A Bioinformatics System for GCxGC-MS (Comprehensive Two-Dimensional Gas Chromotography). Start date: Feb. 1, 2005. Expires: Jan. 31, 2007. Current year award amount: $493,692. Principal investigator: Stephen Reichenbach. Sponsor: GC Imaging.
Phase II SBIR supports development of bioinformatics tools to transform complex data produced by comprehensive two-dimensional gas chromatography with mass spectrometry (GCxGC-MS) to usable chemical information. According to the grantees, results from a Phase I SBIR grant demonstrated the feasibility of using bioinformatics to automatically identify chemical components in complex matrices analyzed by GCxGC-MS. Phase II will carry out further theoretical and experimental research to develop solutions that will enable broader use of GCxGC-MS system.
II+SEI Mediation Technology for Biological Pipeline Analysis. Start date: Feb 1, 2005. Expires: Jan. 31, 2006. This grant is awarded to two investigative teams:
- Arizona State University. Principal investigator: Zoe Lacroix. Current year award amount: $178,999.
- University of Maryland College Park. Principal investigator: Louiqa Raschid. Current year award amount: $253,705.
Proposal to develop a computational infrastructure to execute data integration protocols for biological pipelines.
Challenges to be addressed include: an exploration interface that can express high-level complex queries that in turn are translated into lower-level data manipulation operators; the specification and population of an alternative splice protein analysis pipeline; and a mediation testbed that is implemented using XML-based wrapper technology and IBM’s DB2 II mediator technology.
Global Minimum Determination by Underestimation: Application to Protein-Ligand Docking. Start date: Feb. 15, 2005. Expires: Jan. 31, 2006. Current year award amount: $66,003. Principal investigator: J. Ben Rosen. Sponsor: University of California San Diego.
Proposal to develop two new algorithms for predicting the global minimum of energy surface functions arising in protein-ligand docking in computational drug design. The location of the global minimum determines the most likely location of the docked ligand on the protein surface. This research is based on earlier results where it is assumed that the energy surface is basin-shaped, with many local minima.
The first new algorithm will determine a quadratic underestimating function where the eigenvalues of the function Hessian satisfy specified lower and upper bounds. The second proposed new algorithm will determine an underestimating function which consists of the sum of a small number of negative Gaussians.
Grid-enabled Integration of Experimental Data and Simulations for Flexible Protein Docking. Start date: March 1, 2005. Expires: Feb. 28, 2006. Current year award amount: $247,821. Principal investigator: Jesus Izaguirre. Sponsor: University of Notre Dame.
Funds development of grid-enabled integration of experimental data and simulations for flexible protein docking. These methods will be validated on HIV-1 protease, HIV-1 integrase, and dihydrofolate reductase, for which abundant structural and dynamical data exists. Putative interactions will be validated through NMR experiments. This research will produce grid-based tools and a database for molecular simulations and docking accessible to the research community.
Sparse Spatial Reasoning for High-Throughput Protein Structure Determination. Start date: July 9, 2004. Expires: June 30, 2009. Current year award amount: $108,511. Principal investigator: Christopher J. Bailey-Kellogg. Sponsor: Dartmouth College.
Supports development of new methods for analyzing the structure of protein molecules, interpreting spatial data sets containing significant noise and sparse information content.
This project pursues new theory, representations, and algorithms to address data interpretation and experiment-design problems in domains characterized by sparse spatial data. A spatial reasoner will be developed that will represent data, models, and biophysical knowledge with multi-level, multi-dimensional topological and geometric objects and constraints. This representation will allow algorithms to match features of data and models, overcome problems of noise and scarcity by uncovering consistent feature sets, target clarifying queries in response to conflicts, and plan additional experiments.