Literature DB >> 17804030

Darwinian fitness.

Lloyd Demetrius1, Martin Ziehe.   

Abstract

The term Darwinian fitness refers to the capacity of a variant type to invade and displace the resident population in competition for available resources. Classical models of this dynamical process claim that competitive outcome is a deterministic event which is regulated by the population growth rate, called the Malthusian parameter. Recent analytic studies of the dynamics of competition in terms of diffusion processes show that growth rate predicts invasion success only in populations of infinite size. In populations of finite size, competitive outcome is a stochastic process--contingent on resource constraints--which is determined by the rate at which a population returns to its steady state condition after a random perturbation in the individual birth and death rates. This return rate, a measure of robustness or population stability, is analytically characterized by the demographic parameter, evolutionary entropy, a measure of the uncertainty in the age of the mother of a randomly chosen newborn. This article appeals to computational and numerical methods to contrast the predictive power of the Malthusian and the entropic principles. The computational analysis rejects the Malthusian model and is consistent with of the entropic principle. These studies thus provide support for the general claim that entropy is the appropriate measure of Darwinian fitness and constitutes an evolutionary parameter with broad predictive and explanatory powers.

Mesh:

Year:  2007        PMID: 17804030     DOI: 10.1016/j.tpb.2007.05.004

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  9 in total

1.  Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks.

Authors:  Jacques Demongeot; Mariem Jelassi; Hana Hazgui; Slimane Ben Miled; Narjes Bellamine Ben Saoud; Carla Taramasco
Journal:  Entropy (Basel)       Date:  2018-01-13       Impact factor: 2.524

2.  Selection against demographic stochasticity in age-structured populations.

Authors:  Max Shpak
Journal:  Genetics       Date:  2007-10-18       Impact factor: 4.562

Review 3.  Defining fitness in evolutionary models.

Authors:  Derek A Roff
Journal:  J Genet       Date:  2008-12       Impact factor: 1.166

4.  Genomic sweep and potential genetic rescue during limiting environmental conditions in an isolated wolf population.

Authors:  Jennifer R Adams; Leah M Vucetich; Philip W Hedrick; Rolf O Peterson; John A Vucetich
Journal:  Proc Biol Sci       Date:  2011-03-30       Impact factor: 5.349

5.  Entropy as a Robustness Marker in Genetic Regulatory Networks.

Authors:  Mustapha Rachdi; Jules Waku; Hana Hazgui; Jacques Demongeot
Journal:  Entropy (Basel)       Date:  2020-02-25       Impact factor: 2.524

6.  Evolutionary entropy: a predictor of body size, metabolic rate and maximal life span.

Authors:  Lloyd Demetrius; Stéphane Legendre; Peter Harremöes
Journal:  Bull Math Biol       Date:  2009-01-27       Impact factor: 1.758

7.  Evolutionary entropy determines invasion success in emergent epidemics.

Authors:  Christopher J Rhodes; Lloyd Demetrius
Journal:  PLoS One       Date:  2010-09-23       Impact factor: 3.240

8.  Calculation of the relative metastabilities of proteins in subcellular compartments of Saccharomyces cerevisiae.

Authors:  Jeffrey M Dick
Journal:  BMC Syst Biol       Date:  2009-07-18

9.  Age-related transcriptional changes in gene expression in different organs of mice support the metabolic stability theory of aging.

Authors:  Thore C Brink; Lloyd Demetrius; Hans Lehrach; James Adjaye
Journal:  Biogerontology       Date:  2008-11-23       Impact factor: 4.277

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.