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Recently Awarded NSF Bioinformatics Grants


• Duke University will receive $7.2 million over the next five years from the US National Science Foundation for computational geometry for structural biology and bioinformatics.

The project will address the problems researchers face when they attempt to apply geometry to biology.

“Life at its most detailed level depends on the geometric shape of molecules. Nevertheless, geometric methods are relatively uncommon in computational biology, primarily because of difficult and unsolved issues in applying geometric computing to biology,” the award abstract said.

Under the guidance of investigators Herbert Edelsbrunner, Jack Snoeyink, Homme Hellinga, Pankaj Agarwal, and Leonidas Guibas, the project will study geometric representations and develop novel geometric methods that could help shed light on the relationship between form and function as it relates to protein structure and folding.

These methods will be incorporated into software structural biologists will be able to use and integrate with their current tools.

• The US National Science Foundation has awarded the University of California, Riverside a $490,000, three-year grant to support a collaborative project in which two computer scientists and a plant geneticist plan to develop new methods, algorithms, and software for bioinformatics.

The three-year grant will support research into computational paradigms such as quartet methods, interactive systems, and approximation algorithms as applied to the evolutionary analysis of gene sequences, gene duplication, and horizontal transfer events in the genomes of chloroplasts.

Tao Jiang is the principal investigator on the project and Michael Clegg is serving as the co-principal investigator.

•Researchers from Stanford University and Hebrew University in Jerusalem, Israel were awarded a $494,000 grant to develop new technology for analyzing biological data.

Assistant professors Daphne Koller and Peter Small of Stanford University and professor Nir Friedman of Hebrew University will team up to develop languages for statistical modeling of biological processes, techniques for learning the models from data, and algorithms for reasoning using the resulting models.


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