|Jeffrey Forrester, center, and Michael Roberts, right, discuss the program that Amy Conner '10, left, will use to analyze the cancer cells she's studying.
Math might not seem like the most appropriate tool for fighting cancer. You wouldn’t expect scientists to find a cure with a calculator. But as six students recently learned in a new interdisciplinary program funded by a $230,000 National Science Foundation (NSF) grant, maybe that’s because those scientists aren’t using a big enough calculator. After all, every human cell contains roughly 25,000 genes. Want to know which of those genes define the disease? You’ll need to analyze and group them at a range of times under different conditions, leaving you with about a half-million measurements.
“Biologists need mathematics because the cell is an incredibly complex system involving the interaction of thousands of proteins and other small molecules,” says Jeff Forrester, the assistant professor of mathematics who teamed with Michael Roberts, associate professor of biology, to launch the three-year program bridging biology and mathematics. “In the future we’re going to need more math and more computational techniques to examine these systems.”
To get a head start on that future, the first six students in the program co-enrolled in Roberts’ Biology of Cancer and Forrester’s Mathematical Techniques in the Biological Sciences courses last spring. Studying the molecular and genetic makeup of cancer while also mastering the computational methods mathematicians use to analyze that makeup, they merged two disciplines that increasingly need to work together to tackle the complex genetics of disease.
“The 21st-century scientist needs to be able to bridge the gap between math, computer science and molecular biology,” says Roberts, who also attended Forrester’s class to deepen his own understanding of the math needed to truly understand cancer.
Though there are plenty of computer programs that provide scientists with computational tools powerful enough to probe the disease’s makeup, Roberts’ and Forrester’s teaching went well beyond software training.
“We could just teach them to use the software—just push the right keys and have answers come out,” Roberts explains. “But students need to learn how that analysis is being done, because there’s not just one way to analyze the data. If you don’t know how the computer came up with what it came up with, then you can’t question whether the results are valid. You just have to accept them—you’re a slave to the software.”
“In a lot of [research] labs they do the experiment and hand off the data to the mathematicians, who don’t necessarily understand the biology behind it,” says Emily Swain ’10, a biochemistry & molecular biology major participating in the program. “Then they pick one of the programs to run, give back the data and it’s like, ‘OK, there’s the answer.’ Training us to make those decisions for ourselves is a lot better, because if we understand the math behind it, we’ll know things like which algorithm is most appropriate.”
After taking the courses, meeting regularly to discuss connections between them and attending the annual meeting of the American Association for Cancer Research (AACR) in Denver this April, the students stayed on campus for eight weeks this summer for the program’s research component. In the lab, they conducted experiments using a human leukemia cell line, HL-60, to generate a genetic profile of the cancer cells.
To do that, they added chemicals to the cell cultures to induce the cancer cells to behave like normal cells. By comparing the original cells to their normal-behaving counterparts, the students identified which of those 25,000 genes the chemicals affected at various times. This enabled them to develop a genetic map of the disease that could one day help lead drug researchers to a cure.
Of course, identifying those genes was a lot more complicated than just pointing them out on a microscope slide. “You would never be able to do the analysis by hand,” explains Abby Larson ’10. “It would take you a lifetime.”
That’s where the math comes in. Meeting with students weekly throughout the summer, Forrester helped them apply the mathematical techniques to draw conclusions from those hundreds of thousands of variables. The students used cluster analysis, for instance, to identify key genes by grouping—or “clustering”—those genes that reacted similarly to the chemical inducer. And though the students used software to do that analysis, knowing the math operating behind the computer screen enabled them to wrestle with issues such as the best way to quantify changes in the genes.
“It’s best for the person who’s doing the analysis to have both a math and biology background because there’s not just one way to do the clustering,” explains Adnan Solaiman ’10. “If you have that background, you know what to expect and you can figure out the best approach.”
This fall, the students are developing their results into a paper they hope to present at the AACR’s annual meeting in Washington, D.C., next spring. Though the interdisciplinary learning experience itself has been rewarding, they’re most excited about having the chance to contribute new findings that could help in future drug development.
“When we present our research, that will really bring it all together,” says Amy Conner ’10. “It’s great to be actually doing something that will hopefully make a difference.”
Beyond helping to make that difference, the students also are laying the groundwork for a new way of understanding biology and biochemistry. As computing power increases and genetic understanding deepens, Forrester and Roberts believe that math and computer science will play an increasingly important role in the lab.
“Ultimately, the goal of this kind of work would be to simulate a living cell with a computer program,” says Forrester, who plans to collaborate with Roberts to apply for another NSF grant for a larger program linking biology, chemistry, mathematics and computer science after the current three-year program.
“Imagine if someone wanted to test a drug; we could add the drug in silico—meaning literally on the computer—and see how the cell responds virtually,” Forrester continues. “That would be the holy grail of biological modeling, and this is off in the future, but we’re preparing our students to lead us toward that future.”