Remember way back, when I mentioned genetic algorithms in the course of criticizing Michael Egnor? I described, conceptually, a way of using the mechanisms of mutation and selection to discover the structure of DNA, given X-ray crystallography data.
Let a “gene†in the computer’s memory be the spatial locations of molecular units: sugars, phosphates, purines, pyrimidines — the small molecules which Franklin, Pauling et al. knew were the constituents of DNA. We create a “gene pool” of random variations, and then we iterate the genetic algorithm (GA), using as fitness function a comparison between a calculated X-ray diffraction pattern and the X-ray images taken experimentally.
Imagine tossing out a thousand random guesses about what DNA looks like. For each guess, we could calculate what the X-ray diffraction pattern would look like given that particular molecular structure. Most of the time, it won’t look anything like the X-ray pictures we take in the laboratory, but a few of them will by happy accident look a little more like the real thing. This slight preference becomes the starting point for selection. We let our ideas breed, giving favor to those which perform best. The irresistible logic of Darwin goes to work.
(This is a bit of a perspective shift. Instead of thinking like sane people do about DNA carrying genes, we’re considering an abstract sort of gene which defines the shape of a hypothetical DNA structure.)
If we’d invented fast and cheap computers before we knew about DNA — say, in some parallel Sliders world or steampunk fantasy where computers happened five decades sooner — this might well be how scientists would have tried to solve DNA. It requires much less cleverness, and correspondingly more computer time. As I mentioned before, people have applied this method to figuring out molecule shapes, although to my knowledge nobody has tried to “re-solve” DNA this way.
An interesting point: if our “genes” are molecular positions and our “phenotypes” are X-ray diffraction images, then it looks like we’ve got a non-trivial “morphology” between the two. Some “development” has to take place, although it rather happens “all at once.” It might be interesting to look at a GA in which structures are generated by a really non-trivial development process.
How about, say, the growth of neurons?
Continue reading Dendritic Evo-Devo
