title: Properties of biological mutation networks and their implications for ALife.
abstract: We report a study of networks constructed from mutation patterns observed in biology. These networks form evolutionary trajectories, which allow for both frequent substitution of closely related structures, and a small evolutionary distance between any two structures. These two properties define the small-world phenomenon. The mutation behavior between tokens in an evolvable artificial chemistry determines its ability to explore evolutionary space. This concept is underrepresented in previous work on string-based chemistries. We argue that small-world mutation networks will confer better exploration of the evolutionary space than either random or fully regular mutation strategies. We calculate network statistics from two data sets: amino acid substitution matrices, and codon-level single point mutations. The first class are observed data from protein alignments; while the second class is defined by the standard genetic code that is used to translate RNA into amino acids. We report a methodology for creating small-world mutation networks for artificial chemistries with arbitrary node count and connectivity. We argue that ALife systems would benefit from this approach, as it delivers a more viable exploration of evolutionary space.
authors.affiliation: University of York, York, UK.
authors.forename: Alastair P
authors.initials: AP
authors.lastname: Droop
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Hickinbotham
mesh_terms: D000465:Algorithms; D019943:Amino Acid Substitution / Q000235:genetics; D000596:Amino Acids / Q000235:genetics; D003062:Codon / Q000235:genetics; D019143:Evolution, Molecular; D005815:Genetic Code / Q000235:genetics; D008957:Models, Genetic; D009154:Mutation; D017354:Point Mutation / Q000235:genetics; D012313:RNA / Q000235:genetics; D016415:Sequence Alignment
chemical_list: D000596:Amino Acids; D003062:Codon; D012313:RNA