Chase W. Nelson and John C. Sanford (2013) Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection. Biological Information: pp. 338-368.
Section Two: Biological Information and Genetic Theory: Introductory Comments
Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection
Chase W. Nelson
Rainbow Technologies, Inc., 877 Marshall Rd., Waterloo, NY 13165, USA
John C. Sanford
Department of Horticulture, NYSAES, Cornell University, Geneva, NY 14456, USA
Computational evolution experiments using the population genetics simulation Mendel's Accountant have suggested that deleterious mutation accumulation may pose a threat to the long-term survival of many biological species. By contrast, experiments using the program Avida have suggested that purifying selection is extremely effective and that novel genetic information can arise via selection for high-impact beneficial mutations. The present study shows that these approaches yield seemingly contradictory results only because of disparate parameter settings. Both agree when similar settings are used, and both reveal a net loss of genetic information under biologically relevant conditions. Further, both approaches establish the existence of three potentially prohibitive barriers to the evolution of novel genetic information: (1) the selection threshold and resulting genetic decay; (2) the waiting time to beneficial mutation; and (3) the pressure of reductive evolution, i.e., the selective pressure to shrink the genome and disable unused functions. The adequacy of mutation and natural selection for producing and sustaining novel genetic information cannot be properly assessed without a careful study of these issues.
Keywords: Avida; digital organisms; experimental evolution; genetic entropy; irreducible complexity; Mendel's Accountant; reductive evolution; selection threshold; waiting time to beneficial mutation
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