Lane Fellows Set To Tackle Computational Biology Problems of Today and Tomorrow

From left: Le Song, Bob Murphy, Stephanie Lane, Ray Lane, Arvind Rao and Peter Huggins.

During Homecoming Weekend, alumnus, Board of Trustees member and Campaign Chairman Ray Lane returned to Carnegie Mellon to kick off Carnegie Mellon’s campaign and to welcome the first Ray and Stephanie Lane Fellows in Computational Biology. The inaugural trio of fellows — Peter Huggins, Arvind Rao and Le Song — joined the Ray and Stephanie Lane Center for Computational Biology this summer to pursue postdoctoral research in computational biology.

“These three outstanding Lane Fellows were awarded the fellowship in recognition of their outstanding intellect and dedication to a career at the interface of computational and biological sciences,” said Robert Murphy, the Ray and Stephanie Lane Professor of Computational Biology and Director of the Lane Center for Computational Biology. “At the Lane Center, we’ll provide them with deep foundations in both disciplines so that they will not only solve current problems but also frame and solve new problems in the growing field of computational biology.”

Computational biologists investigate the ways computers can be used to solve biological problems. Because of their complexity, biological systems, such as cells, viruses and bacteria, can’t be understood through traditional, linear approaches. Researchers in the Lane Center are working to expand the understanding of complex biological systems using machine-learning methods in which computers improve automatically from experience, or “learn.” By combining experimental methods with powerful computer models, Lane Center researchers aim to not only acquire deep biological knowledge but also to develop tools for individualized diagnosis and treatment of disease.

“I was drawn to the Lane Center and to Carnegie Mellon because I wanted my work to be practical and useful,” said Huggins, who earned his Ph.D. in mathematics at the University of California, Berkeley. “At the Lane Center, people are developing practical approaches to solve real world problems. Being here is everything I’d hoped for.”

Huggins is currently involved with projects related to cancer and HIV. He’s particularly interested in combining large amounts of data about protein abundance in cancer cells, and applying machine-learning techniques to improve classical statistical tests for analyzing protein abundance patterns. He’s also analyzing HIV’s genetic sequence, which is no easy task since HIV mutates so rapidly. Mutations influence the virus’s resistance to drugs, but some features of HIV’s protein sequences cannot be changed much without harming the virus. Huggins is looking for patterns in what doesn’t change in HIV, assuming that what doesn’t change is critical to survival.  He hopes this research could help reveal new weaknesses in the virus, which could be targeted by drugs.

Like Huggins, Song’s goal is to bring modern machine-learning tools into biology and generate real impact in the biology community. Song, who earned a Ph.D. from the School of Information Technologies at The University of Sydney, is using computational methods to reconstruct gene regulatory networks (GRNs). GRNs are groups of molecules that dictate which genes get “turned on” and which get “turned off” during critical cell processes, including the development of embryos and the formation of cancer. The interaction among the hundreds of molecules in a GRN is very dynamic, so computational methods are needed to understand the evolution of these networks and to predict the structures of other GRNs.

Rao's research interests lie at the intersection of machine-learning and experimental and computational systems biology. As a graduate student in the Electrical Engineering and Bioinformatics graduate programs at the University of Michigan, Rao worked on understanding how cells regulate the copying of DNA into RNA, a process known as transcription. At Carnegie Mellon, he is interested in investigating how RNA is translated into protein. Specifically, he's focusing his efforts on the "unfolded protein response," a mechanism in cells that monitors the folding status of proteins. Misfolded proteins can lead to diseases such as Alzheimer's disease, Parkinson's disease, cancer and type II diabetes. Rao hopes to “bring something to experimental biologists that they didn't think possible,” he says, by approaching the “unfolded protein response” mechanism using a variety of computational techniques.

For more information about the Lane Fellows Program, please visit  The Ray and Stephanie Lane Center for Computational Biology website.

December 10, 2008
Amy Pavlak

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