The first genome-scale model for predicting the functions of genes and gene networks in a grass species has been developed by an international team of researches that includes scientists with the U.S. Department of Energy (DOE)'s Joint BioEnergy Institute (JBEI), a multi-institutional partnership led by Lawrence Berkeley National Laboratory (Berkeley Lab). Called RiceNet, this systems-level model of rice gene interactions should help speed the development of new crops for the production of advanced biofuels, as well as help boost the production and improve the quality of one of the world's most important food staples.
"With RiceNet, instead of working on one gene at a time based on data from a single experimental set, we can predict the function of entire networks of genes, as well as entire genetic pathways that regulate a particular biological process," says Pamela Ronald, a plant geneticist who holds joint appointments with JBEI, where she directs the grass genetics program, and with the University of California (UC) Davis, where she is a professor in the Department of Plant Pathology and at The Genome Center. "RiceNet represents a systems biology approach that draws from diverse and large datasets for rice and other organisms."
Rice is staple food for half the world's population and a model for monocotyledonous species one of the two major groups of flowering plants. Rice is especially useful as a model for the perennial grasses, such as Miscanthus and switchgrass, that have emerged as prime feedstock candidates for the production of clean, green and renewable cellulosic biofuels.
Given the worldwide importance of rice, a network modeling platform that can predict the function of rice genes has been sorely needed. However, until now the high number of rice genes in excess of 41,000 compared to about 27,000 for Arabidopsis, a model for the other major group of flowering plants along with several other important factors,
|Contact: Lynn Yarris|
DOE/Lawrence Berkeley National Laboratory