I haven’t made any headway on the Grey Lady’s Top 100 Books of the Year, partly because I’ve been too busy reading what comes down the arXivotubes. For example, take Giovanni Feverati and Fabio Musso’s recent e-print, “An evolutionary model with Turing machines” (arXiv:0711.3580, 22 November).
The development of a large non-coding fraction in eukaryotic DNA and the phenomenon of the code-bloat in the field of evolutionary computations show a striking similarity. This seems to suggest that (in the presence of mechanisms of code growth) the evolution of a complex code can’t be attained without maintaining a large inactive fraction. To test this hypothesis we performed computer simulations of an evolutionary toy model for Turing machines, studying the relations among fitness and coding/non-coding ratio while varying mutation and code growth rates. The results suggest that, in our model, having a large reservoir of non-coding states constitutes a great (long term) evolutionary advantage.
Taking the broad view, it’s interesting that a large mass of genetic information might not directly code for phenotypic features while still having a long-term adaptive advantage. (Thinking about fitness over multiple generations gives me a headache: the mapping from phenotype to fitness value isn’t constant over time. Cluster a whole bunch of predators together, and they kill off all the prey, changing their environment and thereby making their own phenotype unfit. Some people really care about this; I just know it makes me want to go back to neutrino physics.) A few details of note:
First, Feverati and Musso evolve their Turing machines to specified goals: they want a machine which ends its operation with a tape containing a particular sequence of zeros and ones. In different trials, two distinct goal tapes were used, a tape representing the first 100 prime numbers and a tape holding the bits after the radix point in the binary expansion of π. (These goals were chosen in part because a periodic distribution is an easy thing for a Turing machine to make. I suppose we’re talking about maximizing Kolmogorov complexity, though the authors don’t make that connection explicitly.)
Second, the only form of mutation in their simulation was point mutation. More specifically, they randomly changed each entry in a Turing machine’s description with some probability [tex]p_{\rm m}[/tex]. Gene duplication and other such mechanisms were not implemented. States are added with a certain probability [tex]p_{\rm i}[/tex] per timestep; these states are non-coding until a point mutation elsewhere establishes a call to them.
UPDATE (28 November): A real biologist offers comments below. The number of people who know more than I do about any given subject is awfully impressive!
UPDATE (1 December): More here.



