Elucidating Gene Networks Regulating Development in Tomato. Start date: Dec. 1, 2008. Expires: Nov. 30, 2009. Awarded amount to date: $888,188. Principal investigator: Neelima Sinha. Sponsor: University of California-Davis.
Funds a project that will use genomics to understand natural variation in leaf morphology and light response, and to investigate the genetic networks for these two processes. The researchers will use Illumina’s Genome Analyzer and microarray analysis to find differences in DNA sequence and gene expression in these processes, and will use statistical analyses to reconstruct the genetic networks that regulate leaf morphology and light responses. “This analysis will allow identification of central regulators of development and developmental variation,” according to the grant abstract. All data, analysis, and networks generated by the project “will be made available to the public as soon as the data has passed quality control,” the abstract states.
Unraveling the Structure and Kinetics of Biochemical Pathways from Time-Series Analysis. Start date: July 1, 2008. Expires: July 31, 2009. Awarded amount to date: $141,761. Principal investigator: Santiago Schnell. Sponsor: University of Michigan, Ann Arbor.
Funds development of a new method to use time-series gene expression data to predict the function of genetic components based on the role of the gene in biological networks. “This method allows us to perform functional predictions for proteins independent of homologies in structure or sequence, and provides a way to characterize proteins that have not yet been studied using published biological data from high-throughput technologies,” according to the grant abstract.
Teaching Computing to Biologists Through Data Visualization. Start date: Dec. 15, 2008. Expires: Nov. 30, 2011. Awarded amount to date: $146,646. Principal investigator: Kay Robbins. Sponsor: University of Texas at San Antonio.
Supports the development of course materials for a “Computation for Scientists and Engineers” course that uses Matlab to teach programming to biologists through data analysis and visualization. The project has three primary aims: to teach computation by working with data rather than by working with formulas; to use a “hybrid” approach to teaching programming that allows students to examine and plot data through a point-and-click interface, and then show them the programming code that produced the plot; and to integrate “real science to highlight relevance, interaction and discovery.”