Arabidopsis 2010: High-Throughput Functional Analysis of Differentiation Network Genes

点击翻译按钮获取中文摘要

New York University is awarded a grant to determine gene function for a network or group of genes by using high-throughput reverse genetics to investigate the gene circuitry of differentiating cells. An essential part of the project is the development of computational techniques that can predict genetic redundancy in the genome. Accurate redundancy predictions will provide a model to design efficient reverse genetics pipelines to describe the function of hundreds of genes and enable small labs to participate in focused, high-throughput phenomic screens. The study first targets master regulators of differentiation in the root, examining their influence on gene expression at the cell level. In addition, the investigators examine the function of putative downstream cell-specific targets, many of which are of unknown function. Thus, the end result will be a description of critical hubs in cellular differentiation and the role of their downstream targets. These circuits are expected to act locally to control specific plant traits without perturbing general plant functions. Thus, they are excellent targets for enhancing desirable plant traits or diminishing undesirable ones. The development of high-throughput techniques to discover the function of genes involved in cellular differentiation will allow students from the high school to graduate levels to participate in biological research. A new course in Comparative Genomics will be organized. In addition, New York City high school teachers will be able to participate in an NYU program that introduces them to modern biological research. The proposed project has strong ties to applied research that could lead to improving plants for agricultural and other commercial applications since the genes it targets for analysis are likely to be involved in specifying commercially valuable traits. The project website will be accessible from the PI's homepage at http://homepages.nyu.edu/~kdb4348/, will be open to the public, and will contain information about the project, participants, developed software, and databases built under this award. The database and information will be shared with TAIR and the VirtualPlant project also funded at NYU, and will also be freely available to interested academic researchers.