Chad Myers, McKnight Land-Grant Professor
Computer science and engineering faculty member Chad Myers, center, works with graduate students Raamesh Deshpande, left, and Elizabeth Koch, right, and postdoctoral researcher Jeremy Bellay, foreground, on analyzing the yeast genetic-interaction network.
As cells go, yeast is modest. You can see it under a microscope. It plays a role in many old and familiar processes, from making bread to brewing beer, turning sugar into ethanol.
The genome for baker’s yeast was one of the first to be sequenced. With about 6,000 genes compared to about 20,000 in humans, yeast is more manageable to study, yet many of its genes are conserved in the human genome. That means it can serve as a model for many of the core processes that support a human cell. Simple as it seems, a yeast cell today yields a mind-boggling amount of data with enormous implications.
Too much information is not a problem for Chad Myers of the College of Science & Engineering, who brings the power of computer science to this frontier. Working in collaboration with Charles Boone and Brenda Andrews and their research teams at the University of Toronto, Myers helped to complete the first global genetic-interaction network for any organism—a yeast.
Most common diseases result not from a single gene’s malfunction but from interactions among several genes, or between genes and the environment. The sheer number of genes has made identifying the patterns of interactions impossibly remote. Using yeast as a simpler model for genetic interactions, Myers’ collaborators reverse-engineered several million different strains by inserting gene mutations in specific combinations and documented what happened. Then Myers and his research team used the volumes of data to map how pairs of genes interact.
As a specialist in systems biology, Myers is at the forefront of a generation of young scientists finding out where computational methods are relevant to biological questions. This year he was named a McKnight Land-Grant Professor because of his role in developing computational approaches to tackle challenges related to genetics and disease.
“Team” is a key word for Myers.
“The most interesting science today is being done in teams,” says Myers. “This kind of interdisciplinary work requires a broad set of skills. I’m really a bridge between people working in computer science and biology.”
Biology was not Myers’ first interest. As a high school student in North Dakota, he liked math and physics. In college, those strengths expanded into computer engineering.
Then in graduate school at Princeton, he was exposed to the quickly growing fields of genomics and systems biology. Until that point, says Myers, he thought biology was mainly about memorizing facts that had mostly been worked out. Suddenly, he realized how much uncharted territory was out there.
“It was a real flip—all because I saw how much in biology we still don’t know,” he says, “and because I realized my skills were relevant and could have an impact.”
Myers delved in. Today, the challenges of biology are one of his primary motivators.
In a fifth-floor office of Keller Hall, teams of students and postdoctoral staff move in and out of Myers’ office, clustering around computers, writing code and discussing results. They mine genomic data sets for insight into biological questions and build tools for translating that data into visual models—like the colorful diagram on the wall that shows genetic interactions in a yeast cell.
Some of Myers’ projects focus on accurately measuring the way genes interact. Another group of his projects aims to identify basic organizational principles of the genetic-interaction networks, regardless of species. A third group explores whether genetic interactions mapped in model organisms like yeasts can be used to understand and treat human diseases.
In the case of a disease like cancer, for example, identifying gene pairs that back up each other allow highly targeted treatments to kill tumor cells while preserving normal cells. Myers foresees a day when treatments can be personalized for specific individuals based on their genome sequence.
To accomplish his work, Myers has important assets. The Minnesota Supercomputing Institute across Northrop Mall can run an analysis in a day that would take several weeks or even months to run on one of the computers in his lab. The University of Minnesota’s Rochester campus, with Mayo Clinic and IBM partnerships and a graduate program in biomedical informatics and computational biology, has helped Myers form important collaborations.
The most important assets are students and postdocs, and Myers has proven to be a magnet for top talent as well as a skilled teacher and mentor. His lab includes seven graduate students and a postdoctoral researcher. Several undergraduates have worked on various projects, too, including Jacob Inda, whom Myers met while teaching an introductory class on computing and biology. Last week, Inda won a Barry Goldwater Scholarship to support work toward a research career in molecular biology.
The two-year McKnight award will allow Myers to expand his team with student and postdoctoral talent. It will also provide him with a critical period in the second year to focus on his research.
“I love to teach—that’s why I ended up in a university setting,” says Myers, “but a leave from teaching to focus on my research will be really important.”
Keeping up with the rapid pace of discovery in the field is no small feat. As an example, Myers points out that it took several teams to finish sequencing the human genome only a decade ago, with public support close to $3 billion. Yeast, E. coli, worm, and fly genomes were among the earliest to be mapped. Today, we have sequences for about a thousand different organisms, and the cost of sequencing has dropped to as little as several thousand dollars for small genomes.
Myers also recently won a National Science Foundation CAREER Award, which includes working with high school science and math teachers in the summer. His goal is to send them back into their classrooms with new energy and tools that will attract strong math students to the exciting work and critical challenges in biology.
“It’s an exciting time,” says Myers. “We need more people who are good at math going into biology.”
Chad Myers, computer science and engineering: Learning about the cell by breaking it—computational approaches for understanding complex genetic networks