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. “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.

For more information about Ronald’s research, visit her website at http://indica.ucdavis.edu/.

For more information about the Joint BioEnergy Institute, visit the website at http://www.jbei.org.

Co-authoring the PNAS paper with Ronald were Insuk Lee, Young-Su Seo, Dusica Coltrane, Sohyun Hwang, Taeyun Oh and Edward Marcotte.

This research was supported in part by the Joint BioEnergy Institute through the Department of Energy Office of Science.