Last winter, Trachette Jackson, a professor in the Department of Mathematics, could hardly bear the suspense. In December 2019, she’d learned that her research was in the running to receive a grant from the National Institutes of Health (NIH). “Waiting two months to find out if the award would be officially funded was agonizing,” she says.

But in February, while working at her office in East Hall, the email Jackson hoped to receive finally arrived: I’m extremely pleased to let you know… She was beyond words. “The type of interdisciplinary research I do can easily fall through the cracks between funding agencies,” Jackson explains. “It was nice to have this type of validation.”

The relationship between mathematics, computation, and cancer may not be immediately apparent, but Jackson has been working to build it for more than 20 years. With her colleagues at the University of Chicago and Michigan Medicine, Jackson creates mathematical models for a treatment approach known as targeted molecular therapeutics, a kind of personalized medical therapy that hones in on the forces that drive the way different kinds of cancer develop in different people.

“Mathematical modeling has been involved in studying every aspect of tumor growth for decades, but now we know so much more about molecular drivers that are specific to cancer cells and different from normal cells,” Jackson says. “We can use those molecular targets as a guide and use modeling to pinpoint, with laser focus, our therapeutic attack.”

It may seem that mathematical research—creating equations, setting their parameters, entering them into computer programs, and analyzing their outcomes in graphs—is a long way from treating patients in a hospital. But Jackson says that, at the heart of it, her work harnesses the power of math and computation to help people.

“Historically, treatment decisions have relied on clinical studies that statistically compare different options among large groups of patients who have similar pathologies, regardless of the uniqueness of disease pathways in individual patients,” says Jackson. “Mathematical modeling is playing a more and more important role in personalizing those decisions."

Numbers, Molecules, People

Jackson studied pure mathematics as an undergraduate—“Proving theorems and trying to understand abstract features of mathematical constructs, that kind of thing,” she says—and it wasn’t until late in college, when she attended a lecture on the role of mathematics in predicting cellular patterns in developmental biology, that she first learned of a connection between math and biology.

Now she spends much of her time navigating between these two worlds, translating biological data gathered from real individuals into mathematical equations that she runs as computational simulations to develop models of how tumors grow and respond to therapy. She collaborates with the medical oncologists on her team: hearing their questions and problems and trying to understand what mathematical modeling could do for their particular situation.

It takes a lot of time and effort before Jackson feels comfortable using the models as a predictive tool for medical outcomes, but her approach allows her to tease out intricacies in the biological data that would otherwise be difficult to achieve with experiments alone.

“Experimental technologies are advancing at an accelerated pace, but even still there are so many interconnected pieces working together that it is hard to disentangle the most important ones for any specific situation with an isolated experiment,” she says. “With mathematical models, we can simulate network interactions to try to extract the most important features.