Fitness flux and ubiquity of adaptive evolution

Natural selection favors fitter variants in a population, but actual evolutionary processes may decrease fitness by mutations and genetic drift. How is the stochastic evolution of molecular biological systems shaped by natural selection? Here, we derive a theorem on the fitness flux in a population, defined as the selective effect of its genotype frequency changes. The fitness-flux theorem generalizes Fisher’s fundamental theorem of natural selection to evolutionary processes including mutations, genetic drift, and time-dependent selection. It shows that a generic state of populations is adaptive evolution: there is a positive fitness flux resulting from a surplus of beneficial over deleterious changes. In particular, stationary nonequilibrium evolution processes are predicted to be adaptive. Under specific nonstationary conditions, notably during a decrease in population size, the average fitness flux can become negative. We show that these predictions are in accordance with experiments in bacteria and bacteriophages and with genomic data in Drosophila. Our analysis establishes fitness flux as a universal measure of adaptation in molecular evolution.

[1]  M. Kimura Stochastic processes and distribution of gene frequencies under natural selection. , 1955, Cold Spring Harbor symposia on quantitative biology.

[2]  M. Kimura On the change of population fitness by natural selection2 3 , 1958, Heredity.

[3]  George R. Price,et al.  Selection and Covariance , 1970, Nature.

[4]  G. Price Fisher's ‘fundamental theorem’ made clear , 1972, Annals of human genetics.

[5]  Y. Iwasa,et al.  Free fitness that always increases in evolution. , 1988, Journal of theoretical biology.

[6]  W J Ewens,et al.  An interpretation and proof of the Fundamental Theorem of Natural Selection. , 1989, Theoretical population biology.

[7]  T. Nagylaki Rate of evolution of a character without epistasis. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. Kreitman,et al.  Adaptive protein evolution at the Adh locus in Drosophila , 1991, Nature.

[9]  J. Gillespie,et al.  Substitution processes in molecular evolution. I. Uniform and clustered substitutions in a haploid model. , 1993, Genetics.

[10]  B. Sinervo,et al.  The rock–paper–scissors game and the evolution of alternative male strategies , 1996, Nature.

[11]  Luca Peliti,et al.  Introduction to the statistical theory of Darwinian evolution , 1997, cond-mat/9712027.

[12]  C. Jarzynski Nonequilibrium Equality for Free Energy Differences , 1996, cond-mat/9610209.

[13]  A. Halpern,et al.  Evolutionary distances for protein-coding sequences: modeling site-specific residue frequencies. , 1998, Molecular biology and evolution.

[14]  L. Chao,et al.  Evolution by small steps and rugged landscapes in the RNA virus phi6. , 1999, Genetics.

[15]  G. Crooks Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[16]  K. Lamb The Genetical Theory of Natural Selection A Complete Variorum Edition , 2000 .

[17]  L. Cook The Genetical Theory of Natural Selection — A Complete Variorum Edition , 2000, Heredity.

[18]  T. Jukes,et al.  The neutral theory of molecular evolution. , 2000, Genetics.

[19]  B. Drossel Biological evolution and statistical physics , 2001, cond-mat/0101409.

[20]  M. Feldman,et al.  Local dispersal promotes biodiversity in a real-life game of rock–paper–scissors , 2002, Nature.

[21]  Yoichiro Ito,et al.  On the relation between fluctuation and response in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Johannes Berg,et al.  Adaptive evolution of transcription factor binding sites , 2003, BMC Evolutionary Biology.

[23]  Udo Seifert Entropy production along a stochastic trajectory and an integral fluctuation theorem. , 2005, Physical review letters.

[24]  T. Ohta Population size and rate of evolution , 1972, Journal of Molecular Evolution.

[25]  P. Andolfatto Adaptive evolution of non-coding DNA in Drosophila , 2005, Nature.

[26]  J. Ross,et al.  Fisher's theorems for multivariable, time- and space-dependent systems, with applications in population genetics and chemical kinetics. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[27]  S. Leibler,et al.  Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments , 2005, Science.

[28]  Richard A. Watson,et al.  PERSPECTIVE:SIGN EPISTASIS AND GENETIC CONSTRAINT ON EVOLUTIONARY TRAJECTORIES , 2005 .

[29]  C. Jarzynski,et al.  Path-integral analysis of fluctuation theorems for general Langevin processes , 2006, cond-mat/0605471.

[30]  Nigel F. Delaney,et al.  Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins , 2006, Science.

[31]  D. J. Kiviet,et al.  Empirical fitness landscapes reveal accessible evolutionary paths , 2007, Nature.

[32]  Michael M. Desai,et al.  The Speed of Evolution and Maintenance of Variation in Asexual Populations , 2007, Current Biology.

[33]  M. Lässig,et al.  Adaptations to fluctuating selection in Drosophila , 2007, Proceedings of the National Academy of Sciences.

[34]  D. Petrov,et al.  Genomewide Spatial Correspondence Between Nonsynonymous Divergence and Neutral Polymorphism Reveals Extensive Adaptation in Drosophila , 2007, Genetics.

[35]  Ville Mustonen,et al.  Energy-dependent fitness: A quantitative model for the evolution of yeast transcription factor binding sites , 2008, Proceedings of the National Academy of Sciences.

[36]  M. Lässig,et al.  Molecular evolution under fitness fluctuations. , 2008, Physical review letters.

[37]  S. Rice A stochastic version of the Price equation reveals the interplay of deterministic and stochastic processes in evolution , 2008, BMC Evolutionary Biology.

[38]  M. Lässig,et al.  From fitness landscapes to seascapes: non-equilibrium dynamics of selection and adaptation. , 2009, Trends in genetics : TIG.

[39]  Jeffrey E. Barrick,et al.  Genome evolution and adaptation in a long-term experiment with Escherichia coli , 2009, Nature.

[40]  V. Pande,et al.  On the application of statistical physics to evolutionary biology. , 2009, Journal of theoretical biology.

[41]  J. Krug,et al.  Exploring the Effect of Sex on Empirical Fitness Landscapes , 2009, The American Naturalist.

[42]  H. Girardey,et al.  Trajectories , 2009, Handbook of Critical Agrarian Studies.