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Information Theory, Inference, and Learning ... - Inference Group

Information Theory, Inference, and Learning ... - Inference Group

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Copyright Cambridge University Press 2003. On-screen viewing permitted. Printing not permitted. http://www.cambridge.org/0521642981You can buy this book for 30 pounds or $50. See http://www.inference.phy.cam.ac.uk/mackay/itila/ for links.19.6: Further exercises 279Exercise 19.3. [3C ] Investigate how fast sexual <strong>and</strong> asexual species evolve ifthey have a fitness function with interactions. For example, let the fitnessbe a sum of exclusive-ors of pairs of bits; compare the evolving fitnesseswith those of the sexual <strong>and</strong> asexual species with a simple additive fitnessfunction.Furthermore, if the fitness function were a highly nonlinear function of thegenotype, it could be made more smooth <strong>and</strong> locally linear by the Baldwineffect. The Baldwin effect (Baldwin, 1896; Hinton <strong>and</strong> Nowlan, 1987) has beenwidely studied as a mechanism whereby learning guides evolution, <strong>and</strong> it couldalso act at the level of transcription <strong>and</strong> translation. Consider the evolution ofa peptide sequence for a new purpose. Assume the effectiveness of the peptideis a highly nonlinear function of the sequence, perhaps having a small isl<strong>and</strong>of good sequences surrounded by an ocean of equally bad sequences. In anorganism whose transcription <strong>and</strong> translation machinery is flawless, the fitnesswill be an equally nonlinear function of the DNA sequence, <strong>and</strong> evolutionwill w<strong>and</strong>er around the ocean making progress towards the isl<strong>and</strong> only by ar<strong>and</strong>om walk. In contrast, an organism having the same DNA sequence, butwhose DNA-to-RNA transcription or RNA-to-protein translation is ‘faulty’,will occasionally, by mistranslation or mistranscription, accidentally produce aworking enzyme; <strong>and</strong> it will do so with greater probability if its DNA sequenceis close to a good sequence. One cell might produce 1000 proteins from theone mRNA sequence, of which 999 have no enzymatic effect, <strong>and</strong> one does.The one working catalyst will be enough for that cell to have an increasedfitness relative to rivals whose DNA sequence is further from the isl<strong>and</strong> ofgood sequences. For this reason I conjecture that, at least early in evolution,<strong>and</strong> perhaps still now, the genetic code was not implemented perfectly but wasimplemented noisily, with some codons coding for a distribution of possibleamino acids. This noisy code could even be switched on <strong>and</strong> off from cellto cell in an organism by having multiple aminoacyl-tRNA synthetases, somemore reliable than others.Whilst our model assumed that the bits of the genome do not interact,ignored the fact that the information is represented redundantly, assumedthat there is a direct relationship between phenotypic fitness <strong>and</strong> the genotype,<strong>and</strong> assumed that the crossover probability in recombination is high, I believethese qualitative results would still hold if more complex models of fitness <strong>and</strong>crossover were used: the relative benefit of sex will still scale as √ G. Only insmall, in-bred populations are the benefits of sex expected to be diminished.In summary: Why have sex? Because sex is good for your bits!Further readingHow did a high-information-content self-replicating system ever emerge in thefirst place? In the general area of the origins of life <strong>and</strong> other tricky questionsabout evolution, I highly recommend Maynard Smith <strong>and</strong> Száthmary(1995), Maynard Smith <strong>and</strong> Száthmary (1999), Kondrashov (1988), MaynardSmith (1988), Ridley (2000), Dyson (1985), Cairns-Smith (1985), <strong>and</strong>Hopfield (1978).19.6 Further exercisesExercise 19.4. [3 ] How good must the error-correcting machinery in DNA replicationbe, given that mammals have not all died out long ago? Estimate theprobability of nucleotide substitution, per cell division. [See Appendix C.4.]

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