Scientist joins computers, biology for discovery

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Using statistics and visualization tools Webb-Robertson and others developed, the team identified proteins present in the virulent growth condition that were not present in the non-virulent growth condition, validating the group’s analytical toolbox.

“Not everyone has access to high-performance computing,” Oehmen says.  “What we were trying to do is bridge the gap so that through our visual analytics component, scientists could have access to the computational power of a supercomputer from their desktop.”

Biologists require computational power for completely different purposes than physicists and engineers, Oehmen says.  Physicists often construct formulas based on well-understood physical principles and use them to describe large dynamic systems, such as fluid flow or heat conduction.

Biologists, in contrast, deal with many layers and levels of data simultaneously, he says.  Their problems are analogous to data-mining problems.

To answer that kind of question, Oehmen has joined scientists from the Pacific lab and Oak Ridge National Laboratory in a project dubbed DICCBS — Data-Intensive Computing for Complex Biological Systems.

The group, led by Tjerk Straatsma at Pacific Northwest and Nagiza Samatova at Oak Ridge, is creating data-analysis, modeling and data-mining tools to extract knowledge across layers of biological information, from genes to proteins to whole organisms.

As part of the project, Oehmen has adapted the analytic program BLAST, a nearly universal code biologists use to make gene sequence comparisons.  Oehmen modified the code to run on a parallel computing platform, increasing the efficiency of searching genomic data by 1,500-fold.

“I can’t imagine working anywhere else,” Oehmen says.  “We have a critical mass of people doing pure science and bringing new technology together to solve the biggest problems in biology.  What could be better than that?”

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