Friday, September 02, 2005

Complexity ? No, I didn't order that. Send it back.

In various earlier posts [like this], I made a passing reference to the fact that biologists are finding out that RNA, "the other nucleic acid", plays a much bigger role in biological regulation than we've thought for the last few years, with our focus on DNA and proteins. That role continues to expand -- the current issue of Science magazine [subscription required, unfortunately] is devoted to highlighting the many processes that non-coding RNA [ie RNA that doesn't code for proteins] is involved in.

The main takeaway, for me at least, is this: things just got a whole lot more complicated. We were already struggling with trying to figure out how all the genes/proteins we know about work together, and the bits of DNA encoding those genes only represent about 2% of the human genome ie we thought only about 2% of the human genome did much of anything. However, a recent paper showed that 10% of the human genome is "active" [and a whopping 62% of the mouse genome is active, as described in papers published in this week's issue of Science], and the extra "stuff" is all these non-coding RNAs whose function we really have no clue about. In other words, we may have to worry about 5 times as much stuff as we thought we had to worry about [in humans], and chances are that this means things are [a lot] more than 5 times as complicated as we already thought they were.

TK has a funny way of describing the difference between engineers and scientists: scientists, when presented with something complicated, say "Cool ! I can spend lots of time figuring out how it works !". Engineers, on the other hand, like simplicity, so their reaction is "Who ordered that ?". I fall into the engineer camp, so the depressing part about all this to me is that there's no obvious method for figuring out how it all works other than to just painstakingly do all the required lab work.

The idea/hope/theory is that computational methods will help to speed up the rate of discovery, but it's not clear to me how much they've really helped so far -- they're certainly good at generating lots of hypotheses, but at the end of the day you end up having to do the experiment anyway to verify the hypothesis, and that's what takes a long time. To some extent, it seems like computational methods have mainly made it clearer how little we really know, which is useful in and of itself, but probably not quite what most people are hoping for.

All that said, it's pretty clear that without developing more and better computational [and experimental, of course] methods for generating and examining data, we're never going to have a decent handle on how Mother Nature works. We might as well grit our teeth, get on with it and hope that we don't discover even more complexity.


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