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Emerging Diseases as Complex Adaptive Systems/Pilot.

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Final rept. 15 May 96-30 Sep 97,

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We designed and tested a computational model of emerging viruses that simulates the evolution of real biological viruses. Knowledge of viral molecular replication mechanisms was used to instruct the development of this genetic algorithm-based C language Virtual Virus vW. The model consists of populations of hundreds to thousands of variable length virtual virus genomes that replicate, mutate, recombine, and evolve. Each virus genome is composed of an artificial polynucleotide string in which arbitrary nucleotide triplets encode English letters rather than amino acids, and in which sequences are translated into words or phrases, rather than into polypeptides or proteins. The three wordphrases COREPROThIN, POLY- MERASE, and ENVELOPE, which can be present in any order on the string, together comprise the selected phenotype. Run-on and overlapping reading frames are permitted. Fitness is assigned to each string according to the encoded spelling score. Probability of replication at each generation is directly related to string fitness. VIV populations seeded with random strings regularly evolve terse, high spelling score genomes within a few hundred to a thousand generations. By systematically varying evolutionary operators in the VIV model we observed several reproducible features relevant to the evolution and emergence of biological viruses 1 adaptation fitness slope proceeds most rapidly at mutation rates close to one per genome, and falls off rapidly at rates either higher or lower than unity 2 when added to mutation, recombination in any form speeds adaptation, and 3 homologous recombination is superior to random cross-over recombination.

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  • Medicine and Medical Research

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