Coding and information theory (2nd ed.)
Coding and information theory (2nd ed.)
Artificial Life
Artificial Life
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Recurrence is the possibility of resulting in the same endpoint multiple times when a living system is allowed to evolve repeatedly starting from a given initial point. This concept is of concern to both evolutionary theoreticians and molecular biologists who use nucleic acid selection techniques to mimic biotic and computorial processes in the test tube. Using the continuous in vitro evolution methodology, many replicate experimental evolutionary lineages with populations of catalytic RNA were performed to gain insight into the parameters that could affect recurrence. The likelihood that the same genotype will result in parallel trials of an evolution experiment in vitro depends on several factors, including the phenotype under selection, the size and composition of the initial diverse pool of nucleic acids used in the experiment, the degree of mutation possible during the experiment, the shape of the fitness landscape through which the population evolves, and the strategies used to invoke selection and to search the landscape, among others. By considering these factors, it can be predicted that recurrence is more likely when a small, wild-type-based starting pool is used with efficient selection and search strategies involving little online mutagenesis within a rugged adaptive landscape with a strong local optimum. The recurrence experiments performed here on the 150-nucleotide ligase ribozyme demonstrate that it repeatedly jumps from one peak in a fitness landscape to another, apparently hurdling a deep fitness valley. These predictions can and should be tested by additional multiple replicates of actual evolution experiments in the laboratory.