Imitating plant evolution proves fruitful

An evolutionary algorithm
Instead, his team used an evolutionary algorithm that selected a reaction at random and either increased or decreased the relevant protein by 10 percent. Adjusting the amount of an enzyme effectively changes the rate of its corresponding reaction. Because the reactions were all linked, a relatively minor alteration could impact the entire pathway.

After every round of adjustments, the supercomputer determined whether the virtual plant, given a steady level of nitrogen and overall protein, could fix more or less carbon dioxide per unit of light (the standard measure for photosynthesis efficiency).

“What we find is that 99 times out of 100, we’re actually making it worse, rather than better,” Long said. But that rare improvement could be used to seed the next generation in the plant’s simulated evolution. The computer repeated the process for 1,500 generations, always hunting for the best possible solution. By the time it was finished, the virtual plant’s photosynthesis output had clobbered its real-world competition.

The big question, of course, is whether the tinkering could work within a living plant.

Some independent findings have given Long reason for optimism. One protein whose levels increased significantly within the simulation — inscrutably named sedoheptulose-1,7-bisphosphatase — has been found to aid real plant production when upped experimentally by researchers in England and Japan. “That’s one clue we have. It was striking that the computer actually selected this protein,” Long said.

Boosting virtual plant proteins
For a more efficient plant, in fact, the supercomputer suggested the protein should be increased four-fold. The simulation gave another big boost to a notoriously inefficient but abundant enzyme abbreviated RuBisCO, which Blankenship called the “800 pound gorilla of plant proteins” and a key player in photosynthesis.

A handful of other proteins were significantly increased as well in the virtual plant, whereas most others were increased or decreased by no more than 20 percent to 30 percent. As a start, Long suggested, maybe the half-dozen proteins whose levels were altered the most could be the focus of future genetic engineering. “We view this as a guide,” he said. “These are the best bets.”

If the evolutionary algorithm produced such a clear increase in efficiency, why hasn’t evolution naturally done the same thing?

One reason, Long said, may have to do with a plant’s priorities. Evolution selects for those individuals producing the most viable offspring, which isn’t the same thing as selecting for a photosynthetic superstar. Getting the biggest bang for the buck from photosynthesis may require cutbacks in other plant processes.

Long believes the computer’s simulated shortchanging of some enzymes known to hamper photosynthesis could prove lethal for real plants exposed to high temperatures and drought conditions. Higher photosynthesis efficiency, it seems, may come at the cost of reduced stress tolerance — just one of the many potential pitfalls of messing with Mother Nature that will have to be addressed in the future.

In addition, most vegetation evolved under atmospheric conditions far different from what exists today. Perhaps, Long and his co-authors reasoned in their study, the plants’ photosynthetic cycle simply hasn’t been able to adequately adjust to the spike in atmospheric carbon dioxide levels over the past 150 years.

Nor are green plants the photosynthetic champions of the natural world. That distinction belongs to tiny cyanobacteria and some microalgae, which have generated considerable interest from researchers seeking to harness their long-term potential for producing bioenergy.

Nevertheless, Blankenship said exploring different avenues is likely to be the best strategy, and he lauded Long’s efforts at trying to maximize what nature didn’t necessarily intend. “There’s really no reason to think that we can’t do better,” he said. “You shouldn’t assume that nature is just perfect.”

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