Literature DB >> 22897313

The potential and flux landscape theory of evolution.

Feng Zhang1, Li Xu, Kun Zhang, Erkang Wang, Jin Wang.   

Abstract

We established the potential and flux landscape theory for evolution. We found explicitly the conventional Wright's gradient adaptive landscape based on the mean fitness is inadequate to describe the general evolutionary dynamics. We show the intrinsic potential as being Lyapunov function(monotonically decreasing in time) does exist and can define the adaptive landscape for general evolution dynamics for studying global stability. The driving force determining the dynamics can be decomposed into gradient of potential landscape and curl probability flux. Non-zero flux causes detailed balance breaking and measures how far the evolution from equilibrium state. The gradient of intrinsic potential and curl flux are perpendicular to each other in zero fluctuation limit resembling electric and magnetic forces on electrons. We quantified intrinsic energy, entropy and free energy of evolution and constructed non-equilibrium thermodynamics. The intrinsic non-equilibrium free energy is a Lyapunov function. Both intrinsic potential and free energy can be used to quantify the global stability and robustness of evolution. We investigated an example of three allele evolutionary dynamics with frequency dependent selection (detailed balance broken). We uncovered the underlying single, triple, and limit cycle attractor landscapes. We found quantitative criterions for stability through landscape topography. We also quantified evolution pathways and found paths do not follow potential gradient and are irreversible due to non-zero flux. We generalized the original Fisher's fundamental theorem to the general (i.e., frequency dependent selection) regime of evolution by linking the adaptive rate with not only genetic variance related to the potential but also the flux. We show there is an optimum potential where curl flux resulting from biotic interactions of individuals within a species or between species can sustain an endless evolution even if the physical environment is unchanged. We offer a theoretical basis for explaining the corresponding Red Queen hypothesis proposed by Van Valen. Our work provides a theoretical foundation for evolutionary dynamics.

Mesh:

Year:  2012        PMID: 22897313     DOI: 10.1063/1.4734305

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  20 in total

1.  Eddy current and coupled landscapes for nonadiabatic and nonequilibrium complex system dynamics.

Authors:  Kun Zhang; Masaki Sasai; Jin Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2013-08-26       Impact factor: 11.205

2.  Nonequilibrium landscape theory of neural networks.

Authors:  Han Yan; Lei Zhao; Liang Hu; Xidi Wang; Erkang Wang; Jin Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-21       Impact factor: 11.205

3.  Uncovering the underlying mechanism of cancer tumorigenesis and development under an immune microenvironment from global quantification of the landscape.

Authors:  Li Wenbo; Jin Wang
Journal:  J R Soc Interface       Date:  2017-06       Impact factor: 4.118

4.  Uncovering the mechanisms of Caenorhabditis elegans ageing from global quantification of the underlying landscape.

Authors:  Lei Zhao; Jin Wang
Journal:  J R Soc Interface       Date:  2016-11       Impact factor: 4.118

5.  Quantifying the landscape and kinetic paths for epithelial-mesenchymal transition from a core circuit.

Authors:  Chunhe Li; Tian Hong; Qing Nie
Journal:  Phys Chem Chem Phys       Date:  2016-06-21       Impact factor: 3.676

Review 6.  Perspectives on the landscape and flux theory for describing emergent behaviors of the biological systems.

Authors:  Jin Wang
Journal:  J Biol Phys       Date:  2021-11-25       Impact factor: 1.365

Review 7.  How Do Cells Adapt? Stories Told in Landscapes.

Authors:  Luca Agozzino; Gábor Balázsi; Jin Wang; Ken A Dill
Journal:  Annu Rev Chem Biomol Eng       Date:  2020-06-07       Impact factor: 11.059

8.  Wright-Fisher dynamics on adaptive landscape.

Authors:  Shuyun Jiao; Song Xu; Pengyao Jiang; Bo Yuan; Ping Ao
Journal:  IET Syst Biol       Date:  2013-10       Impact factor: 1.615

9.  Unifying deterministic and stochastic ecological dynamics via a landscape-flux approach.

Authors:  Li Xu; Denis Patterson; Ann Carla Staver; Simon Asher Levin; Jin Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-15       Impact factor: 11.205

10.  Equilibrium and non-equilibrium furanose selection in the ribose isomerisation network.

Authors:  Avinash Vicholous Dass; Thomas Georgelin; Frances Westall; Frédéric Foucher; Paolo De Los Rios; Daniel Maria Busiello; Shiling Liang; Francesco Piazza
Journal:  Nat Commun       Date:  2021-05-12       Impact factor: 14.919

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