This is a good example of an advancement in one area because a researcher from another area joined the effort. His engineering perspective allowed him to see the problem differently. It's like the opposite case, where for example a biologist joins computer scientists and invents a clever new genetic algorithm.

I wonder if he would recommend that we participate in projects like http://boinc.bakerlab.org/rosetta/ or http://folding.stanford.edu .

Interesting article, but one of the most confusingly-organized pieces of science coverage I have run across lately.

A few key takeaways:

About the guy who came up with the ideas discussed in the article: Danny Hillis, an inventor, scientist, engineer, author, and visionary, pioneered the concept of parallel computers that is now the basis for most supercomputers, as well as the RAID disk array technology used to store large databases. He broke the von Neumann bottleneck and changed the way we think about, and use, computation. Hillis's contributions affect nearly every scientific discipline, not to speak of the daily lives of most people on the planet. When he speaks, I listen.

"We misunderstand cancer by making it a noun", Hillis says. "Instead of saying, 'My house has water', w' say, 'My plumbing is leaking.' Instead of saying, 'I have cance'", we should say, "I am cancering.' The truth of the matter is we're probably cancering all the time, and our body is checking it in various ways, so we're not cancering out of control. Probably every house has a few leaky faucets, but it doesn't matter much because there are processes that are mitigating that by draining the leaks. Cancer is probably something like that.

"In order to understand what's actually going on, we have to look at the level of the things that are actually happening, and that level is proteomics. Now that we can actually measure that conversation between the parts, we're going to start building up a model that's a cause-and-effect model: This signal causes this to happen, that causes that to happen. Maybe we will not understand to the level of the molecular mechanism but we can have a kind of cause-and-effect picture of the process. More like we do in sociology or economics."