“Turning Off ” Cancer Genes
Chris Wiggins | Applied Physics and Applied Mathematics
The key to unlocking complex problems like the biological cause of cancer—the second-leading
cause of all deaths—may lay in the fundamental building blocks of life.
How genes control each other—and how to predict that activity—is a research focus of Chris Wiggins, associate professor in the Department of Applied Physics and Applied Mathematics. He is working to develop models that predict how genes behave to explain how some cells become cancerous.
“The relationship between biology and mathematics has completely changed in the last decade,” Wiggins explains. “New technologies have transformed biology into a data-rich science, and advances in algorithms have made possible data-driven predictive modeling in biology. At the same time, the World Wide Web made it possible for any biologists to share their data with the entire mathematical community with the click of a mouse.”
Wiggins and his collaborators have shown how one can use these data, along with the appropriate math, to learn which genes are controlling which other genes and why. “The problem is a bit like watching stocks go up and down, and trying to predict which stocks are driving each other,” he says.
While the architecture of the underlying genetic network is a basic biological topic, Wiggins says “it is at the root of numerous biological diseases, including cancer, and we are now on the threshold of fi nding more of those genetic links.”
Wiggins, who earned his PhD in theoretical physics from Princeton and was an NSF postdoctoral research fellow in biomathematics at the Courant Institute, has had his work profiled in Scientific American.
