Received September 5, 2018; Revision received September 16, 2018
Aging diminishes individual fitness, and aging could never evolve as an adaptive program according to the most prevalent model of evolutionary theory. On the other hand, some mechanisms of aging have been found to be conserved since the Cambrian explosion, and the physiology of aging sometimes looks like programmed self-destruction. Biostatisticians find evidence of an epigenetic aging clock, extending the clock that controls the growth and development into a realm of inexorably increasing mortality. These and other observations have suggested to some biologists that our understanding of aging is being constrained by restrictive evolutionary paradigms. Several computational models have been proposed; but evolution of an aging program requires group selection on a scale that goes beyond the theory of multilevel selection, a perspective that is already controversial. So, the question whether plausible models exist that can account for aging as a group-selected adaptation is central to our understanding of what aging is, where it comes from and, importantly, how anti-aging medicine might most propitiously be pursued. In a 2016 Aging Cell article, Kowald and Kirkwood reviewed computational models that evolve aging as an adaptation. They find fault with each of these models in turn, based on theory alone, and on this basis, they endorse the standing convention that aging must be understood in terms of trade-off models. But consideration of the corpus of experimental evidence creates a picture that stands in counterpoint to the conclusions of that review. Presented herein is a broad summary of that evidence, together with a description of one model that Kowald and Kirkwood omitted, the demographic theory of aging, which may be the most conservative, and therefore most plausible of the alternative evolutionary theories, and which is the subject of a book by the present author, published contemporaneously with Kowald and Kirkwood.
KEY WORDS: aging, evolution, programmed aging, simulation, computational model