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For mathematicians who grasp biology, opportunities
are plentiful. But which mathematicians? And which math?
"People outside of the field see it as one uniform
thing, but there are definitely different disciplines,"
says Steve Lincoln, who is vice president of
Bioinformatics at Affymetrix Inc. in Santa Clara,
California. "Pure math is the study of systems and
equations, and the logical structure of things that are
generally pretty abstract in nature. [On the other hand]
if you are trained as a biostatistician, you're trained
to do applied [work]. Some people wind up doing basic
research for a living, but most apply [statistics] to a
problem, be it [in the] life sciences, or health care,
or modeling the stock market."
Biostatistics is big, but such basic mathematics
tools such as differential equations--especially partial
differential equations--are also useful. These equations
are handy for tracking quantities in time and space--a
quality just right for investigating systems and
mechanisms, biological or otherwise. Variables might
include metabolites of a cell, the strength of a
neuron's signal over time, or the number of patients
infected by a virus as it spreads over a geographical
area.
Ordinary differential equations typically apply when
several variables are a function of time, while partial
differential equations get used when a variable is
dependent on both time and space, says Michael Reed, a
professor of mathematics at Duke University who applies
mathematics to physiology and medicine. For example, a
protein in a cell might start life in the nucleus and
then move into the cytoplasm to take part in cell
signaling. Hence the amount of protein in one area of
the cell depends both on time and on the amount of
protein somewhere else.
Academic boom
Academia and the National
Institutes of Health (NIH) figure to be important
employers of mathematicians that cross over into
biology. "The experience in the human genome project was
that 25% to 30% of every project's budget went into
informatics. If you need to coordinate a lot of data,
you need to devote significant resources to doing that.
If some significant fraction of the NIH budget is going
to large-scale projects, and a substantial fraction of
each project's budget goes into informatics, that
translates into a lot of jobs," says David States, who
is a professor of bioinformatics at the University of
Washington, Seattle.
Mathematics departments are on the look out for
mathematicians well versed in biology. "You get
mathematicians who don't really know much biology, or
biologists who don't know much math. It's not so easy to
find a mathematician who is trained well enough in
biology to talk to biologists and be taken seriously. I
see a big opportunity there in the foreseeable future,"
says Reinhardt Laubenbacher, a research professor at the
Virginia
Bioinformatics Institute (VBI) and a mathematics
professor at Virginia Polytechnic Institute and State
University in Blacksburg. Laubenbacher should know; VBI
just added one new research professor, bringing the
number of permanent faculty to 15, as well as two
visiting research scientists.
If the outlook is bright in academia, will there be
similar heady times in industrial sectors like
pharmaceuticals and biotechnology? Newspaper and
magazine articles often quote corporate managers
describing grand visions of computer simulations of
disease states, as well as "in silico" drug design that
could, it is argued, replace the battery of compounds
churned out today by synthetic chemists, as well as the
expensive animal tests used to weed out poor
performers.
"It's a nice long-term goal, but we have a lot of
work to do [to get there]," says States. "I'm not sure
the pharmaceutical companies are investing in it right
now. I think there's more of an empirical [frame of
mind]: Don't show me a model, show me experimental data
that I can show the Food and Drug Administration. And
some of that may be appropriate. A lot of the modeling
opportunities are probably more academic than
commercial."
Still, it isn't all bad news for employment at big
pharma and big biotech. Biostatisticians are in demand
to assist with analysis of clinical trial data. Genetic
data and analysis is playing an increasing role in
clinical trials, with companies beginning to track side
effects and sometimes responses of patients based on
genetic markers (pharmacogenomics). "Biostatistics is
one of the biggest employment opportunities in the
pharmaceutical industry," says Robert Jernigan, director
of the Laurence H. Baker Center for Bioinformatics and
Biological Statistics at Iowa State University.
"Biologists have to pick up the
mathematics"
But the onus isn't all on
mathematicians. Biologists could use an infusion of
mathematics as well, says Iya Khalil, vice president of
R&D at Gene Network Sciences Inc. in Ithaca, New
York. Biologists frequently run experiments that
generate large amounts of data, but the usefulness of
the data will likely depend on the design of the
experiment. "In the realm of high-throughput
experiments, often times [mathematicians] figure out
that if the biologist had done the experiment in a
particular way, it would have improved the statistics of
the analysis by an order of magnitude," she says. By
then it's too late.
"Biologists have to pick up the mathematics," agrees
Laubenbacher. "If you want to use your data to make a
mathematical model, then you need to take the modeling
method into account when you design new experiments.
Different modeling methods will require different kinds
of data."
Mathematicians are following in the footsteps of
physicists, who have crossed into biology in droves, in
part because the work looks familiar to them, says
Lincoln. "[Automated experiments] produce very large
data sets, which tend to be multivariate in nature. In
any biological experiment you can do on a large sale,
you're bound to capture a variety of phenomena. One is
the one you are interested in, and the other six or 600
are either noise or confounding factors. It's fairly
analogous to the kind of work that physicists have been
doing for years."
There are no limits on opportunities for
mathematicians. "The work may not look fancy to pure
mathematicians ... you may be using 19th century math.
The intellectual difficulty is in the biology, and how
to use the mathematics to study it," says Reed. And much
depends on mathematics departments embracing biology so
that students can get proper training. "I think they
will, but it's a slow transition."
Read the companion article Profile:
The Scrutable and the Inscrutable, also part of this
Next Wave feature.
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