Artificial Pattern Formation with Synthetic Gene Networks. Start date: May 1, 2005 Expires: April 30, 2008. Expected total amount: $383,000. Principal investigator: Ron Weiss. Sponsor: Princeton University.
Supports a project that will engineer de novo cellular patterns by building synthetic gene networks. The project intends "to create a new engineering discipline for generating user-specified artificial differentiation patterns," according to the grant abstract. Novel genetic circuits will be built and validated experimentally. These circuits will direct cells to form stable and dynamic spatiotemporal gene expression patterns. The project is expected to advance research in a number of areas in systems biology, including the understanding of robust gene networks, noise in gene expression, gene regulation, and cell-cell communications, as well as provide mathematical models for intracellular and intercellular systems.
A Systems Approach to Genomic Signal Processing: From Signal Extraction to Regulatory Intervention. Start date: May 1, 2005. Expires: April 30, 2007. Expected total amount: $515,318. Principal investigator: Edward Dougherty. Sponsor: Texas Engineering Experimental Station.
Funds development of signal-processing-based solutions to individual sub-problems that arise in functional genomics that can be applied to disease diagnosis and therapy. A "large body" of integrated software will be developed to address five categories of microarray-based genomic signal processing: signal extraction, clustering, classification of phenotypes, modeling genetic regulatory networks, and developing strategies for regulatory intervention.
Haplotype Linkage and Association Mapping of Quantitative Trait Loci. Start date: June 1, 2005. Expires: May 31, 2006. Expected total amount: $61,147. Principal investigator: Ruzong Fan. Sponsor: Texas A&M Research Foundation.
Supports development of statistical methods and algorithms for linkage and association mapping of quantitative trait loci. The project will extend existing methodology to analyze non-temporal genetic data and will create novel approaches and models to analyze temporally longitudinal human genetic data.
Biomolecular Simulations Coming to Age: Folding of Globular and Membrane-bound Proteins Studied by Computer. Start date: May 1, 2005. Expires: April 30, 2006. Expected total amount: $168,128. Principal investigator: Alfredo Cardenas. Sponsor: University of South Florida.
Project will use computer simulation techniques to study the mechanism of folding of proteins. The main method to be employed is the stochastic difference equation in length algorithm, a boundary value method that permits the calculation of trajectories connecting two known states of the system. This methodology allows viewing, with atomic detail, dynamical processes that were impossible to study before by computer simulations, according to the grantees. The project will study the folding process of globular single-domain proteins (apomyoglobin and bovine beta-lactoglobulin) and multi-domain proteins (gene-3 protein of phage fd and the phosphoglycerate kinase) in aqueous environment by simulating explicitly the water molecules. According to the investigators, most of these systems have not been studied before by computer simulations because of their large size and their slow folding times.
MSM: Multi-Scale Modeling of the Mouse Heart: From Genotype to Phenotype. Start date: June 1, 2005. Expires: May 31, 2006. Expected total amount: $339,613. Principal investigator: Andrew McCulloch. Sponsor: University of California, San Diego.
Funds development of new multi-scale computational models of the mouse heart that integrate functionally and structurally across multiple scales of biological organization, from molecular networks to the organ system. These new models will integrate data across the following biological scales and classes of mathematical models: biochemical models of molecular regulatory networks; biophysical models of whole excitation-contraction coupling; microstructural constitutive models of the regional anisotropic electrical and mechanical properties of multicellular cardiac tissue; three-dimensional anatomically detailed models of mouse ventricular geometry, fiber architecture, and conducting system anatomy; and lumped parameter models of circulatory system hemodynamics. The project will also develop a multi-scale data resource that will allow scientists to navigate whole mouse heart anatomy at high resolution.
Five Hierarchical Statistical Models for Protein Folding. Start date: June 1, 2005. Expires: May 31, 2006. Expected total amount: $160,419. Principal investigator: Christopher Bystroff. Sponsor: Rensselaer Polytechnic Institute.
Project to study the folding pathways of proteins using five statistical models to represent the various stages of the path to a folded protein. The models represent a decision tree in the sequence-structure space, where each model feeds into the following step. These steps include initiation (the folding of short motifs); propagation (local extensions of structured regions); condensation (contacts between pairs of local units); packing (units fold into a space filling way); and topology (ordering of loop lengths to yield the fully folded structure). Green fluorescent protein will be used as a test case.
Conceptual Data Integration for the VirtualPlant. Start date: June 1, 2005. Expires: May 31, 2006. Expected total amount: $437,147. Principal investigator: Gloria Coruzzi. Sponsor: New York University.
Supports development of new informatics, visualization, math, and statistic tools to enable dynamic modeling and visualization of molecular networks in cells. These tools will be integrated in a common platform so as to understand how internal and external perturbations affect processes, pathways and networks controlling plant growth and development. The project has four major goals: integrating known relationships among genes, proteins, and molecules; developing novel visualization techniques; integrating mathematical and statistical methods with the novel visualization techniques to help summarize the data and test hypothesis; and developing algorithms to make predictions of the molecular network state under untested conditions. These approaches will be combined in a system called VirtualPlant.