Abstract
While solutions to major scientific and medical problems are never perfect or complete, it is still reasonable to delineate cases where both have been essentially solved. For example, Darwin’s theory of natural selection provides a successful solution to the problem of biological adaptation, while the germ theory of infection solved the scientific problem of contagious disease. Likewise in the context of medicine, we have effectively solved the problem of contagious disease, reducing it to a minor cause of death and disability for almost everyone in countries with advanced medicine and adequate resources. Evolutionary biologists claim to have solved the scientific problem of aging: we explain it theoretically using Hamilton’s forces of natural selection; in experimental evolution we readily manipulate the onset, rate, and eventual cessation of aging by manipulating these forces. In this article, we turn to the technological challenge of solving the medical problem of aging. While we feel that the broad outlines of such a solution are clear enough starting from the evolutionary solution to the scientific problem of aging, we do not claim that we can give a complete or exhaustive plan for medically solving the problem of aging. But we are confident that biology and medicine will effectively solve the problem of aging within the next 50 years, providing Hamiltonian lifestyle changes, tissue repair, and genomic technological opportunities are fully exploited in public health practices, in medical practice, and in medical research, respectively.
Keywords: Death spiral, evolutionary biology, experimental evolution, genomics, Hamilton's forces of natural selection, Medawar.
Current Aging Science
Title:An Evolutionary and Genomic Approach to Challenges and Opportunities for Eliminating Aging
Volume: 7 Issue: 1
Author(s): Michael R. Rose, Grant A. Rutledge, Kevin H. Phung, Mark A. Phillips, Lee F. Greer and Laurence D. Mueller
Affiliation:
Keywords: Death spiral, evolutionary biology, experimental evolution, genomics, Hamilton's forces of natural selection, Medawar.
Abstract: While solutions to major scientific and medical problems are never perfect or complete, it is still reasonable to delineate cases where both have been essentially solved. For example, Darwin’s theory of natural selection provides a successful solution to the problem of biological adaptation, while the germ theory of infection solved the scientific problem of contagious disease. Likewise in the context of medicine, we have effectively solved the problem of contagious disease, reducing it to a minor cause of death and disability for almost everyone in countries with advanced medicine and adequate resources. Evolutionary biologists claim to have solved the scientific problem of aging: we explain it theoretically using Hamilton’s forces of natural selection; in experimental evolution we readily manipulate the onset, rate, and eventual cessation of aging by manipulating these forces. In this article, we turn to the technological challenge of solving the medical problem of aging. While we feel that the broad outlines of such a solution are clear enough starting from the evolutionary solution to the scientific problem of aging, we do not claim that we can give a complete or exhaustive plan for medically solving the problem of aging. But we are confident that biology and medicine will effectively solve the problem of aging within the next 50 years, providing Hamiltonian lifestyle changes, tissue repair, and genomic technological opportunities are fully exploited in public health practices, in medical practice, and in medical research, respectively.
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Cite this article as:
Rose R. Michael, Rutledge A. Grant, Phung H. Kevin, Phillips A. Mark, Greer F. Lee and Mueller D. Laurence, An Evolutionary and Genomic Approach to Challenges and Opportunities for Eliminating Aging, Current Aging Science 2014; 7 (1) . https://dx.doi.org/10.2174/1874609807666140521110314
DOI https://dx.doi.org/10.2174/1874609807666140521110314 |
Print ISSN 1874-6098 |
Publisher Name Bentham Science Publisher |
Online ISSN 1874-6128 |
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