After determining the relative abundance of each of the proteins involved in photosynthesis, the researchers created a series of linked differential equations, each mimicking a single photosynthetic step. The team tested and adjusted the model until it successfully predicted the outcome of experiments conducted on real leaves, including their dynamic response to environmental variation.
The researchers then programmed the model to randomly alter levels of individual enzymes in the photosynthetic process.
Before a crop plant, like wheat, produces grain, most of the nitrogen it takes in goes into the photosynthetic proteins of its leaves. Knowing that it was undesirable to add more nitrogen to the plants, Long said, the researchers asked a simple question: Can we do a better job than the plant in the way this fixed amount of nitrogen is invested in the different photosynthetic proteins?
Using evolutionary algorithms, which mimic evolution by selecting for desirable traits, the model hunted for enzymes that if increased would enhance plant productivity. If higher concentrations of an enzyme relative to others improved photosynthetic efficiency, the model used the results of that experiment as a parent for the next generation of tests.
This process identified several proteins that could, if present in higher concentrations relative to others, greatly enhance the productivity of the plant. The new findings are consistent with results from other researchers, who found that increases in one of these proteins in transgenic plants increased productivity.
By rearranging the investment of nitrogen, we could almost double efficiency, Long said.
An obvious question that stems from the research is why plant productivity can be increased so much, Long said. Why havent plants already evolved to be as efficient as possible?
The answer may lie in the fact that evolution selects for survival and fecundity, whil
|Contact: Diana Yates|
University of Illinois at Urbana-Champaign