Systems-level modeling--a new approach for engineering efficient photosynthetic machinery.
Photosynthesis produces the basic building block for crop biomass and yields; however, improving photosynthesis has not been effectively used as a breeding goal. More and more evidences suggested that improving photosynthesis can substantially increase crop yields. The complexity of photosynthesis however makes experimentally identifying new ways to engineer higher photosynthesis inherently time-consuming and costly. Combining systems modeling with evolutionary algorithm makes it possible to identify optimal engineering options for future global climate change scenarios and simultaneously consider environmental constraints, such as with constant or even decreasing nitrogen fertilizer application in the field. This method enables in silico examination of a large number of engineering options which natural selection has not explored, for higher photosynthetic energy conversion efficiency. The new approach comes particularly timely for now when our society is facing serious challenges in food security and global climate change. The traditional reductionist's approach will continue generating critical knowledge required to support this systems biology method to engineering higher photosynthesis.
Zhu
Xin-Guang
XG
Plant Systems Biology Group, Partner Institute of Computational Biology, Chinese Academy of Sciences/Max Planck Society, YueYang Road, Shanghai 200031, China. zhuxinguang@picb.ac.cn
D008954:Models, Biological; D010788:Photosynthesis; D049490:Systems Biology