Biology Reference
In-Depth Information
PhD students and postdocs with computational and wet-lab skills, and
he had to worry about how frequently his lab members moved back and
forth between the two domains—too much or too little time on one side
or the other might damage their chances for doing successful biological
work.
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And, most importantly, he had to ensure that members of the
wet lab and computer lab were communicating well enough to collabo-
rate effectively. Doing good work meant continually attending to this
boundary between computation and biology.
During my fi eldwork, a PhD student from the computer science de-
partment at MIT visited Burge's lab. As he explained to me in an inter-
view, he was trying to fi nd a “good” problem for his dissertation work,
so his advisor had made arrangements to have him spend some time in
a biology lab, looking for an appropriate biological problem to which
he could apply his computational skills. At least in the beginning, he
had found such a problem elusive: “It's very hard to get a PhD level
background in computer science and biology. Right now I'm doing this
project in a biology lab but . . . I don't feel like I have a huge amount
of background, so at this point I'm trying to i gure out the things that I
need to know to get the work done.” Part of the reason for the problem,
he surmised, was a fundamental difference in the way biologists and
computer scientists think:
Most computer scientists are probably . . . put off of biology
because the very things they like about computer science seem
to not be present in biology . . . Francis Collins even made that
particular observation, when he fi rst started his career he was a
physicist, I believe, and he stayed away from biology because it
was quote-unquote messy—it was messy science and he didn't
want to deal with the messy details. . . . A lot of computer sci-
entists who may want to get into computational biology would
have to go through a similar process of reconciling their dis-
like for messiness, because they are trained to look for elegance,
mathematical elegance.
This became a constant trope in my interviews and conversations with
biologists and computer scientists: the latter were interested in elegant,
discrete, neat, tractable problems, while the former constantly had to
deal with contingency, exceptions, messiness, and disorder.
The lack of communication between computer scientists and biolo-
gists, then, goes deeper than problems with language. Indeed, we can
