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Biological Senescence: Loss of Integration and Resilience

Published online by Cambridge University Press:  29 November 2010

F. Eugene Yates  and
Laurel A. Benton
F. Eugene Yates
Affiliation:
University of California, Los Angeles
Laurel A. Benton
Affiliation:
University of California, Los Angeles
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Abstract

The flow of time can be conceptualized either as a cycle or an arrow. We offer a combined view: a helix. Chronological age (geophysical time reference) is not necessarily identical to biological age (internal time reference), and aging does not necessarily imply senescence. A new scheme of senescence, based on homeodynamics (nonlinear mechanics and nonequilibrium thermodynamics), is introduced as a plausible physical basis for understanding senescence. We propose that energy throughput, initially constructive of forms and functions, becomes destructive once most of the available degrees of freedom have been “frozen out” by the construction. Senescence becomes manifested at that point.

Résumé

Le cours du temps peut être conceptualisé selon un modèle cyclique ou linéaire. Nous présentons une perspective combinant ces deux idées, soit un modèle spiral. L'âge chronologique (référence de temps géophysique) n'est pas nécessairement identique à l'âge biologique (référence de temps interne), et le vieillissement ne suggère pas nécessairement la sénescence. Un nouveau schéma de sénescence, fondé sur l'homéodynamique (mécanique non linéaire et thermodynamique de non équilibre), est présenté comme un fondement physique plausible servant à comprendre la sénescence. Nous proposons que la production d'énergie, matériaux initial pour la construction des formes et des fonctions, devient défavorable lorsque la majorité des degrés de liberté ont été «immobilisés» par la construction. C'est à ce moment que la sénescence se manifeste.

Keywords

SenescenceHomeodynamicsStabilityIntegration (Vertical and Horizontal)Death (System and Component Types)Complexity.Sénescencehoméodynamiquestabilitéintégration verticale et horizontalemort du système et de ses composantescomplexité.
Type
Articles
Information
Canadian Journal on Aging / La Revue canadienne du vieillissement , Volume 14 , Issue 1: Models of Aging , Printemps/Spring 1995 , pp. 106 - 120
Copyright
Copyright © Canadian Association on Gerontology 1995

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References

Barham, J. (1993). Reply to critics. Journal of Social and Biological Structures, 16.Google Scholar
Cannon, W.B. (1929). Organization for physiological homeostasis. Physiological Reviews, 9, 399–431.Google Scholar
Cunningham, W.R., & Birren, J.E. (1980). Age changes in the factor structure of intellectual abilities in adulthood and old age. Education and Psychological Measurement, 40, 271–290.CrossRefGoogle Scholar
Edelman, G.M. (1988). Topobiology. New York: Basic Books.Google Scholar
Finch, CE. (1990). Longevity, Senescence, and the Genome. Chicago: University of Chicago Press.Google Scholar
Fries, J.F., & Crapo, L.M. (1981). Vitality and Aging. San Francisco: W.H. Freeman and Company.Google Scholar
Gould, S.J. (1987). Time's Arrow, Time's Cycle: Myth and Metaphor in the Discovery of Geological Time. Cambridge: Harvard University Press.Google Scholar
Hawking, S.W. (1988). A Brief History of Time: From the Big Bang to Black Holes. New York: Bantam Books.Google Scholar
Holland, J.H. (1981). Genetic algorithms and adaptation. Technical Report 34, Department of Cognitive Sciences, University of Michigan, Ann Arbor.Google Scholar
Hopfield, J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Science of the United States of America, 79, 2554–2563.CrossRefGoogle ScholarPubMed
Iberall, A.S. (1972). Toward a General Science of Viable Systems. New York: McGraw-Hill.Google Scholar
Iberall, A.S., & Soodak, H. (1978). Physical basis for complex systems — some propositions relating levels of organization. Collective Phenomena, 3, 9–24.Google Scholar
Iberall, A.S., & Soodak, H. (1987). A physics for complex systems. In Yates, F.E. (Ed.), Self-Organizing Systems: The Emergence of Order (pp. 499–520). New York: Plenum Press.CrossRefGoogle Scholar
Kaplan, D.T., Furman, M.L., Pincus, S.M., Ryan, S.M., Lipsitz, L.A., & Goldberger, A.L. (1991). Aging and the complexity of cardiovascular dynamics. Biophysical Journal, 59, 945–949.CrossRefGoogle ScholarPubMed
Kauffman, S.A. (1993). The Origins of Order. New York: Oxford University Press.CrossRefGoogle Scholar
Kenyon, G.M., Birren, J.E., & Schroots, J.J.F. (Eds.). (1991). Metaphors of Aging in Science and the Humanities. New York: Springer Publishing Company.Google Scholar
Leuchter, A.F., Spar, J.E., Walter, D.O., & Weiner, H. (1987). Electroencephalo graphic spectra and coherence in the diagnosis of Alzheimer's-type and multi infarct dementia. Archives of General Psychiatry, 44, 993–998.CrossRefGoogle Scholar
Lipsitz, L.A. & Goldberger, A.L. (1992). Loss of “complexity” and aging. Journal of the American Medical Association, 267, 1806–1809.Google Scholar
Lovelock, J. (1988). The Ages of Gaia. New York: Norton.Google Scholar
Medawar, P.B. (1952). An Unsolved Problem of Biology. London: H.K. Lewis.Google Scholar
Mehlberg, H. (1980). Time, Causality and the Quantum Theory. Volume 1: Causal Theory of Time. Volume 2: Time in a Quantized Universe (English translation). Volume 19 in the Boston Studies in the Philosophy of Science, Cohen, R.S. & Wartofsky, M.W. (Eds.). Boston: D. Reidel Publishing Company.Google Scholar
Moody, H.R. (1988). Toward a critical gerontology: The contribution of the humanities to the theories of aging. In Birren, J.E. & Bengtson, V.L. (Eds.), Emergent Theories of Aging (pp. 19–40). New York: Springer Publishing Company.Google Scholar
Murphy, E.A. (1982). Muddling, meddling, and modeling. In Anderson, V.E., Anderson, V.E., Penry, J.K., & Sing, C.R. (Eds.), Genetic Basis of Epilepsy (pp. 333–348). New York: Raven Press.Google Scholar
Penrose, R. (1994). Shadows of the Mind: A Search for the Missing Science of Consciousness. New York: Oxford University Press.Google Scholar
Pincus, S.M. (1994). Greater signal regularity may indicate increased system isolation. Mathematical Biosciences (in press.)CrossRefGoogle Scholar
Prigogine, I. (1980). From Being to Becoming: Time and Complexity in the Physical Sciences. San Francisco: W.H. Freeman & Co.Google Scholar
Richardson, I.W., & Rosen, R. (1979). Aging and the metrices of time. Journal of Theoretical Biology, 79, 415–423.Google Scholar
Rössler, O.E. (1987). Chaos in coupled optimizers. In Koslow, S.H., Mandeli, A.J., & Shlesinger, M.F. (Eds.), Perspectives in Biological Dynamics and Theoretical Medicine (pp. 229–240). New York: Annals of the New York Academy of Science, 504.Google Scholar
Sacher, G.A. (1980). Theory in gerontology, part I. Annual Reviews of Gerontology and Geriatrics, 1, 3–25.Google Scholar
Sacher, G.A., & Duffy, P.H. (1979). Genetic relation of lifespan to metabolic rate for inbred mouse strains and their hybrids. Federation Proceedings, 38, 184–189.Google Scholar
Schneider, E.D., & Kay, J.J. (1994). Life as a manifestation of the Second Law of Dynamics. Mathematical and Computer Modelling, 19, 25–48.Google Scholar
Soodak, H., & Iberall, A.S. (1978). Homeokinetics: A physcial science for complex systems. Science, 201, 579–582.CrossRefGoogle Scholar
Stear, E.B. (1987). Control paradigms and self-organization in living systems. In Yates, F.E. (Ed.), Self-Organizing Systems and the Emergence of Order (pp. 351–397). New York: Plenum Press.Google Scholar
Thorn, R. (1980). Halte au hasard, silence au bruit. Le Débat, 6, 119–132.Google Scholar
Waldrop, M.M. (1992). Complexity: The Emerging Science at the Edge of Order and Chaos. New York: Simon and Schuster.Google Scholar
White, K., Grether, M.E., Abrams, J.M., Young, L., Farrell, K., & Steller, H. (1994). Genetic control of programmed cell death in Drosophila. Science, 264, 677–683.Google Scholar
Yates, F.E. (1982a). Outline of a physical theory of physiological systems. Canadian Journal of Physiology and Pharmacology, 60(3), 217–248.Google Scholar
Yates, F.E. (1982b). The dynamics of aging and time: How physical action implies social action. In Birren, J.E. & Bengtson, V.L. (Eds.), Emergent Theories of Aging (pp. 90–117). New York: Springer Publishing Company.Google Scholar
Yates, F.E. (1987). Self-Organizing Systems: The Emergence of Order. New York: Plenum Press.Google Scholar
Yates, F.E. (1988). The dynamics of aging and time: How physical action implies social action. In Birren, J.E. & Bengston, V.L. (Eds.), Emergent Theories of Aging (pp. 90–117). New York: Springer Publishing Company.Google Scholar
Yates, F.E. (1993). Self-organizing systems. In Boyd, C.A.R. & Noble, D. (Eds.), The Logic of Life (pp. 189–218). Oxford, England: Oxford University Press and the International Union of Physiological Sciences.Google Scholar
Yates, F.E. (1994). Order and complexity in dynamical systems: Homeodynamics as a generalized mechanics for biology. Mathematical and Computer Modelling, 19: 49–74CrossRefGoogle Scholar
Yates, F.E., & Benton, L.A. (1995). Loss of integration and stability with age: Theories and conjectures. In Masoro, E.J. (Ed.), Handbook of Physiology: Aging. New York: Oxford Press and the American Physiological Society (in press.)Google Scholar