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Friday, April 20, 2012
It could just be another boom, soon to be followed by another bust—no one can rule out that possibility—but these are good days for computational biologists. Even as scientists in other fields struggle to find jobs, computational biologists are being snapped up as soon as they graduate with lucrative salary offers, says Russ Altman, a professor of bioengineering, genetics, and medicine and director of the biomedical informatics training program at Stanford University in Palo Alto, California.
Altman doesn’t think we’re just at another high point in the boom-bust cycle. The reason computational biology never fully got off the ground before now, he says, is that pharmaceutical companies weren’t yet grappling with the kinds of problems that are best-suited to computational biologists: finding useful signals in tremendously large sets of unsorted, noisy data.
Welcome to big data
Bioinformatics is hardly new to the pharmaceutical industry. The problem is that until recently, those companies weren’t thinking big enough, Altman says. Traditionally trained chemists and biopharmacologists mostly studied their own data sets with no formal training in the computing side.
“Now there are these amazing data sets from extremely clever experimentalists who’ve figured out how to do things in high-throughput [experimentation], and they represent a substantial challenge to people who aren’t trained in computation because it passes what I call the ‘Excel barrier,’ ” Altman says. “I’ve been amazed at what a biologist with Excel can do, but we have now exceeded the Excel barrier in terms of the number of rows and columns and the computational powers of Excel.”
For the pharmaceutical industry, big data is the copious and ever-growing collection of human genome data available freely and publicly. Instead of systematically testing the effects of known compounds—the pharmaceutical industry’s basic model for more than a century—scientists can now investigate backward, combing through genomic data to find links between specific genotypes and diseases and then screening drug data to identify therapeutic candidates. But that kind of data simply won’t fit into an Excel spreadsheet.
“I think the old paradigm of ‘one drug, one target’ is quickly becoming outdated,” says Nicholas Tatonetti, a computational biology graduate student in Altman’s lab who is finishing his Ph.D. this year and who recently accepted an assistant professorship at Columbia University. “It [was] a smart way to think about it originally … and they took it really far and made billions of dollars. But what’s happened is, people forgot that biology is not so simple. The systems are really what we’re playing with here, not one protein doing one simple function.”
“If we can understand [these systems]—and the only way to really do that is through modeling with computational biology—then maybe we can predict the adverse effects of a drug or the therapeutic effects of a drug,” Tatonetti says.
Boom or bubble?
As the pharmaceutical industry’s blockbuster drugs fall off the patent cliff, with precious few drugs in the pipeline to replace them, there are signs that big pharma could turn more of its attention to biologically derived medicines. If that happens, computational biologists will likely play a leading role in their discovery, Altman says.
It’s not a job for traditional computer scientists. “They have no intuition for why they’re doing what they’re doing, so you’d have to train them in-house in a boot camp on the basics of biology, why certain assumptions are not OK, and why other assumptions are,” he says. “The comfort with ambiguity and fuzziness that we introduce in our training programs and, most importantly, the biological vocabulary,” mean that people with computational biology training “wind up being extremely valuable to these companies.”
The latest numbers from the Washington, D.C.-based Computing Research Association’s annual Taulbee Survey, which tracks employment statistics for new Ph.D. computer scientists, show that last year fewer grads in the “Informatics: biomedical/other science” category took postdoc positions; instead, more took positions in industry, says survey director Stuart Zweben. The data will be released in May.
Altman says that according to his own observations, demand for computational biologists far outstrips supply. “I was just talking to a colleague the other day from a major drug company who came in with a piece of paper with 15 bioinformatics jobs that they’re ready to hire tomorrow,” he says. The job listings, posted by Merck, were primarily for positions in Boston and in various cities in Pennsylvania.
The need is even more pronounced in California’s Silicon Valley area, Altman says. It’s not big pharmaceutical companies driving the demand there, he says, but small biotech companies who’ve realized they can capitalize on the enormous amount of publicly available health and genomics data.
Joel Dudley, a former student of Altman’s who last year founded NuMedii, one of Silicon Valley’s numerous biotech companies, agrees that computational biologists currently have a wealth of opportunities. Every person in his graduating class at Stanford received at least one job offer before graduation, he says, and most received more than one. “The job market is amazing,” he says.
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