Advertisement

Biochemistry (Moscow)

, Volume 83, Issue 12–13, pp 1504–1516 | Cite as

Can Aging Develop as an Adaptation to Optimize Natural Selection? (Application of Computer Modeling for Searching Conditions When the “Fable of Hares” Can Explain the Evolution of Aging)

  • A. V. Markov
  • M. A. Barg
  • E. Yu. YakovlevaEmail author
Article

Abstract

There are two points of view on the evolution of aging. The classical theory of aging suggests that natural selection does not efficiently eliminate mutations or alleles that are harmful to organisms at later age. Another hypothesis is that the genetic program of aging has evolved as an adaptation that contributes to the optimization of the evolutionary process. Academician V. P. Skulachev advocates the latter hypothesis, which he has illustrated with the “Fable of hares”. In this paper, we have used computer simulation to search for conditions when, according to the “Fable”, aging develops as an adaptation required for the evolution of useful traits. The simulation has shown that the evolutionary mechanism presented in the “Fable of hares” is only partially functional. We have found that under certain conditions, programmed deterioration of some organismal functions makes it possible to increase the efficiency of natural selection of other functions. However, we have not identified mechanisms that would ensure the distribution and support of genes of aging within the population.

Keywords

senescence evolution of aging “Fable of hares” adaptation natural selection simulation computer modeling 

Abbreviations

A

aging rate

C

intelligence

F

“intelligence fine”

TA

threshold age

V

running speed

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Jones, O. R., Scheuerlein, A., Salguero–Gomez, R., Camarda, C. G., Schaible, R., Casper, B. B., Dahlgren, J. P., Ehrlen, J., Garcia, M. B., Menges, E. S., Quintana–Ascencio, P. F., Caswell, H., Baudisch, A., and Vaupel, J. W. (2014) Diversity of ageing across the tree of life, Nature, 505, 169–173.CrossRefGoogle Scholar
  2. 2.
    Hamilton, W. D. (1966) The moulding of senescence by natural selection, J. Theor. Biol., 12, 12–45.CrossRefGoogle Scholar
  3. 3.
    Rose, M. (1991) Evolutionary Biology of Aging, Oxford University Press, Oxford.Google Scholar
  4. 4.
    Medawar, P. B. (1952) An Unsolved Problem of Biology, HK Lewis, London.Google Scholar
  5. 5.
    Williams, G. C. (1957) Pleiotropy, natural selection, and the evolution of senescence, Evolution, 11, 398–411.Google Scholar
  6. 6.
    Severin, F. F., and Skulachev, V. P. (2009) Programmed cell death as a target for struggle against aging of organism, Uspekhi Gerontol., 22, 37–48.Google Scholar
  7. 7.
    Skulachev, V. P. (2003) Aging and the programmed death phenomena, Top. Curr. Genet., 3, 191–238.CrossRefGoogle Scholar
  8. 8.
    Heredia, D., Sanz, V., Urquia, A., and Sandin, M. (2015) A systemic approach for modeling biological evolution using Parallel DEVS, Biosystems, 134, 56–70.CrossRefGoogle Scholar
  9. 9.
    Markov, M. A., and Markov, A. V. (2014) Computer simulation of the ontogeny of organisms with different types of symmetry, Paleontol. J., 48, 1143–1151.CrossRefGoogle Scholar
  10. 10.
    Markov, M. A., and Markov, A. V. (2011) Self–organization in ontogenesis of multicellular organisms: an experience of simulation modeling, Zh. Obshch. Biol., 5, 323–39.Google Scholar
  11. 11.
    Menshutkin, V. V., and Natochin, Y. V. (2008) Simulation modeling of the generation of multicellular animals, Paleontol. J., 2, 3–12.Google Scholar
  12. 12.
    Menshutkin, V. V. (2003) Computer simulation of different type of evolution process, Zh. Obshch. Biol., 4, 328–36.Google Scholar
  13. 13.
    Peck, S. L. (2004) Simulation as experiment: a philosophical reassessment for biological modeling, Trends Ecol. Evol., 10, 530–534.CrossRefGoogle Scholar
  14. 14.
    Chistyakov, V. A., Denisenko, D. V., and Bren, A. B. (2018) Presence of old individuals in a population accelerates and optimizes the process of selection: in silico experiments, Biochemistry (Moscow), 83, 159–168.CrossRefGoogle Scholar
  15. 15.
    Wright, S. (1932) The roles of mutation, inbreeding, cross–breeding and selection in evolution, in Proc. Sixth Int. Congr. of Genetics (Jone, D. F., ed.) Brooklyn Botanic Garden, Menasha, WI, pp. 356–366.Google Scholar
  16. 16.
    Crow, J. F., and Kimura, M. (1970) An Introduction to Population Genetics Theory, Harper and Row, N. Y.Google Scholar
  17. 17.
    Barton, N. H. (2000) Genetic hitchhiking, Philos. Trans. R. Soc. Lond. B Biol. Sci., 355, 1553–1562.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • A. V. Markov
    • 1
    • 2
  • M. A. Barg
    • 3
  • E. Yu. Yakovleva
    • 1
    Email author
  1. 1.Biological FacultyLomonosov Moscow State UniversityMoscowRussia
  2. 2.Paleontological InstituteRussian Academy of SciencesMoscowRussia
  3. 3.ABK Ltd.MoscowRussia

Personalised recommendations