Finally our paper is in press!
in the coming issue of PNAS, we describe a genome-scale model for predicting the functions of genes and gene networks in rice, an important staple food. Called RiceNet, this systems-level model of rice gene interactions allows us to effectively predict gene function. This information can be used to 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. RiceNet represents a systems biology approach that draws from diverse and large datasets for rice and other organisms.
Rice is a 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 also an excellent model for the perennial grasses, such as Miscanthus and switchgrass, that have emerged as prime feedstock candidates for the production of 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, has proven to be too great a challenge.
Our Joint BioEnergy Institute and the University of California at Davis teams were very fortunate to collaborate on this project with stellar researchers at the University of Texas in Austin and Yonsei University in Seoul, Korea. The paper is titled "Genetic dissection of the biotic stress response using a genome-scale gene network for rice." The article is open access.
RiceNet builds upon 24 publicly available data sets from five species as well as a mid-sized network of 100 rice stress response proteins that my group constructed previously through protein interaction mapping. We have conducted experiments that validated RiceNet's predictive power for genes involved in the rice innate immune response regulated by the XA21 pattern recognition receptor.
We also showed that RiceNet can accurately predict gene functions in another important monocotyledonous crop species, maize.
Insuk's team generated a user-interactive web tool for RiceNet-based selection of candidate genes, which is publicly available.
The ability to identify key genes that control simple or complex traits in rice has important biological, agricultural, and economic consequences. 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.
My co-authors are Insuk Lee, Young-Su Seo, Dusica Coltrane, Sohyun Hwang, Taeyun Oh and Edward Marcotte.
This research was supported in part by JBEI through the DOE Office of Science.This work was also supported by the National Research Foundation of Korea funded by the Korean government Ministry of Education, Science, and Technology Grants 2010-
0017649 and 2010-0001818 and POSCO TJ Park Science fellowship (to I.L.);
the National Science Foundation, National Institutes of Health (NIH), Welch
Foundation Grant F1515, and Packard Foundation (to E.M.M.); and NIH
Grant GM 55962 (to P.C.R.).
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