Abstract
Independent populations often use the same phenotypic and genetic solutions to adapt to a selective challenge, suggesting that evolution is surprisingly repeatable. This observation has inspired a shift in focus for evolutionary biology towards predictive studies, but progress is impeded by a lack of insight into the causes for repeatability, which prevents tests of forecasting models outside the original biological systems. Experimental evolution with microbes could provide a way to identify the causes of repeated evolution, directly test forecasting ability and develop methodology, but a range of difficulties limits successful prediction. This chapter discusses the limitations on forecasting of experimental evolution, what can and cannot be predicted on different biological levels and why predictions will often fail. Focusing on experimental populations of bacteria, the importance of selection, mutational biases and genotype-to-phenotype maps in determining evolutionary outcomes is discussed, as well as the potential for including these factors in forecasting models. The chapter concludes with a discussion on the desired properties of experimental evolution models suitable for testing forecasting models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersson DI, Hughes D (2009) Gene amplification and adaptive evolution in bacteria. Annu Rev Genet 43:167–195. https://doi.org/10.1146/annurev-genet-102108-134805
Baek M, Park T, Heo L, Park C, Seok C (2017) GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure. Nucleic Acids Res 45:W320–W324. https://doi.org/10.1093/nar/gkx246
Bailey SF, Bataillon T (2016) Can the experimental evolution programme help us elucidate the genetic basis of adaptation in nature? Mol Ecol 25:203–218. https://doi.org/10.1111/mec.13378
Bailey SF, Blanquart F, Bataillon T, Kassen R (2017) What drives parallel evolution?: how population size and mutational variation contribute to repeated evolution. BioEssays 39:1–9. https://doi.org/10.1002/bies.201600176
Barrick JE, Lenski RE (2013) Genome dynamics during experimental evolution. Nat Rev Genet 14:827–839. https://doi.org/10.1038/nrg3564
Bennett DE et al (2009) Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PLoS One 4:e4724. https://doi.org/10.1371/journal.pone.0004724
Blank D, Wolf L, Ackermann M, Silander OK (2014) The predictability of molecular evolution during functional innovation. Proc Natl Acad Sci USA 111:3044–3049. https://doi.org/10.1073/pnas.1318797111
Blattner FR et al (1997) The complete genome sequence of Escherichia coli K-12. Science 277:1453–1462
Bloom JD, Labthavikul ST, Otey CR, Arnold FH (2006) Protein stability promotes evolvability. Proc Natl Acad Sci USA 103:5869–5874. https://doi.org/10.1073/pnas.0510098103
Blount ZD, Barrick JE, Davidson CJ, Lenski RE (2012) Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489:513–518. https://doi.org/10.1038/nature11514
Blount ZD, Lenski RE, Losos JB (2018) Contingency and determinism in evolution: replaying life’s tape. Science 362. https://doi.org/10.1126/science.aam5979
Bromberg Y, Rost B (2007) SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res 35:3823–3835. https://doi.org/10.1093/nar/gkm238
Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R (2013) WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation. BMC Genomics 14(Suppl 3):S6. https://doi.org/10.1186/1471-2164-14-s3-s6
Celniker G et al (2013) ConSurf: using evolutionary data to raise testable hypotheses about protein function. Isr J Chem 53:199–206. https://doi.org/10.1002/ijch.201200096
Choi Y, Chan AP (2015) PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31:2745–2747. https://doi.org/10.1093/bioinformatics/btv195
Cooper VS, Schneider D, Blot M, Lenski RE (2001) Mechanisms causing rapid and parallel losses of ribose catabolism in evolving populations of Escherichia coli B. J Bacteriol 183:2834–2841. https://doi.org/10.1128/JB.183.9.2834-2841.2001
Daegelen P, Studier FW, Lenski RE, Cure S, Kim JF (2009) Tracing ancestors and relatives of Escherichia coli B, and the derivation of B strains REL606 and BL21(DE3). J Mol Biol 394:634–643. https://doi.org/10.1016/j.jmb.2009.09.022
de Visser JA, Krug J (2014) Empirical fitness landscapes and the predictability of evolution. Nat Rev Genet 15:480–490. https://doi.org/10.1038/nrg3744
de Visser J, Elena SF, Fragata I, Matuszewski S (2018) The utility of fitness landscapes and big data for predicting evolution. Heredity (Edinb) 121:401–405. https://doi.org/10.1038/s41437-018-0128-4
Deatherage DE, Kepner JL, Bennett AF, Lenski RE, Barrick JE (2017) Specificity of genome evolution in experimental populations of Escherichia coli evolved at different temperatures. Proc Natl Acad Sci USA 114:E1904–E1912. https://doi.org/10.1073/pnas.1616132114
Dehouck Y, Kwasigroch JM, Gilis D, Rooman M (2011) PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform 12:151. https://doi.org/10.1186/1471-2105-12-151
Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8:610–618. https://doi.org/10.1038/nrg2146
Ferguson GC, Bertels F, Rainey PB (2013) Adaptive divergence in experimental populations of Pseudomonas fluorescens. V. Insight into the Niche specialist “Fuzzy Spreader” compels revision of the model Pseudomonas radiation genetics. https://doi.org/10.1534/genetics.113.154948
Fischer A, Vazquez-Garcia I, Illingworth CJR, Mustonen V (2014) High-definition reconstruction of clonal composition in cancer. Cell Rep 7:1740–1752. https://doi.org/10.1016/j.celrep.2014.04.055
Flowers JM, Hanzawa Y, Hall MC, Moore RC, Purugganan MD (2009) Population genomics of the Arabidopsis thaliana flowering time gene network. Mol Biol Evol 26:2475–2486. https://doi.org/10.1093/molbev/msp161
Gerrish PJ, Lenski RE (1998) The fate of competing beneficial mutations in an asexual population. Genetica 102–103:127–144
Gerstein AC, Lo DS, Otto SP (2012) Parallel genetic changes and nonparallel gene-environment interactions characterize the evolution of drug resistance in yeast. Genetics 192:241–252. https://doi.org/10.1534/genetics.112.142620
Goldstein BP (2014) Resistance to rifampicin: a review. J Antibiot (Tokyo) 67:625–630. https://doi.org/10.1038/ja.2014.107
Good BH, Rouzine IM, Balick DJ, Hallatschek O, Desai MM (2012) Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc Natl Acad Sci USA 109:4950–4955. https://doi.org/10.1073/pnas.1119910109
Good BH, McDonald MJ, Barrick JE, Lenski RE, Desai MM (2017) The dynamics of molecular evolution over 60,000 generations. Nature 551:45–50. https://doi.org/10.1038/nature24287
Gould SJ (1989) Wonderful life: the Burgess Shale and the nature of history, 1st edn. W.W. Norton, New York
Gullberg E, Cao S, Berg OG, Ilback C, Sandegren L, Hughes D, Andersson DI (2011) Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathog 7:e1002158. https://doi.org/10.1371/journal.ppat.1002158
Harpak A, Bhaskar A, Pritchard JK (2016) Mutation rate variation is a primary determinant of the distribution of allele frequencies in humans. PLoS Genet 12:e1006489. https://doi.org/10.1371/journal.pgen.1006489
Herron MD, Doebeli M (2013) Parallel evolutionary dynamics of adaptive diversification in Escherichia coli. PLoS Biol 11:e1001490. https://doi.org/10.1371/journal.pbio.1001490
Hietpas RT, Jensen JD, Bolon DN (2011) Experimental illumination of a fitness landscape. Proc Natl Acad Sci USA 108:7896–7901. https://doi.org/10.1073/pnas.1016024108
Hodgkinson A, Ladoukakis E, Eyre-Walker A (2009) Cryptic variation in the human mutation rate. PLoS Biol 7:e1000027. https://doi.org/10.1371/journal.pbio.1000027
Hudson RE, Bergthorsson U, Ochman H (2003) Transcription increases multiple spontaneous point mutations in Salmonella enterica. Nucleic Acids Res 31:4517–4522
Hughes D, Andersson DI (2017) Evolutionary trajectories to antibiotic resistance. Annu Rev Microbiol 71:579–596. https://doi.org/10.1146/annurev-micro-090816-093813
Johnson PL, Hellmann I (2011) Mutation rate distribution inferred from coincident SNPs and coincident substitutions. Genome Biol Evol 3:842–850. https://doi.org/10.1093/gbe/evr044
Kassen R, Bataillon T (2006) Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nat Genet 38:484–488. https://doi.org/10.1038/ng1751
Kawecki TJ, Lenski RE, Ebert D, Hollis B, Olivieri I, Whitlock MC (2012) Experimental evolution. Trends Ecol Evol 27:547–560. https://doi.org/10.1016/j.tree.2012.06.001
Kaznatcheev A (2019) Computational complexity as an ultimate constraint on evolution. Genetics. https://doi.org/10.1534/genetics.119.302000
Keightley PD, Eyre-Walker A (2010) What can we learn about the distribution of fitness effects of new mutations from DNA sequence data? Philos Trans R Soc Lond B Biol Sci 365:1187–1193. https://doi.org/10.1098/rstb.2009.0266
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. https://doi.org/10.1038/nprot.2015.053
Knoppel A, Knopp M, Albrecht LM, Lundin E, Lustig U, Nasvall J, Andersson DI (2018) Genetic adaptation to growth under laboratory conditions in Escherichia coli and Salmonella enterica. Front Microbiol 9:756. https://doi.org/10.3389/fmicb.2018.00756
Koskiniemi S, Andersson DI (2009) Translesion DNA polymerases are required for spontaneous deletion formation in Salmonella typhimurium. Proc Natl Acad Sci USA 106:10248–10253. https://doi.org/10.1073/pnas.0904389106
Kovacs AT, Dragos A (2019) Evolved biofilm: review on the experimental evolution studies of Bacillus subtilis pellicles. J Mol Biol. https://doi.org/10.1016/j.jmb.2019.02.005
Krasovec R et al (2017) Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLoS Biol 15:e2002731. https://doi.org/10.1371/journal.pbio.2002731
Kumar MD, Bava KA, Gromiha MM, Prabakaran P, Kitajima K, Uedaira H, Sarai A (2006) ProTherm and ProNIT: thermodynamic databases for proteins and protein-nucleic acid interactions. Nucleic Acids Res 34:D204–D206. https://doi.org/10.1093/nar/gkj103
Lamrabet O, Plumbridge J, Martin M, Lenski RE, Schneider D, Hindre T (2019) Plasticity of promoter-core sequences allows bacteria to compensate for the loss of a key global regulatory gene. Mol Biol Evol. https://doi.org/10.1093/molbev/msz042
Lang GI, Desai MM (2014) The spectrum of adaptive mutations in experimental evolution. Genomics 104:412–416. https://doi.org/10.1016/j.ygeno.2014.09.011
Lässig M, Mustonen V, Walczak AM (2017) Predicting evolution. Nat Ecol Evol 1:0077. https://doi.org/10.1038/s41559-017-0077
Levinson G, Gutman GA (1987) Slipped-strand mispairing: a major mechanism for DNA sequence evolution. Mol Biol Evol 4:203–221. https://doi.org/10.1093/oxfordjournals.molbev.a040442
Liao HX et al (2013) Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus. Nature 496:469–476. https://doi.org/10.1038/nature12053
Lind PA (2018) Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas. bioRxiv https://doi.org/10.1101/342261
Lind PA, Andersson DI (2008) Whole-genome mutational biases in bacteria. Proc Natl Acad Sci USA 105:17878–17883. https://doi.org/10.1073/pnas.0804445105
Lind PA, Berg OG, Andersson DI (2010a) Mutational robustness of ribosomal protein genes. Science 330:825–827. https://doi.org/10.1126/science.1194617
Lind PA, Tobin C, Berg OG, Kurland CG, Andersson DI (2010b) Compensatory gene amplification restores fitness after inter-species gene replacements. Mol Microbiol 75:1078–1089. https://doi.org/10.1111/j.1365-2958.2009.07030.x
Lind PA, Farr AD, Rainey PB (2015) Experimental evolution reveals hidden diversity in evolutionary pathways. Elife 4. https://doi.org/10.7554/elife.07074
Lind PA, Arvidsson L, Berg OG, Andersson DI (2017a) Variation in mutational robustness between different proteins and the predictability of fitness effects. Mol Biol Evol 34:408–418. https://doi.org/10.1093/molbev/msw239
Lind PA, Farr AD, Rainey PB (2017b) Evolutionary convergence in experimental Pseudomonas populations. ISME J 11:589–600. https://doi.org/10.1038/ismej.2016.157
Lind PA, Libby E, Herzog J, Rainey PB (2019) Predicting mutational routes to new adaptive phenotypes. Elife 8. https://doi.org/10.7554/elife.38822
Long A, Liti G, Luptak A, Tenaillon O (2015) Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat Rev Genet 16:567–582. https://doi.org/10.1038/nrg3937
Lovett ST (2004) Encoded errors: mutations and rearrangements mediated by misalignment at repetitive DNA sequences. Mol Microbiol 52:1243–1253. https://doi.org/10.1111/j.1365-2958.2004.04076.x
Lovett ST, Hurley RL, Sutera VA Jr, Aubuchon RH, Lebedeva MA (2002) Crossing over between regions of limited homology in Escherichia coli. RecA-dependent and RecA-independent pathways. Genetics 160:851–859
Luksza M, Lassig M (2014) A predictive fitness model for influenza. Nature 507:57–61. https://doi.org/10.1038/nature13087
Lundin E, Tang PC, Guy L, Nasvall J, Andersson DI (2017) Experimental determination and prediction of the fitness effects of random point mutations in the biosynthetic enzyme HisA. Mol Biol Evol. https://doi.org/10.1093/molbev/msx325
Lynch M, Ackerman MS, Gout JF, Long H, Sung W, Thomas WK, Foster PL (2016) Genetic drift, selection and the evolution of the mutation rate. Nat Rev Genet 17:704–714. https://doi.org/10.1038/nrg.2016.104
MacLean RC, Buckling A (2009) The distribution of fitness effects of beneficial mutations in Pseudomonas aeruginosa. PLoS Genet 5:e1000406. https://doi.org/10.1371/journal.pgen.1000406
Maddamsetti R, Hatcher PJ, Green AG, Williams BL, Marks DS, Lenski RE (2017) Core genes evolve rapidly in the long-term evolution experiment with Escherichia coli. Genome Biol Evol. https://doi.org/10.1093/gbe/evx064
Maharjan RP, Ferenci T (2017) A shifting mutational landscape in 6 nutritional states: stress-induced mutagenesis as a series of distinct stress input-mutation output relationships. PLoS Biol 15:e2001477. https://doi.org/10.1371/journal.pbio.2001477
Malone JG (2015) Role of small colony variants in persistence of Pseudomonas aeruginosa infections in cystic fibrosis lungs. Infect Drug Resist 8:237–247. https://doi.org/10.2147/IDR.S68214
McCandlish DM, Stoltzfus A (2014) Modeling evolution using the probability of fixation: history and implications. Q Rev Biol 89:225–252
McDonald MJ, Gehrig SM, Meintjes PL, Zhang XX, Rainey PB (2009) Adaptive divergence in experimental populations of Pseudomonas fluorescens. IV. Genetic constraints guide evolutionary trajectories in a parallel adaptive radiation. Genetics 183:1041–1053. https://doi.org/10.1534/genetics.109.107110
McDonald MJ, Cooper TF, Beaumont HJE, Rainey PB (2011) The distribution of fitness effects of new beneficial mutations in Pseudomonas fluorescens. Biol Letters 7:98–100. https://doi.org/10.1098/Rsbl.2010.0547
Nasvall J, Sun L, Roth JR, Andersson DI (2012) Real-time evolution of new genes by innovation, amplification, and divergence. Science 338:384–387. https://doi.org/10.1126/science.1226521
Neher RA, Russell CA, Shraiman BI (2014) Predicting evolution from the shape of genealogical trees. Elife 3. https://doi.org/10.7554/elife.03568
Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814
O’Neill AJ, Huovinen T, Fishwick CW, Chopra I (2006) Molecular genetic and structural modeling studies of Staphylococcus aureus RNA polymerase and the fitness of rifampin resistance genotypes in relation to clinical prevalence. Antimicrob Agents Chemother 50:298–309. https://doi.org/10.1128/AAC.50.1.298-309.2006
Orgogozo V (2015) Replaying the tape of life in the twenty-first century. Interface Focus 5:20150057. https://doi.org/10.1098/rsfs.2015.0057
Orr HA (2003) The distribution of fitness effects among beneficial mutations. Genetics 163:1519–1526
Orr HA (2010) The population genetics of beneficial mutations. Philos Trans R Soc Lond B Biol Sci 365:1195–1201. https://doi.org/10.1098/rstb.2009.0282
Otwinowski J (2018) Biophysical inference of epistasis and the effects of mutations on protein stability and function. Mol Biol Evol 35:2345–2354. https://doi.org/10.1093/molbev/msy141
Perfeito L, Fernandes L, Mota C, Gordo I (2007) Adaptive mutations in bacteria: high rate and small effects. Science 317:813–815. https://doi.org/10.1126/science.1142284
Rainey PB, Travisano M (1998) Adaptive radiation in a heterogeneous environment. Nature 394:69–72. https://doi.org/10.1038/27900
Rainey PB, Remigi P, Farr AD, Lind PA (2017) Darwin was right: where now for experimental evolution? Curr Opin Genet Dev 47:102–109. https://doi.org/10.1016/j.gde.2017.09.003
Reams AB, Roth JR (2015) Mechanisms of gene duplication and amplification. Cold Spring Harb Perspect Biol 7:a016592. https://doi.org/10.1101/cshperspect.a016592
Reams AB, Kofoid E, Duleba N, Roth JR (2014) Recombination and annealing pathways compete for substrates in making rrn duplications in Salmonella enterica. Genetics 196:119–135. https://doi.org/10.1534/genetics.113.158519
Rokyta DR, Beisel CJ, Joyce P, Ferris MT, Burch CL, Wichman HA (2008) Beneficial fitness effects are not exponential for two viruses. J Mol Evol 67:368–376. https://doi.org/10.1007/s00239-008-9153-x
Romling U, Galperin MY, Gomelsky M (2013) Cyclic di-GMP: the first 25 years of a universal bacterial second messenger. Microbiol Mol Biol Rev 77:1–52. https://doi.org/10.1128/mmbr.00043-12
Sanjuan R (2010) Mutational fitness effects in RNA and single-stranded DNA viruses: common patterns revealed by site-directed mutagenesis studies. Philos Trans R Soc Lond B Biol Sci 365:1975–1982. https://doi.org/10.1098/rstb.2010.0063
Sankar TS, Wastuwidyaningtyas BD, Dong Y, Lewis SA, Wang JD (2016) The nature of mutations induced by replication-transcription collisions. Nature. https://doi.org/10.1038/nature18316
Savageau MA, Fasani RA (2009) Qualitatively distinct phenotypes in the design space of biochemical systems. FEBS Lett 583:3914–3922. https://doi.org/10.1016/j.febslet.2009.10.073
Shewaramani S, Finn TJ, Leahy SC, Kassen R, Rainey PB, Moon CD (2017) Anaerobically grown Escherichia coli has an enhanced mutation rate and distinct mutational spectra. PLoS Genet 13:e1006570. https://doi.org/10.1371/journal.pgen.1006570
Sommer MOA, Munck C, Toft-Kehler RV, Andersson DI (2017) Prediction of antibiotic resistance: time for a new preclinical paradigm? Nat Rev Microbiol 15:689–696. https://doi.org/10.1038/nrmicro.2017.75
Sousa A, Bourgard C, Wahl LM, Gordo I (2013) Rates of transposition in Escherichia coli. Biol Lett 9:20130838. https://doi.org/10.1098/rsbl.2013.0838
Spiers AJ, Kahn SG, Bohannon J, Travisano M, Rainey PB (2002) Adaptive divergence in experimental populations of Pseudomonas fluorescens. I. Genetic and phenotypic bases of wrinkly spreader fitness. Genetics 161:33–46
Spiers AJ, Bohannon J, Gehrig SM, Rainey PB (2003) Biofilm formation at the air-liquid interface by the Pseudomonas fluorescens SBW25 wrinkly spreader requires an acetylated form of cellulose. Mol Microbiol 50:15–27
Steenackers HP, Parijs I, Dubey A, Foster KR, Vanderleyden J (2016) Experimental evolution in biofilm populations. FEMS Microbiol Rev 40:373–397. https://doi.org/10.1093/femsre/fuw002
Stern DL (2013) The genetic causes of convergent evolution. Nat Rev Genet 14:751–764. https://doi.org/10.1038/nrg3483
Stoltzfus A, McCandlish DM (2017) Mutational biases influence parallel adaptation. Mol Biol Evol 34:2163–2172. https://doi.org/10.1093/molbev/msx180
Sun S, Berg OG, Roth JR, Andersson DI (2009) Contribution of gene amplification to evolution of increased antibiotic resistance in Salmonella typhimurium. Genetics 182:1183–1195. https://doi.org/10.1534/genetics.109.103028
Tenaillon O, Rodriguez-Verdugo A, Gaut RL, McDonald P, Bennett AF, Long AD, Gaut BS (2012) The molecular diversity of adaptive convergence. Science 335:457–461. https://doi.org/10.1126/science.1212986
Thulin E, Sundqvist M, Andersson DI (2015) Amdinocillin (Mecillinam) resistance mutations in clinical isolates and laboratory-selected mutants of Escherichia coli. Antimicrob Agents Chemother 59:1718–1727. https://doi.org/10.1128/aac.04819-14
Valderrama-Gomez MA, Parales RE, Savageau MA (2018) Phenotype-centric modeling for elucidation of biological design principles. J Theor Biol 455:281–292. https://doi.org/10.1016/j.jtbi.2018.07.009
Van den Bergh B, Swings T, Fauvart M, Michiels J (2018) Experimental design, population dynamics, and diversity in microbial experimental evolution. Microbiol Mol Biol Rev 82. https://doi.org/10.1128/mmbr.00008-18
van Ditmarsch D et al (2013) Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria. Cell Rep 4:697–708. https://doi.org/10.1016/j.celrep.2013.07.026
Viswanathan M, Lacirignola JJ, Hurley RL, Lovett ST (2000) A novel mutational hotspot in a natural quasipalindrome in Escherichia coli. J Mol Biol 302:553–564. https://doi.org/10.1006/jmbi.2000.4088
Wang X, Zorraquino V, Kim M, Tsoukalas A, Tagkopoulos I (2018) Predicting the evolution of Escherichia coli by a data-driven approach. Nat Commun 9:3562. https://doi.org/10.1038/s41467-018-05807-z
Weinreich DM, Delaney NF, Depristo MA, Hartl DL (2006) Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312:111–114. https://doi.org/10.1126/science.1123539
Wiser MJ, Ribeck N, Lenski RE (2013) Long-term dynamics of adaptation in asexual populations. Science 342:1364–1367. https://doi.org/10.1126/science.1243357
Wong A, Rodrigue N, Kassen R (2012) Genomics of adaptation during experimental evolution of the opportunistic pathogen Pseudomonas aeruginosa. PLoS Genet 8:e1002928. https://doi.org/10.1371/journal.pgen.1002928
Yampolsky LY, Stoltzfus A (2001) Bias in the introduction of variation as an orienting factor in evolution. Evol Dev 3:73–83
Yona AH, Alm EJ, Gore J (2018) Random sequences rapidly evolve into de novo promoters. Nat Commun 9:1530. https://doi.org/10.1038/s41467-018-04026-w
Zhen Y, Aardema ML, Medina EM, Schumer M, Andolfatto P (2012) Parallel molecular evolution in an herbivore community. Science 337:1634–1637. https://doi.org/10.1126/science.1226630
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lind, P.A. (2019). Repeatability and Predictability in Experimental Evolution. In: Pontarotti, P. (eds) Evolution, Origin of Life, Concepts and Methods. Springer, Cham. https://doi.org/10.1007/978-3-030-30363-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-30363-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30362-4
Online ISBN: 978-3-030-30363-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)