A dozen or so years ago, for a change of pace, Jim Simons invited his friend, biologist Michael Wigler to give a lecture about big unsolved problems in biology to the mathematicians and statisticians at Renaissance Technologies, the hedge fund management company Simons founded in 1982. Yet as Wigler spoke on topics from genome sequence analysis to cellular signaling, Simons realized that these subjects weren’t in fact such a departure from the kinds of problems tackled daily at Renaissance Technologies.
“I remember thinking, ‘If the guys in this building dropped everything and worked on these problems, they’d make great progress,’” Simons recounts. The questions Wigler was trying to answer “just called out for” the kinds of methods that were the natural currency at Renaissance, he says.
Simons’ epiphany, along with similar ones by other scientists around that time, heralded the dawn of a new field of biology known as quantitative biology, or systems biology, depending on whom you ask. As researchers have begun to explore what quantitative methods have to offer biology, Simons is keeping alive his early vision of the field’s promise: over the past seven years, the Simons Foundation has invested heavily in quantitative biology research, creating two centers devoted to its study and fostering ambitious collaborations between top research institutions in biology and mathematics.
Biology has had a quantitative side for many decades. But in the last 10 to 15 years, quantitative methods have begun migrating to center stage as advances in areas such as imaging and high-throughput genetic sequencing have produced a deluge of data, of an order of magnitude previously unheard of by biologists.
“There’s so much data coming in now that we don’t even have the ability to store it,” says Arnold Levine, director of the Simons Center for Systems Biology at the Institute for Advanced Study in Princeton. “Analyzing the kind of data we’ve been getting in the last decade requires a very different way of doing science than in the previous 40 years, when you might discover one gene at a time, or one protein.”
The exponential upsurge in available data permits a new model—and level—of inquiry, shifting biology from a primarily descriptive science to a more analytical one. Just as condensed matter physicists figured out in the 19th century how to segue from an understanding of the individual interactions of water molecules to a description of the large-scale flow of water, systems biologists face the challenge of translating massive amounts of data at the level of molecules or genes into an understanding of complex behavior on the scale of cells, individuals or even populations.
“We can now describe at the molecular level, for example, how two neurons in the brain interact through a synapse, but that’s still several layers below the layer of thinking, memory and love,” says Stanislas Leibler, a professor at the Institute for Advanced Study. “The ultimate goal of systems biology is to find a language for these kinds of collective phenomena.”
The foundation’s support of systems biology research began formally in 2005 with the creation of the Simons Center for Systems Biology at the Institute for Advanced Study (IAS), legendary for its strength in mathematics and physics. Levine had joined the faculty of IAS a few years earlier as its first-ever experimental biologist, with a mandate from IAS and the Simons Foundation to create a systems biology center. Before Levine’s arrival, IAS mathematicians had considered some theoretical biology questions, but without an experimental biologist to guide them, “they didn’t get much traction,” Simons says.
Arriving at IAS, Levine found a cadre of mathematicians, physicists and computer scientists eager to sink their teeth into substantial biological problems. Together the Center’s researchers have since tackled a wide range of topics, from the genetics of the HIV and H1N1 viruses to the metabolic pathways of the Masai, an ethnic group in Kenya and Tanzania. And while IAS is a very small place—its entire permanent faculty typically numbers about 25 people—a constant influx of visitors and long-term members has enabled the systems biology center to extend its reach far beyond the Institute’s walls. “We’ve trained maybe 30 people here over the last decade who are now at universities and biotech companies across the country,” Levine says.
In 2008 the Simons Foundation expanded its quantitative biology commitments to include the establishment of a partnership between IAS and The Rockefeller University in New York City, and the creation of the Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory, in Cold Spring Harbor, New York.
IAS and Rockefeller offer complementary strengths: IAS has an almost unparalleled history in mathematics and theoretical physics, while Rockefeller, the country’s first institution devoted entirely to biomedical research, places its focus squarely on experimental biology. The two institutions now share joint visiting professors and graduate and postdoctoral fellows, Thursday morning coffee sessions, and an annual symposium whose stated goal is to evoke “undisciplined conversation.”
Leibler, who holds joint appointments at Rockefeller and IAS, appreciates the opportunity to slow down and take the long view when at IAS.
“Maybe because it doesn’t have labs, the Institute is one of the rare places where you can step back a bit and think about where biology is going in the long term,” he says.
Cold Spring Harbor Laboratory, meanwhile, was already an established strength in quantitative biology when the Simons Foundation created the Simons Center there: several faculty members, including Wigler, were actively pursuing quantitative biology research. But the Center’s creation has assisted that laboratory in growing into an even greater quantitative biology powerhouse. The Center has nine faculty members as of March 2012, and plans to grow to 12 to 13 faculty, and to about 50 total individuals, including postdocs and other researchers. The goal is to build up a dynamic group trained not only in genetics and neuroscience, but also mathematics and physics.
One recent focus at the Center has been the use of machine-learning techniques to search for genomic markers that could indicate a particular cancer patient’s prognosis, and which treatments would be most effective. In recent work, for example, the researchers have been looking for markers that suggest which breast cancer patients are most likely to benefit from particular treatments. “These approaches will have a profound medical impact,” Wigler says.
While these particular studies have clear practical implications, researchers at the two Simons centers are not under particular pressure to produce work with immediate practical application. After all, observes Simons, when IAS was created in 1930, its founders didn’t tell Einstein which equations to solve. Likewise, he says, the Simons Foundation doesn’t have a specific goal in mind in supporting the two quantitative biology centers.
“We are happy to see efforts at the most basic level,” Simons says. “It’s not that we don’t expect applications, but we just don’t know what they will be yet.”