A Web-based game that uses the brainpower of biology novices to understand molecules key to life and disease is producing working designs of those molecules in a Stanford University laboratory—and the process could influence the way scientific discovery works.
RNA molecules—DNA’s single-stranded relative—play key roles in cell function. Those roles depend on RNA shapes, the way the shape of a key determines which lock it can open. And that’s where things get tricky. RNA shapes depend on how the molecules’ components fit together, but the rules that govern what fits where are not well understood. Shapes that should work in theory often prove impossible to produce in practice.
The game EteRNA, which was started by the Stanford biochemist Rhiju Das and the Carnegie Mellon computer scientist Adrien Treuille, allows researchers to farm out some of the intellectual legwork behind RNA design to 26,000 players, rather than a relatively few lab workers. Players are given a puzzle design—an RNA molecule in the shape of a star or a cross, for example—that they must fill in with the components, called nucleotides, to produce the most plausible solution.
The community of players then votes for the blueprint it thinks will have the best chance of success in the lab. The Stanford researchers select the highest-rated blueprints and actually synthesize them. The scientists then report back the results of the experiments to the crowd to inform future designs.
The crowd-sourcing has produced results that tend to be more effective than computer-generated arrangements. “Computational methods are not perfect in making these shapes,” says Mr. Das, “and as we get to more and more complex ones, they essentially always fail, so we know that there are rules to be learned.”
Players are figuring out these principles on their own, says Mr. Treuille. He says that while they’re more like a grandmother’s instructions on baking a cake than a strict scientific formula, they work remarkably well in practice. “EteRNA players are extremely good at designing RNA’s,” says Mr. Treuille, “which is all the more surprising because the top algorithms published by scientists are not nearly so good. The gap is pretty dramatic.”
He attributes this chasm to the fact that humans are much better than machines at thinking through the scientific process and making intelligent decisions based on past results. “A computer is completely flummoxed by not knowing the rules, but players are unfazed: they look at the data, they make their designs, and they do phenomenally.”
Since the game-and-crowdsourcing project began in January, Mr. Das’s lab has synthesized 306 designs in test tubes, and he hopes to soon begin testing these molecules in cells. And while the project is still in its early stages, the researchers for EteRNA believe it shows great promise for integrating machine learning, experimental data, and the intelligence of the masses to come up with new ideas.
“If this works in practice it will be a completely 21st-century kind of science,” says Mr. Treuille.
