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Our results also suggest an intriguing connection between the experimentally realizable system that we study and some provocative ideas concerning the competition between self-organization and selection in evolution. Populations can be considered to evolve on "tness landscapes due to mutations [51]. man has suggested the NK model (which characterizes the varying degrees of ruggedness of "tness landscapes) to study how the competition between the ability to self-organize and selection in#uences the manner in which populations evolve [51].
Erent statistical patterns to illustrate a more complex separation than that depicted in Fig. 13. The surfaces are generated using a lattice where the identity of each site can be A, B, C or neutral. For all cases, in the MC simulations that generate statistically patterned surfaces the interaction energies (< , where i GH and j denote type of site) are symmetric and equal in magnitude, with the neutral sites being Fig. 15. Statistically patterned surface with three types (depicted in light grey, dark grey and black) of sites.
Chakraborty / Physics Reports 342 (2001) 1}61 sequences. This speculation can be explored by studying whether an ensemble of statistically pattern matched sequences can evolve by mutations to a few speci"c sequences. This type of research may also prove useful for applications. Given a particular surface pattern, can we start from the ensemble of statistically pattern matched sequences and systematically design speci"c types of sequences that would bind most e$ciently? These issues can be addressed by using sequence space Monte-Carlo annealing of DHP sequences.