Phenotype mapping

The truth tables of two Sblocks. (Haddow, P.C. and Tufte, G. [2001] Bridging the genotype-phenotype mapping for digital FPGAs. In proceedings of the Third NASA/DoDWorkshop on Evolvable Hardware, IEEE Computer Society.)... 

Fig. 1. Partitioning of the complex phenomenon of evolution into three simpler processes. Population dynamics is tantamount to population genetics of asexually reproducing haploid populations or chemical kinetics of polynucleotide replication. Population support dynamics describes the migration of populations in genotype or sequence space during the course of evolutionary optimization. Genotype-phenotype mapping deals with the unfolding of a phenotype under the conditions and constraints provided by the environment. It represents the major source of complexity in evolution.

Fig. 2.3. Sequence space and genotype - phenotype mappings. Mapping genotypes onto phenotypes and into fitness values. The sketch shows a map from sequence (or genotype) space onto phenotype space, as described in the text, and further into the real numbers resulting in fitness values assigned in two steps to the indi-...

Neutral networks [41] represent more or less the basic and most important feature of genotype-phenotype mappings. Although protein structure and function has been discussed with respect to neutrality for a very long time, direct evidence for neutrality and neutral networks came only recently from empirical potentials and neural network studies [58, 59]. Other investigations on protein foldability landscapes are in general agreement with the existence of extended neutral networks too [60, 61],... 

Variation and selection turns out to be an enormously potent tool for improvement also in vitro. Why this is so, does not trivially follow from the nature of random searches. The efficiency of Monte-Carlo methods may work very poorly as we know from other optimization problems. The intrinsic regularities of genotype-phenotype mappings with high degrees of neutrality and very wide scatter of the points in sequence space, which lead to the same or very similar solutions, are the clues to evolutionary success. 

Gunsalus, K. C., W-C. Yueh, P. MacMenamin, and F. Piano. 2004. RNAiDB and PhenoBlast Web tools for genome-wide phenotypic mapping projects. Nucleic Acids Res 32 D406-10. 

Fontana, W, Schuster, P. Shaping space The possible and the attainable in RNA genotype-phenotype mapping. J. Theor. Biol. 1998, 194(4), 491-515. 

Ogata et al. attacked the same nucleic acid conformation problem, but replaced the buildup scheme of Lucasius with a local filter that is equivalent to the use of a rotamer library. In both cases, these methods must deal with the fact that this is an underconstrained problem because several of the dihedrals have no NOEs associated with them. Schuster earlier treated a simple model of RNA to predict three-dimensional (3D) conformations, using a variant on a spin-glass Hamiltonian as his fitness function. The simple model used allowed for the analysis of the complexity of the fitness landscape, couched in terms of the genotype-to-phenotype mapping. 

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