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Built on 24 known data sets, the first genome-scale model for predicting the functions of genes and gene networks in a grass species, called RiceNet, is expected to help speed the development of new advanced biofuels from Miscanthus and switchgrass (see complete press release).
"With RiceNet, instead of working on one gene at a time, based on data from a single experiment set, we can predict the function of entire networks of genes, as well as entire genetic pathways that regulate a biological process," says co-founder Pamela Ronald, aprofessor of plant pathology with the Joint BioEnergy Institute.
To the right is a graphic visualization of RiceNet data relationships, color-coded to indicate the likelihood of network links; red for higher and blue for lower. Professor Ronald blogs:
RiceNet offers an attractive and potentially rapid route for focusing crop engineering efforts on the small sets of genes that are deemed most likely to affect the traits of interest. The paper, titled "Genetic dissection of the biotic stress response using a genome-scale gene network for rice," is open access.
Ronald has already validated RiceNet's predictive power for genes involved in the rice innate immune response regulated by the XA21 pattern recognition receptor.
Since many researchers are looking to increase fermentable sugars in feedstock cell walls, the RiceNet website is now publicly available. A team at the Yonsei University in Korea generated a user-interactive web tool for RiceNet. Ronald and her colleagues at JBEI will be using RiceNet to identify genes that have not previously been known to be involved in cell wall synthesis. Ronald adds:
RiceNet builds upon data sets from five species as well as a network of 100 rice stress response proteins that my group constructed previously through protein interaction mapping." Rice is also an excellent model for perennial grasses, such as Miscanthus and switchgrass, that have emerged as prime feedstock candidates for cellulosic biofuels.
Ronald's website: http://indica.ucdavis.edu
http://scienceblogs.com/tomorrowstable/