David E. Shaw's Supercomputer Is Uncovering Secrets of Human Biology

David E. Shaw, known for innovations in finance, has turned his focus to engineering better ways to study molecular biology.
David E. Shaw, known for innovations in computational finance, has turned his focus to engineering better ways to study molecular biology.
—Photo by Timothy Lee Photographers

David E. Shaw, the legendary quantitative investment manager turned innovator in computational biochemistry, spoke to a packed auditorium of Columbia Engineering students and described his research and development of high-speed simulations that could lead to better understanding of protein behavior and improvements in drug discovery for diseases like cancer and Alzheimer’s.

Shaw was featured as part of the School’s “Engineering Icons” series, which brings leading experts who are having a major impact in different fields of engineering and the applied sciences to campus.

Dean Mary C. Boyce introduced Shaw, referencing his time as a professor in Columbia’s Computer Science department in the 1980s before he became a forerunner in computational finance through his quantitative investment firm, D.E. Shaw Group. Since 2001, Shaw’s primary focus has been on efforts in molecular biology through D.E. Shaw Research (DESRES), where he is chief scientist.

The evening began as a Q&A between Dean Boyce and Shaw, who also holds an appointment as senior research fellow at the Center for Computational Biology and Bioinformatics at Columbia University. Shaw showed several videos of high-speed molecular dynamics simulations performed by Anton, the specialized supercomputer built by his research group.

Shaw noted that computer science has been a common thread guiding his multi-faceted career. A former teacher and mentor once counseled him to “stop trying to think of big ideas” and focus on the close study of a concrete application, since this often reveals fundamental problems whose solution leads naturally to new, big ideas.

“What’s great about discovery is that sometimes big, juicy theoretical discoveries grow out of practical work on specific applications,” Shaw said.

David E. Shaw was mobbed with questions from students after his Engineering Icons talk.
David E. Shaw answers students' questions after his Engineering Icons talk.
—Photo by Timothy Lee Photographers

His initial foray into molecular simulations was inspired by the observation of a friend that important insights could be learned if such simulations were fast enough that researchers could watch molecules move around for periods far longer than what was then possible. Previously, such motions could only be viewed for less than 10 microseconds. Anton operates orders of magnitude faster than other computational simulators and allows scientists to view molecular activity for much longer, even up to 10 milliseconds. Such advantages allow researchers to study not just the structure of proteins, but their dynamics as well, along with interactions between proteins and drugs being delivered to the body.

Protein folding, in particular, has been a rich area of study that had been difficult to explore due to the inability of laboratory experiments to reveal the continuous structural changes in a protein over time. Anton’s ability to show protein folding is helping to uncover what is involved in a number of neurodegenerative diseases such as Alzheimer’s and Parkinson’s where the protein is actually “misfolding.”

In one video, showing a simulation of drug delivery, the color-coded drug searched around a cloudlike, squishy protein to find the optimal spot for binding.

Students asked Shaw about the applications for his research, the process of trial and error, and the likelihood of successful drug development. Though Shaw believes the research is promising, he cautioned that it should not be viewed as a “magic thing for developing drugs.”

Commenting on scientific advances in general, he said, “Pursuing major scientific advances is high-risk, high-return work. If you want to make a big impact, unfortunately, 99 percent of [the work] will fail, and 1 percent will succeed.”

One component for success that Shaw highlighted was collaboration. He noted that bringing disparate methodologies and disciplines together often brought better results. “That kind of interplay is so important,” Shaw said.

His own team draws from computational chemists and biologists, computer scientists and applied mathematicians, and computer architects and engineers. The group also engages in collaborations outside the team. In addition, DESRES has made an Anton machine available without cost to academic researchers, and 150 research groups around the country have used this machine for their own projects.

—by Allison Elliott

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