ing picture is a more relevant analogy—they’re able to pore over vastly
more data across a wide swath of disciplines and fields. “Biological systems provide remarkable insights into
many things occurring in the world
around us,” Levitt observes.
The possibilities are nearly limitless. In addition to vastly improved
medical therapies and drugs, researchers could use biological data to
better understand air and water pollution patterns and how they impact
health; how hazardous substances
disperse and interact with their surroundings, and how soil organisms
and different chemicals react to different conditions. All of this could lead to
new types of pollution controls, better
HAZMAT monitoring and protective
clothing, and vastly improved food
science and farming methods. Bioinformatics research could also produce
new and better types of fuels, and revolutionize everything from batteries to
Developing better algorithms
and computer models requires fresh
thinking and interdisciplinary input.
Shaw believes in most areas of com-
putational biology, the most signifi-
cant contributions tend to emerge
from interdisciplinary research that
“brings computer scientists together
with biologists, chemists, and other
application experts. Collaborations
of this sort often lead to creative new
approaches to problems that would be
difficult to solve using the paradigms
traditionally employed within any one
of those disciplines.”
According to Shamir, the key to min-
ing relevant biomedical data is refining
algorithms to appropriately estimate
the value of relevant experiments and
data. In addition, it is critical to further
improve storage capacity and compres-
sion—and to tap into cloud comput-
ing more effectively—in order to make
data more accessible. “We need new
and more sophisticated bioinformat-
ics algorithms that can integrate het-
erogeneous data better,” Shamir says.
Increasingly, “The challenge isn’t ob-
taining data, it is figuring out exactly
how to decipher it. Right now, analysis
is the bottleneck.”
Levitt believes bioinformatics will
fundamentally alter science in the
years ahead. “At a certain level, there is
a structure to all data that exists in the
physical world,” he explains. “Today,
many of the methods used to analyze
data are generic; researchers look for
correlations, dependency, and causal-
ity.” However, as researchers learn to
drill down to a more granular layer and
gain a much deeper understanding of
objects, context, and relationships, re-
markable advances will follow.
On a molecular level, “A bridge
and an eating utensil are both made
of steel; it is the shape of the object
and how it works that determines its
place in the scheme of things.” As we
learn to better recognize and differentiate complex patterns in biology,
Levitt says, we will begin to see the
shapes and structures of biological
things beyond the basic structures.
“It is possible to gain a level of knowledge that will revolutionize many aspects of our world.”
Karplus, M., Levitt., M., Warshel, A.
Development of Multiscale Models for
Complex Chemical Systems, The Royal
Swedish Academy of Science, http://
Orenstein, Y., Linhart, C., Shamir, R.
Assessment of Algorithms for Inferring
Positional Weight Matrix Motifs of
Transcription Factor Binding Sites using
Protein Binding Microarray Data, PLoS ONE,
7 ( 9) e46145, 2012. http://www.plosone.org/
Shaw, D.E., Maragakis, P.,
Lindorff-Larsen, K., Piana, S., Dror, R.O.,
Eastwood, M.P., Bank, J.A., Jumper, J.M.,
Salmon, J.K., Shan, T., Wriggers, W.
Atomic-Level Characterization of the
Structural Dynamics of Proteins, Science,
Vol. 330, October, 15, 2010.
Shaw, D.E., Dror, R.O., Salmon, J.K.,
Grossman, J.P., Mackenzie, K.M., Bank, J.A.,
Young, C., Deneroff, M.M., Batson, B.,
Bowers, K.J., Chow, E., Eastwood, M.P.,
Ierardi, D.J., Klepeis, J.L., Kuskin, J.S.,
Larson, R.H., Lindorff-Larsen, K., Maragakis, P.,
Moraes, M.A., Piana, S., Shan, Y., Towles, B.
Molecular Dynamics Simulations on Anton,
SC ‘09 Proceedings of the Conference on
High Performance Computing Networking,
Storage and Analysis, Article No. 39.
Samuel Greengard is an author and journalist based in
West Linn, OR.
© 2014 ACM 0001-0782/14/05 $15.00
model and the computer keys on relationships and correlations, the model
presumably becomes more accurate.
This modeling approach complements
traditional biological research methods
and has the potential to reduce costs,
speed development, and improve the
efficacy of medications.
Such modeling also opens up new
possibilities. For instance, researchers
from eight major institutions are now
collaborating on the Artificial Pancreas
Project, attempting to develop and test
sophisticated software that will automatically control glucose levels for
people with type 1 diabetes.
Nanotechnology, gaming, crowdsourc-ing, and connected devices are also
emerging as important components
in the giant bioinformatics cog. For
example, a game called Dizeez created
by The Scripps Research Institute aims
to resolve questions about genetic
medicine. It has resulted in the identification of several novel gene-disease
annotations. Another game created
by New England Biolabs, Cut it Out,
revolves around players creating and
manipulating DNA sequences.
As researchers turn to these tools,
the possibilities grow exponentially.
Using sensors and data input from
mobile phones, biologists are not only
able to capture a more complete snapshot of the surrounding environment
and various factors—perhaps a mov-
Shamir, the key to
biomedical data is
value of relevant