation.

“I love how the lines between math and biology blur,” she says. “When I am working on math I am imagining biology and when I read about biology I visualize it as a mathematical model. When I can no longer tell the two apart is when I am happiest.”