When it comes to feeding the world you’d be hard pressed to find a crop more valuable than rice - the staple food of half the world’s population.
After corn and wheat, it’s the grain with the third-highest worldwide production. Rice also happens to be a model for monocotyledonous species – one of the two major groups of flowering plants.
This status has proved extremely interesting to researchers searching for both alternative fuel and food solutions.
Scientists with the Joint BioEnergy Institute (JBEI), a multi-institutional partnership led by Lawrence Berkeley National Laboratory (Berkeley Lab) have developed the first genome-scale model for predicting the functions of genes and gene networks in a grass species.
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.
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.
A RiceNet website is now available that allows researchers from all over the world to use the model. At JBEI, RiceNet will be used to identify genes that have not previously been known to be involved in cell wall synthesis and modification.
JBEI researchers are looking for ways to increase the accessibility of fermentable sugars in the cell walls of feedstock plants. It’s the fermentable sugars that hold the key to unlocking a more efficient way to utilize these crops for biofuels.