DOCSLIB.ORG

On Mutation and Crossover in the Theory of Evolutionary Algorithms

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
On Mutation and Crossover in the Theory of Evolutionary Algorithms ON MUTATION AND CROSSOVER IN THE THEORY OF EVOLUTIONARY ALGORITHMS by James Neal Richter A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science MONTANA STATE UNIVERSITY Bozeman, Montana April, 2010 c Copyright by James Neal Richter 2010 All Rights Reserved ii APPROVAL of a dissertation submitted by James Neal Richter This dissertation has been read by each member of the dissertation committee and has been found to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is ready for submission to the Division of Graduate Education. Dr. John T. Paxton Approved for the Department of Computer Science Dr. John T. Paxton Approved for the Division of Graduate Education Dr. Carl A. Fox iii STATEMENT OF PERMISSION TO USE In presenting this dissertation in partial fulfillment of the requirements for a doc- toral degree at Montana State University, I agree that the Library shall make it available to borrowers under rules of the Library. I further agree that copying of this dissertation is allowable only for scholarly purposes, consistent with \fair use" as pre- scribed in the U.S. Copyright Law. Requests for extensive copying or reproduction of this dissertation should be referred to ProQuest Information and Learning, 300 North Zeeb Road, Ann Arbor, Michigan 48106, to whom I have granted \the exclusive right to reproduce and distribute my dissertation in and from microform along with the non-exclusive right to reproduce and distribute my abstract in any format in whole or in part." James Neal Richter April, 2010 iv DEDICATION This work is dedicated to my father. I miss you every day Dad. James Franklin Richter (February 24, 1957 - January 14, 2006). I also dedicate this work to my mothers Maureen, Francile and Geneal, my lovely wife Stephanie and our four children, James Zachary, Kathleen, Brooklyn and Marcus. v ACKNOWLEDGEMENTS Dr. John Paxton has been my academic advisor since 1994 when I began under- grad studies in Computer Science. His encouragement and advice over the years have been invaluable. Dr. Alden Wright of the University of Montana acted as the external co-chair of the dissertation committee. His mentorship, expertise and collaboration was instru- mental in following this research path. Since I began PhD studies I've gotten married, lost a father, gained four children, had three different jobs, helped reboot and sell a software company then joined the buyer's software company to tackle bigger challenges. As a result this work was often left idle in drawer. I would like to thank John and Alden for their encouragement and patience through all that. Thomas Jansen, Michael Vose, Jon Rowe, Richard Watson, Lothar Schmitt, and the late Ingo Wegener are also acknowledged for their encouragement and advice. I would also like to express gratitude to the participants and organizers of the Theory of Evolutionary Algorithms conference series at Schloss Dagstuhl International Con- ference and Research Center for Computer Science, where the seeds of this research were germinated. The Departments of Computer Science and Mathematics have my gratitude for the education and support provided. Thanks also go out to my industry co-workers and supervisors at RightNow, Others Online and the Rubicon Project. Thanks also to Michael Guerini, who helped edit and beat this text into better shape. vi TABLE OF CONTENTS 1. INTRODUCTION ........................................................................................1 1.1 Document Organization........................................................................2 1.2 Framework and Contributions ...............................................................3 2. INTRODUCTION TO EVOLUTIONARY ALGORITHMS .............................4 2.1 Evolutionary Algorithms.......................................................................4 2.1.1 The Simple Genetic Algorithm.........................................................6 2.2 Pre-History of Evolutionary Algorithms.................................................8 2.2.1 Birth of Genetic Algorithms .......................................................... 10 2.2.2 Synthesis of Evolutionary Computation.......................................... 11 2.2.3 Population Size............................................................................. 12 2.2.4 The Selection Operators ................................................................ 13 2.2.5 The Mutation Operator................................................................. 15 2.2.6 The Crossover Operator ................................................................ 15 2.3 Common Difficulties ........................................................................... 17 2.4 Operators and Parameter Setting ........................................................ 19 2.5 Relation to Metaheuristic Optimization ............................................... 21 2.6 How do EAs really work?.................................................................... 21 3. HISTORICAL REVIEW OF EVOLUTION AND EVOLUTIONARY THEO- RIES..........................................................................................................23 3.1 Theory of Evolution ........................................................................... 23 3.2 Historical Anticipation and Reception of Darwinism ............................. 24 3.3 Early Interpretations .......................................................................... 26 3.4 Genetics and The Chromosome ........................................................... 28 3.5 Mutationism ...................................................................................... 29 3.6 Selection, Recombination and Linkage ................................................. 31 3.7 Population Genetics ........................................................................... 35 3.8 Modern Evolutionary Synthesis ........................................................... 41 3.9 Chromosomes and DNA...................................................................... 43 3.10 Neutral Theory of Evolution ............................................................... 45 3.11 Views on Recombination..................................................................... 48 3.12 Punctuated Equilibria ........................................................................ 57 3.13 Statistical versus Dynamical Theories .................................................. 57 3.14 Overconfidence in Evolutionary Theory ............................................... 58 3.15 Current Status of the Synthesis........................................................... 59 3.16 Relevance to Evolutionary Algorithms ................................................. 60 vii TABLE OF CONTENTS { CONTINUED 4. REVIEW OF EVOLUTIONARY ALGORITHM THEORY...........................61 4.1 Introduction to EA Theory ................................................................. 61 4.1.1 Theoretical Population Genetics..................................................... 61 4.1.2 Reductionist Approaches ............................................................... 71 4.1.3 Schema Theorem and Building Block Hypothesis ............................ 73 4.1.4 Markov Models ............................................................................. 76 4.1.5 Infinite Population and Dynamical Systems Models......................... 78 4.1.6 Statistical Mechanics..................................................................... 80 4.1.7 Time Complexity Analysis............................................................. 81 4.1.8 No Free Lunch Theorems............................................................... 83 4.2 Crossover versus Mutation .................................................................. 84 4.2.1 Hard Functions for Mutation based EAs......................................... 84 4.2.2 Hard Functions for Crossover based EAs ........................................ 88 4.3 Genetic and Population Diversity ........................................................ 90 4.4 Detailed Review of Relevant Theory .................................................... 90 4.5 Detailed Review of Time Complexity Analysis ..................................... 91 4.5.1 The (µ + λ) EA ............................................................................ 93 4.5.2 The (1 + 1) EA............................................................................. 94 4.5.3 Metropolis Algorithms and Simulated Annealing Hillclimbers .......... 96 4.5.4 Comparing 1 vs µ Population EAs ............................................... 100 4.5.5 Analyzing Crossover.................................................................... 102 4.6 Detailed Review of the Infinite Population Model............................... 106 4.6.1 Selection-Mutation Models .......................................................... 109 4.6.2 Course Grained Models ............................................................... 111 4.7 Summary......................................................................................... 112 5. IGNOBLE TRAILS FOR CONSTANT POPULATION SIZE...................... 113 5.1 Introduction..................................................................................... 113 5.2 Minimal Population Evolutionary Algorithms..................................... 115 5.2.1 The Steady-State (2+1) EA......................................................... 115 5.2.2 The Steady-State (2+1)
Load more

Recommended publications
  • The Evolution of Imitation Without Cultural Transmission
    The Evolution of Imitation Without Cultural Transmission Lee Altenberg∗1, Susanne Still1, and Christopher J. Watkins2 1University of Hawai`i at M¯anoa 2Royal Holloway University of London July 22, 2021 Abstract The evolution and function of imitation in animal learning has always been associated with its crucial role in cultural transmission and cultural evolution. Can imitation evolve in the absence of cultural transmission? We investigate a model in which imitation is unbundled from cultural transmission: an organism's adult phenotype is plastically altered by its juvenile experiences of genetically determined juvenile traits within its co- hort, and the only information transmitted between generations is genetic. Transmission of phenotypic information between generations is precluded by the population being semelparous with discrete, non-overlapping gen- erations. We find that during a period of directional selection towards a phenotypic optimum, natural selection favors modifiers which cause an organism to bias its plastic phenotype in the direction opposite to the mean phenotype of the population. As the population approaches the phenotypic optimum and shifts into stabilizing selection, selection on the modifier reverses and favors strong imitation of the population mean. Imitation can become so strong that it results in \genotype-phenotype disengagement" (Gonzalez et al., 2017) or even \hyper-imitation"|where the adult phenotype overshoots the mean phenotype. Hyper-imitation produces an evolutionary pathology, where the phenotype is driven away from the optimum, resulting in a drop in mean fitness, and the collapse of imitation. Repeated cycles of hyper-imitation and its collapse are observed. Genetic constraints on hyper-imitation will prevent these cycles.
    [Show full text]
  • Population Size and the Rate of Evolution
    Review Population size and the rate of evolution 1,2 1 3 Robert Lanfear , Hanna Kokko , and Adam Eyre-Walker 1 Ecology Evolution and Genetics, Research School of Biology, Australian National University, Canberra, ACT, Australia 2 National Evolutionary Synthesis Center, Durham, NC, USA 3 School of Life Sciences, University of Sussex, Brighton, UK Does evolution proceed faster in larger or smaller popu- mutations occur and the chance that each mutation lations? The relationship between effective population spreads to fixation. size (Ne) and the rate of evolution has consequences for The purpose of this review is to synthesize theoretical our ability to understand and interpret genomic varia- and empirical knowledge of the relationship between tion, and is central to many aspects of evolution and effective population size (Ne, Box 1) and the substitution ecology. Many factors affect the relationship between Ne rate, which we term the Ne–rate relationship (NeRR). A and the rate of evolution, and recent theoretical and positive NeRR implies faster evolution in larger popula- empirical studies have shown some surprising and tions relative to smaller ones, and a negative NeRR implies sometimes counterintuitive results. Some mechanisms the opposite (Figure 1A,B). Although Ne has long been tend to make the relationship positive, others negative, known to be one of the most important factors determining and they can act simultaneously. The relationship also the substitution rate [5–8], several novel predictions and depends on whether one is interested in the rate of observations have emerged in recent years, causing some neutral, adaptive, or deleterious evolution. Here, we reassessment of earlier theory and highlighting some gaps synthesize theoretical and empirical approaches to un- in our understanding.
    [Show full text]
  • Completely Derandomized Self-Adaptation in Evolution Strategies
    Completely Derandomized Self-Adaptation in Evolution Strategies Nikolaus Hansen [email protected] Technische Universitat¨ Berlin, Fachgebiet fur¨ Bionik, Sekr. ACK 1, Ackerstr. 71–76, 13355 Berlin, Germany Andreas Ostermeier [email protected] Technische Universitat¨ Berlin, Fachgebiet fur¨ Bionik, Sekr. ACK 1, Ackerstr. 71–76, 13355 Berlin, Germany Abstract This paper puts forward two useful methods for self-adaptation of the mutation dis- tribution – the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation dis- tributions are developed. Applying arbitrary, normal mutation distributions is equiv- alent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigor- ously, a completely derandomized self-adaptation scheme results, which adapts arbi- trary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation – utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their per- formances are comparable only on perfectly scaled functions. On badly scaled, non- separable functions usually a speed up factor of several orders of magnitude is ob- served. On moderately mis-scaled functions a speed up factor of three to ten can be expected. Keywords Evolution strategy, self-adaptation, strategy parameter control, step size control, de- randomization, derandomized self-adaptation, covariance matrix adaptation, evolu- tion path, cumulation, cumulative path length control.
    [Show full text]
  • Interpreting the History of Evolutionary Biology Through a Kuhnian Prism: Sense Or Nonsense?
    Interpreting the History of Evolutionary Biology through a Kuhnian Prism: Sense or Nonsense? Koen B. Tanghe Department of Philosophy and Moral Sciences, Universiteit Gent, Belgium Lieven Pauwels Department of Criminology, Criminal Law and Social Law, Universiteit Gent, Belgium Alexis De Tiège Department of Philosophy and Moral Sciences, Universiteit Gent, Belgium Johan Braeckman Department of Philosophy and Moral Sciences, Universiteit Gent, Belgium Traditionally, Thomas S. Kuhn’s The Structure of Scientific Revolutions (1962) is largely identified with his analysis of the structure of scientific revo- lutions. Here, we contribute to a minority tradition in the Kuhn literature by interpreting the history of evolutionary biology through the prism of the entire historical developmental model of sciences that he elaborates in The Structure. This research not only reveals a certain match between this model and the history of evolutionary biology but, more importantly, also sheds new light on several episodes in that history, and particularly on the publication of Charles Darwin’s On the Origin of Species (1859), the construction of the modern evolutionary synthesis, the chronic discontent with it, and the latest expression of that discon- tent, called the extended evolutionary synthesis. Lastly, we also explain why this kind of analysis hasn’t been done before. We would like to thank two anonymous reviewers for their constructive review, as well as the editor Alex Levine. Perspectives on Science 2021, vol. 29, no. 1 © 2021 by The Massachusetts Institute of Technology https://doi.org/10.1162/posc_a_00359 1 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/posc_a_00359 by guest on 30 September 2021 2 Evolutionary Biology through a Kuhnian Prism 1.
    [Show full text]
  • The Evolution of Dispersal in Random Environments and the Principle of Partial Control
    The Evolution of Dispersal in Random Environments and The Principle of Partial Control Lee Altenberg [email protected] Abstract If a genotype is distributed in space and its fitness varies with location, then selection will McNamara and Dall (2011) identified novel relationships change the spatial distribution of the genotype between the abundance of a species in different environ- through its effect on population demography. ments, the temporal properties of environmental change, This process can accumulate genotype members and selection for or against dispersal. Here, the mathemat- in locations to which they are well suited. This ics underlying these relationships in their two-environment accumulation by selection is the multiplier effect. model are investigated for arbitrary numbers of environ- ments. The effect they described is quantified as the It is possible, they discover, for the `multiplier effect’ to fitness-abundance covariance. The phase in the life cycle reverse | for there to be an excess of the population in where the population is censused is crucial for the impli- the worst habitats | when there is very rapid environ- cations of the fitness-abundance covariance. These rela- mental change. The environmental change they model tionships are shown to connect to the population genetics is a Markov process where are large number of patches literature on the Reduction Principle for the evolution of switch independently between two environments that pro- genetic systems and migration. Conditions that produce duce different growth rates for a population of organ- selection for increased unconditional dispersal are found to isms. They find that for moderate rates of environmental be new instances of departures from reduction described change, populations will have higher asymptotic growth by the \Principle of Partial Control" proposed for the evo- rates if they reduce their rate of unconditional disper- lution of modifier genes.
    [Show full text]
  • Use of Genomic Tools to Discover the Cause of Champagne Dilution Coat Color in Horses and to Map the Genetic Cause of Extreme Lordosis in American Saddlebred Horses
    University of Kentucky UKnowledge Theses and Dissertations--Veterinary Science Veterinary Science 2014 USE OF GENOMIC TOOLS TO DISCOVER THE CAUSE OF CHAMPAGNE DILUTION COAT COLOR IN HORSES AND TO MAP THE GENETIC CAUSE OF EXTREME LORDOSIS IN AMERICAN SADDLEBRED HORSES Deborah G. Cook University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cook, Deborah G., "USE OF GENOMIC TOOLS TO DISCOVER THE CAUSE OF CHAMPAGNE DILUTION COAT COLOR IN HORSES AND TO MAP THE GENETIC CAUSE OF EXTREME LORDOSIS IN AMERICAN SADDLEBRED HORSES" (2014). Theses and Dissertations--Veterinary Science. 15. https://uknowledge.uky.edu/gluck_etds/15 This Doctoral Dissertation is brought to you for free and open access by the Veterinary Science at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Veterinary Science by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known.
    [Show full text]
  • Implications of the Reduction Principle for Cosmological Natural Selection
    Implications of the Reduction Principle for Cosmological Natural Selection Lee Altenberg Associate Editor of BioSystems; Ronin Institute; [email protected] Smolin (1992) proposed that the fine-tuning problem for parameters of the Standard Model might be accounted for by a Darwinian process of universe reproduction — Cosmological Natural Selection (CNS) — in which black holes give rise to offspring universes with slightly altered parameters. The laws for variation and inheritance of the parameters are also subject to CNS if variation in transmission laws occurs. This is the strategy introduced by Nei (1967) to understand genetic transmission, through the evolutionary theory of modifier genes, whose methodology is adopted here. When mechanisms of variation themselves vary, they are subject to Feldman’s (1972) evolutionary Reduction Principle that selection favors greater faithfulness of replication. A theorem of Karlin (1982) allows one to generalize this principle beyond biological genetics to the unknown inheritance laws that would operate in CNS. The reduction principle for CNS is illustrated with a general multitype branching process model of universe creation containing competing inheritance laws. The most faithful inheritance law dominates the ensemble of universes. The Reduction Principle thus provides a mechanism to account for high fidelity of inheritance between universes. Moreover, it reveals that natural selection in the presence of variation in inheritance mechanisms has two distinct objects: maximization of both fitness and faithful inheritance. Tradeoffs between fitness and faithfulness open the possibility that evolved fundamental parameters are compromises, and not local optima to maximize universe production, in which case their local non-optimality may point to their involvement in the universe inheritance mechanisms.
    [Show full text]
  • Adaptive Tuning of Mutation Rates Allows Fast Response to Lethal Stress In
    Manuscript 1 Adaptive tuning of mutation rates allows fast response to lethal stress in 2 Escherichia coli 3 4 a a a a a,b 5 Toon Swings , Bram Van den Bergh , Sander Wuyts , Eline Oeyen , Karin Voordeckers , Kevin J. a,b a,c a a,* 6 Verstrepen , Maarten Fauvart , Natalie Verstraeten , Jan Michiels 7 8 a 9 Centre of Microbial and Plant Genetics, KU Leuven - University of Leuven, Kasteelpark Arenberg 20, 10 3001 Leuven, Belgium b 11 VIB Laboratory for Genetics and Genomics, Vlaams Instituut voor Biotechnologie (VIB) Bioincubator 12 Leuven, Gaston Geenslaan 1, 3001 Leuven, Belgium c 13 Smart Systems and Emerging Technologies Unit, imec, Kapeldreef 75, 3001 Leuven, Belgium * 14 To whom correspondence should be addressed: Jan Michiels, Department of Microbial and 2 15 Molecular Systems (M S), Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, box 16 2460, 3001 Leuven, Belgium, [email protected], Tel: +32 16 32 96 84 1 Manuscript 17 Abstract 18 19 While specific mutations allow organisms to adapt to stressful environments, most changes in an 20 organism's DNA negatively impact fitness. The mutation rate is therefore strictly regulated and often 21 considered a slowly-evolving parameter. In contrast, we demonstrate an unexpected flexibility in 22 cellular mutation rates as a response to changes in selective pressure. We show that hypermutation 23 independently evolves when different Escherichia coli cultures adapt to high ethanol stress. 24 Furthermore, hypermutator states are transitory and repeatedly alternate with decreases in mutation 25 rate. Specifically, population mutation rates rise when cells experience higher stress and decline again 26 once cells are adapted.
    [Show full text]
  • Mendelism, Plant Breeding and Experimental Cultures: Agriculture and the Development of Genetics in France Christophe Bonneuil
    Mendelism, plant breeding and experimental cultures: Agriculture and the development of genetics in France Christophe Bonneuil To cite this version: Christophe Bonneuil. Mendelism, plant breeding and experimental cultures: Agriculture and the development of genetics in France. Journal of the History of Biology, Springer Verlag, 2006, vol. 39 (n° 2 (juill. 2006)), pp.281-308. hal-00175990 HAL Id: hal-00175990 https://hal.archives-ouvertes.fr/hal-00175990 Submitted on 3 Oct 2007 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Mendelism, plant breeding and experimental cultures: Agriculture and the development of genetics in France Christophe Bonneuil Centre Koyré d’Histoire des Sciences et des Techniques, CNRS, Paris and INRA-TSV 57 rue Cuvier. MNHN. 75005 Paris. France Journal of the History of Biology, vol. 39, no. 2 (juill. 2006), 281-308. This is an early version; please refer to the original publication for quotations, photos, and original pagination Abstract The article reevaluates the reception of Mendelism in France, and more generally considers the complex relationship between Mendelism and plant breeding in the first half on the twentieth century. It shows on the one side that agricultural research and higher education institutions have played a key role in the development and institutionalization of genetics in France, whereas university biologists remained reluctant to accept this approach on heredity.
    [Show full text]
  • Assortative Mating (4 P.)
    Evolutionary Dynamics: Population Genetics and Ecological Models WS 2019/2020 course number: MA4453-KP05 Assignment 1 due: October 28 Mendel's experiments & Punnett squares (4 P.) In the lecture, we discussed Mendel's experiments with garden peas in which he considered one character (e.g. ower color or seed shape). In a second step, he considered two characters simultaneously. He started with two pure lines. One had yellow (dominant allele Y ), wrinkled (recessive allele r) seeds. The other had green (recessive allele y), round (dominant allele R) seeds. I.e. the genotypes of these pure lines were Y Y rr and yyRR. He crossed these two lines to obtain the F1 generation. He then obtained the F2 generation through selng of individuals from the F1 generation. Draw the corresponding Punnett square to determine the genotypes and phenotypes that are generated in this way. What are the proportions of the observed phenotypes if you determine proportions by counting the respective cells in the Punnett square? Mendel indeed observed these proportions. However, in 1905, William Bateson, Edith Re- becca Saunders, and Reginald Punnett performed a similar experiment in sweet pea plants. They simultaneously considered ower color and pollen shape and found dierent proportions than Mendel. How can this discrepancy be explained? You can read Mendel's original publication here (in German!): http: // www. deutschestextarchiv. de/ book/ view/ mendel_ pflanzenhybriden_ 1866? p= 14 Assortative mating (4 P.) In the lecture, we studied randomly mating individuals. However, in reality, mating is often non-random. E.g. individuals may prefer to mate with individuals that are similar to them with respect to some trait.
    [Show full text]
  • Adaptive Mutation Sequences Reproduced by Mismatch Repair Deficiency (Escherichia Coli/Muts/Dam/Spontaneous Mutation/Recombination) SIMONNE LONGERICH, ANNE M
    Proc. Natl. Acad. Sci. USA Vol. 92, pp. 12017-12020, December 1995 Biochemistry Adaptive mutation sequences reproduced by mismatch repair deficiency (Escherichia coli/MutS/Dam/spontaneous mutation/recombination) SIMONNE LONGERICH, ANNE M. GALLOWAY, REUBEN S. HARRIS, CINDY WONG, AND SUSAN M. ROSENBERG* Department of Biochemistry, 4-74 Medical Sciences Building, University of Alberta, Edmonton, AB Canada T6G 2H7 Communicated by Allan M. Campbell, Stanford University, Stanford, CA, September 15, 1995 (received for review June 15, 1995) ABSTRACT Adaptive reversions of a lac frameshift mu- mutation (16). The lagging-strand synthesis, which occurs tation in Escherichia coli are -1 deletions in small mononu- along with leading-strand synthesis in vegetative replication, cleotide repeats, whereas growth-dependent reversions are was suggested to produce the heterogeneity of the growth- heterogeneous. The adaptive mutations resemble instability of dependent reversion sequences by, e.g., incorrect joinings of simple repeats, which, in hereditary colon cancer, in yeast, Okazaki fragments (16). Data discourage the view that con- and in E. coli occurs in the absence of mismatch repair. The jugational transfer is a predominant source of adaptive rever- postulate that mismatch repair is disabled transiently during sion (and thus of its unusual sequence spectrum) (17, 18). adaptive mutation in E. coli is supported here by the demon- However, a possible caveat regarding such data has been stration that the growth-dependent mutation spectrum can be suggested (15), and it is also possible that transfer replication made indistinguishable from adaptive mutations by disallow- occurs without actual transfer (16, 18). ing mismatch repair during growth. Physiologically induced (iv) A fourth possibility is that both growth-dependent and mismatch repair deficiency could be an important mutagenic mechanism in in adaptive mutations result from essentially similar polymerase cancers and evolution.
    [Show full text]
  • The Role of Mutation Bias in Adaptive Molecular Evolution: Insights from 2 Convergent Changes in Protein Function 3
    bioRxiv preprint doi: https://doi.org/10.1101/580175; this version posted March 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 1 The role of mutation bias in adaptive molecular evolution: insights from 2 convergent changes in protein function 3 4 5 Jay F. Storz1*, Chandrasekhar Natarajan1, Anthony V. Signore1, Christopher C. Witt2,3, David M. 6 McCandlish4, Arlin Stoltzfus5* 7 8 1School of Biological Sciences, University of Nebraska, Lincoln, NE 68588 9 2Department of Biology, University of New Mexico, Albuquerque, NM 87131 10 3Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM 87131 11 4Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724 12 5Office of Data and Informatics, Material Measurement Laboratory, NIST, and Institute for Bioscience 13 and Biotechnology Research, Rockville, MD 20850 14 15 *Corresponding authors: Jay F. Storz ([email protected]), Arlin Stoltzfus ([email protected]) 16 17 18 Abstract 19 An underexplored question in evolutionary genetics concerns the extent to which mutational bias in the 20 production of genetic variation influences outcomes and pathways of adaptive molecular evolution. In the 21 genomes of at least some vertebrate taxa, an important form of mutation bias involves changes at CpG 22 dinucleotides: If the DNA nucleotide cytosine (C) is immediately 5’ to guanine (G) on the same coding 23 strand, then – depending on methylation status – point mutations at both sites occur at an elevated rate 24 relative to mutations at non-CpG sites.
    [Show full text]