NSF Bioinformatics Grants Awarded Nov. 3 — Dec. 8, 2010
Theory of Biomolecular Structure and Dynamics
Start Date: Dec. 1, 2010
Expires: Nov. 30, 2012
Awarded Amount to Date: $171,803
Principal Investigator: James McCammon
Sponsor: University of California, San Diego
The grant abstract states that the funds will be used to develop methods for using computers to understand the activity of biological molecules. The computational tools produced during the course of the project "will include more accurate models for the watery solvent around the simulated biological molecules, and ... methods for sampling the shapes and energetic properties of the biological molecules."
Novel Sampling Approaches for Protein Modeling Applications
Start Date: Sept. 1, 2010
Expires: July 31, 2014
Awarded Amount to Date: $376,712
Principal Investigator: Yaohang Li
Sponsor: Old Dominion University Research Foundation
Supports the investigation of sampling approaches that can be used to predict high-resolution protein structures. The researchers plan to establish computational models for "multi-scoring functions sampling in protein structure modeling" and design sampling algorithms to explore protein conformation and apply the algorithms to applications such as protein folding and protein-protein docking.
Multiscale Genomic Imaging Informatics
Start Date: Aug. 1, 2010
Expires: Nov. 30, 2011
Awarded Amount to Date: $336526
Principal Investigator: Yu-Ping Wang
Sponsor: Tulane University
The grant abstract states that the funds will be used to build a publicly accessible "imaging database management and analysis system that can integrate multiscale and multimodality structural genomic information with microarray gene expression for comprehensive and integrative analysis of a biological system." The researchers plan to develop "image processing and signal analysis algorithms to extract visual quantitative traits and structural genomic signatures" from genomic imaging techniques such as fluorescence in situ hybridization imaging and microarray-based comparative genomic hybridization, and correlate them with microarray gene expression data. The information will be integrated to provide improved characterization of biological systems.