REVIEW: Digital Genetics, Variation, Evolvability, and the Evolution of Programmed Aging

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T. C. Goldsmith^1,a

¹Azinet LLC, Box 239 Crownsville, MD 21032 USA

Received June 7, 2019; Revised September 2, 2019; Accepted September 2, 2019

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A major unresolved issue in gerontology concerns the evolutionary nature of senescence: Is aging caused by genetically programmed evolved mechanisms because limiting individual lifespan increases a population’s ability to survive and grow? Or is aging non-programmed because aging reduces an individual’s ability to survive and reproduce? There has been little disagreement with the many proposed population benefits of senescence, but evolution theory as described by Darwin and currently taught is very individual-oriented and until recently, programmed aging has been widely thought to be theoretically impossible. However, genetics discoveries have exposed issues with traditional theory that support population-driven evolution and programmed aging. In particular, as described in this article, the discovery that biological inheritance involves the transmission of information in digital form between parent and descendant of any organism strongly supports population-oriented evolution concepts and dependent programmed aging theories. A related issue concerns evolvability. Traditional theory assumes that the ability to evolve (evolvability) and involving mutations and natural selection is an inherent property of life. Evolvability theories suggest that evolvability in complex species is instead mainly itself the result of evolved traits and that such traits can evolve even if individually adverse. Programmed aging theories based on evolvability suggest that aging has evolved because it increases evolvability causing a population benefit. This idea is also strongly supported by the digital nature of inheritance. The programmed vs. non-programmed issue is critical to medical research because the two concepts suggest that very different biological mechanisms are responsible for aging and most instances of age-related disease.

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KEY WORDS: senescence, gerontology, genetics, genomic design, digital systems

DOI: 10.1134/S0006297919120046

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