Free Energies in Biomolecular Systems: Theoretical Development and Application of Computational Approaches. Start date: Dec. 31, 2005. Expires: Aug. 31, 2007. Expected total amount: $451,474. Principal investigator: Benoit Roux. Sponsor: University of Chicago.
Supports the extension of current theoretical and computational approaches used in the modeling of biomolecular systems and the development of test protocols that will provide increased accuracy and reliability in estimating free energies while remaining computationally tractable.
RNA-Parafold: Algorithms and Web Server for Parametric Aspects of RNA Secondary Structure. Start date: April 1, 2006. Expires: March 31, 2007. Expected total amount: $747,804. Principal investigator: Peter Clote. Sponsor: Boston College.
Funds research on "novel parametric aspects of RNA secondary structure," according to the grant abstract. The investigators will develop algorithms "to compute the partition function, base pairing probability, and statistically valid sampled structures for parameterized folding problems related to mutagens of a given RNA sequence, neighbors of a given RNA structure, locally optimal structures, [and] structures having hairpin loops." Public access to the algorithms will be provided through a web server called RNA-parafold.
Neural Network Model for Chemotaxis in C. elegans. Start date: April 1, 2006. Expires: March 31, 2010. Expected total amount: $455,960. Principal investigator: Shawn Lockery. Sponsor: University of Oregon, Eugene.
Funds development of a computer model of the Caenorhabditis elegans neural network for chemotaxis. The model will be used to test the idea that the nematode's chemotaxis network uses separate neuronal pathways to signal increases and decreases in sensory input, "and should provide new insights into how neural networks function to control adaptive behaviors," according to the grant abstract.
Theoretical Studies of Aqueous Solvation of Proteins. Start date: April 15, 2006. Expires: April 30, 2007. Expected total amount: $639,712. Principal investigator: Toshiko Ichiye. Sponsor: Georgetown University.
Funds development of fast and accurate treatments of solvent effects in computer simulations of biological macromolecules with the goal of understanding the nature of protein solvation using computer simulations. The investigators will develop "the new soft, sticky dipole-quadrupole-octopole potential energy model of water for computer simulations of biological systems," according to the grant abstract. The SSDQO model is a modification of the old soft, sticky dipole (SSD) single-point model, according to the investigators. The model will be made compatible with the CHARMM force field and will be implemented into the CHARMM computer program.
Advanced Bayesian Approaches for Heterogeneous Temporal Genomic Metadata. Start date: June 1, 2006. Expires: May 31, 2009. Expected total amount: $144,999. Principal investigator: Yulan Liang. Sponsor: State University of New York, Buffalo.
Proposal to develop Bayesian modeling techniques for studying the dynamics of heterogeneous temporal genomic metadata. These techniques will be used for inferring the genomic profiles associated with diseases and treatments; estimating important hidden biological parameters; and constructing gene-gene and protein-protein interaction networks and pathways for hybrid biological systems.
Integrated Cross-disciplinary Summer Program in Bioinformatics. Start date: June 1, 2006. Expires: May 31, 2009. Expected total amount: $282,747. Principal investigator: Howard Laten. Sponsor: Loyola University of Chicago.
Sponsors a nine-week summer undergraduate bioinformatics program at Loyola University of Chicago.
Bayesian models and Monte Carlo strategies in identifying protein or DNA sequence motifs. Start date: July 1, 2006. Expires: June 30, 2009. Expected total amount: $160,246. Principal investigator: Jun Xie. Sponsor: Purdue University.
Supports the development of new probability models and Monte Carlo strategies to detect functionally relevant sequence motifs within protein or DNA sequence information. The investigator proposes to develop Bayesian models for protein sequence motifs that combine distributions of amino acids with distributions of sequence-derived secondary and tertiary characteristics; to develop a parallel procedure that runs multiple Markov chains in parallel to improve the convergence of the motif-alignment algorithm; and to develop new probability models and statistical methods that describe modules of transcription factor binding sites and combine genomic sequence information with gene expression information.