food science ,

RiceNet: Better grain, new biofuel crops

UC DAVIS (US) — The first genome-scale model for predicting gene function in rice is expected to speed up development of new crops for biofuels—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, professor of plant pathology at University of California, Davis and director of the grass genetics program within the U.S. Department of Energy’s Joint BioEnergy Institute.

Rice is a staple food for half of the world’s population and a research model for monocotyledonous species—one of the two major groups of flowering plants. It’s especially useful as a model for perennial grasses, such as Miscanthus and switchgrass, which have emerged as prime feedstock candidates for the production of clean, green, and renewable cellulosic biofuels.


Researchers are using molecular biology to better understand how to improve the hardiness and yield of this grain, which plays such an important role in global nutrition and food security.

Given the worldwide importance of rice, a network-modeling platform that can predict the function of rice genes has been sorely needed. However, the task has been complicated by the high number of rice genes—more than 41,000 genes compared to about 27,000 genes for the common research plant Arabidopsis—among other important factors.

“RiceNet builds upon 24 publicly available data sets from five species as well as an earlier mid-sized network of 100 rice stress response proteins that my group constructed through protein interaction mapping,” Ronald says in the study published online in Proceedings of the National Academy of Sciences. “We have conducted experiments that validated RiceNet’s predictive power for genes involved in the rice innate immune response.”

Ronald and her team also showed that RiceNet can accurately predict gene functions in maize, another important monocotyledonous crop species.

A RiceNet website is now available to researchers around the world. At the Joint BioEnergy Insitute, RiceNet will be used to identify genes that have not previously been known to be involved in cell wall synthesis and modification. Researchers are looking for ways to increase the accessibility of fermentable sugars in the cell walls of biofuel feedstock plants.

Researchers from the University of Texas at Austin and Yonsei University in Seoul, Korea, contributed to the study, which was supported in part by the Joint BioEnergy Institute through the Department of Energy Office of Science.

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