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      Kinetics of Muller's Ratchet from Adaptive Landscape Viewpoint

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          Abstract

          Background: The accumulation of deleterious mutations of a population directly contributes to the fate as to how long the population would exist. Muller's ratchet provides a quantitative framework to study the effect of accumulation. Adaptive landscape as a powerful concept in system biology provides a handle to describe complex and rare biological events. In this article we study the evolutionary process of a population exposed to Muller's ratchet from the new viewpoint of adaptive landscape which allows us estimate the single click of the ratchet starting with an intuitive understanding. Methods: We describe how Wright-Fisher process maps to Muller's ratchet. We analytically construct adaptive landscape from general diffusion equation. It shows that the construction is dynamical and the adaptive landscape is independent of the existence and normalization of the stationary distribution. We generalize the application of diffusion model from adaptive landscape viewpoint. Results: We develop a novel method to describe the dynamical behavior of the population exposed to Muller's ratchet, and analytically derive the decaying time of the fittest class of populations as a mean first passage time. Most importantly, we describe the absorption phenomenon by adaptive landscape, where the stationary distribution is non-normalizable. These results suggest the method may be used to understand the mechanism of populations evolution and describe the biological processes quantitatively.

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          Most cited references10

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          THE RELATION OF RECOMBINATION TO MUTATIONAL ADVANCE.

          J. Müller (1964)
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            Some Genetic Aspects of Sex

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              Laws in Darwinian Evolutionary Theory

              P Ao (2006)
              In the present article the recent works to formulate laws in Darwinian evolutionary dynamics are discussed. Although there is a strong consensus that general laws in biology may exist, opinions opposing such suggestion are abundant. Based on recent progress in both mathematics and biology, another attempt to address this issue is made in the present article. Specifically, three laws which form a mathematical framework for the evolutionary dynamics in biology are postulated. The second law is most quantitative and is explicitly expressed in the unique form of a stochastic differential equation. Salient features of Darwinian evolutionary dynamics are captured by this law: the probabilistic nature of evolution, ascendancy, and the adaptive landscape. Four dynamical elements are introduced in this formulation: the ascendant matrix, the transverse matrix, the Wright evolutionary potential, and the stochastic drive. The first law may be regarded as a special case of the second law. It gives the reference point to discuss the evolutionary dynamics. The third law describes the relationship between the focused level of description to its lower and higher ones, and defines the dichotomy of deterministic and stochastic drives. It is an acknowledgement of the hierarchical structure in biology. A new interpretation of Fisher's fundamental theorem of natural selection is provided in terms of the F-Theorem. The proposed laws are based on continuous representation in both time and population. Their generic nature is demonstrated through their equivalence to classical formulations. The present three laws appear to provide a coherent framework for the further development of the subject.
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                Author and article information

                Journal
                18 September 2011
                2011-09-21
                Article
                1109.3922
                6443cfb4-5097-4ccb-91b4-c09fa7dffbc2

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                2011 IEEE Conference on Systems Biology, pp: 27-32. Zhuhai, China, Sep 2-4
                6 pages, 3 figures; IEEE Conference on Systems Biology, 2011; ISBN 978-1-4577-1666-9
                q-bio.PE

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