Predicting evolution

Nature Ecology & Evolution volume 1, Article number: 0077 (2017) Cite this article

Subjects

Abstract

The face of evolutionary biology is changing: from reconstructing and analysing the past to predicting future evolutionary processes. Recent developments include prediction of reproducible patterns in parallel evolution experiments, forecasting the future of individual populations using data from their past, and controlled manipulation of evolutionary dynamics. Here we undertake a synthesis of central concepts for evolutionary predictions, based on examples of microbial and viral systems, cancer cell populations, and immune receptor repertoires. These systems have strikingly similar evolutionary dynamics driven by the competition of clades within a population. These dynamics are the basis for models that predict the evolution of clade frequencies, as well as broad genetic and phenotypic changes. Moreover, there are strong links between prediction and control, which are important for interventions such as vaccine or therapy design. All of these are key elements of what may become a predictive theory of evolution.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

Rent or Buy

All prices are NET prices.

Figure 1: Clonal interference is a common mode of evolution.
Figure 2: Predictability in evolution (schematic).
Figure 3: From fitness models to evolutionary predictions.
Figure 4: Information gain and time horizon of predictions.

References

  1. 1

    Gould, S. J. Wonderful Life: The Burgess Shale and the Nature of History (1989).

    Google Scholar 

  2. 2

    Orgogozo, V. Replaying the tape of life in the twenty-first century. Interface Focus 5, 20150057 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3

    Lobkovsky, A. E. & Koonin, E. V. Replaying the tape of life: quantification of the predictability of evolution. Front. Genet. 3, 246 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  4. 4

    Achaz, G., Rodríguez-Verdugo, A., Gaut, B. S. & Tenaillon, O. The reproducibility of adaptation in the light of experimental evolution with whole genome sequencing. Adv. Exp. Med. Biol. 781, 211–231 (2014).

    PubMed  Article  PubMed Central  Google Scholar 

  5. 5

    Salverda, M. L. M. et al. Initial mutations direct alternative pathways of protein evolution. PLoS Genet. 7, e1001321 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6

    Fonville, J. M. et al. Antibody landscapes after influenza virus infection or vaccination. Science 346, 996–1000 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7

    Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8

    Lipinski, K. A. et al. Cancer evolution and the limits of predictability in precision cancer medicine. Trends Cancer 2, 49–63 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9

    Greaves, M. Evolutionary determinants of cancer. Cancer Discov. 5, 806–820 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10

    Lieberman, T. & Michel, J. Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes. Nat. Genet. 43, 1275–1280 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11

    Miles, J. J., Douek, D. C. & Price, D. A. Bias in the αβ T-cell repertoire: implications for disease pathogenesis and vaccination. Immunol. Cell Biol. 89, 375–387 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  12. 12

    Bull, J. J. & Molineux, I. J. Predicting evolution from genomics: experimental evolution of bacteriophage T7. Heredity (Edinb.) 100, 453–463 (2008).

    CAS  Article  Google Scholar 

  13. 13

    Łuksza, M. & Lässig, M. A predictive fitness model for influenza. Nature 507, 57–61 (2014).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  14. 14

    Munck, C., Gumpert, H. K., Wallin, A. I. N., Wang, H. H. & Sommer, M. O. A. Prediction of resistance development against drug combinations by collateral responses to component drugs. Sci. Transl. Med. 6, 262ra156 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15

    Neher, R. A., Russell, C. A. & Shraiman, B. I. Predicting evolution from the shape of genealogical trees. eLife 3, e03568 (2014).

    PubMed Central  Article  Google Scholar 

  16. 16

    Barton, J. P. et al. Relative rate and location of intra-host HIV evolution to evade cellular immunity are predictable. Nat. Commun. 7, 11660 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17

    Neher, R. A. Genetic draft, selective interference, and population genetics of rapid adaptation. Annu. Rev. Ecol. Evol. Syst. 44, 195–215 (2013).

    Article  Google Scholar 

  18. 18

    Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102–103, 127–144 (1998).

    PubMed  Article  PubMed Central  Google Scholar 

  19. 19

    Miralles, R., Gerrish, P., Moya, A. & Elena, S. Clonal interference and the evolution of RNA viruses. Science 285, 1745–1747 (1999).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20

    Lang, G. I., Botstein, D. & Desai, M. M. Genetic variation and the fate of beneficial mutations in asexual populations. Genetics 188, 647–661 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  21. 21

    Strelkowa, N. & Lässig, M. Clonal interference in the evolution of influenza. Genetics 192, 671–682 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22

    Miller, J. D. et al. Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. Immunity 28, 710–722 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  23. 23

    Schuh, A., Becq, J., Humphray, S. & Alexa, A. Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns. Blood 120, 4191–4197 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  24. 24

    Landau, D. A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25

    Hoehn, K. B. et al. Dynamics of immunoglobulin sequence diversity in HIV-1 infected individuals. Philos. Trans. R. Soc. B 370, 20140241 (2015).

    Article  CAS  Google Scholar 

  26. 26

    Barrick, J. E. et al. Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461, 1243–1247 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27

    Tenaillon, O. et al. The molecular diversity of adaptive convergence. Science 335, 457–461 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28

    Long, A., Liti, G., Luptak, A. & Tenaillon, O. Elucidating the molecular architecture of adaptation via evolve and resequence experiments. Nat. Rev. Genet. 16, 567–582 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29

    Lang, G. I. & Desai, M. M. The spectrum of adaptive mutations in experimental evolution. Genomics 104, 412–416 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30

    Stratton, M., Campbell, P. & Futreal, P. The cancer genome. Nature 458, 719–724 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31

    Behjati, S. et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32

    Venturi, V., Price, D. A., Douek, D. C. & Davenport, M. P. The molecular basis for public T-cell responses? Nat. Rev. Immunol. 8, 231–238 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33

    Mora, T. & Walczak, A. M. Quantifying lymphocyte receptor diversity. Preprint at bioRxiv https://dx.doi.org/10.1101/046870 (2016).

  34. 34

    Elhanati, Y. et al. Inferring processes underlying B-cell repertoire diversity. Philos. Trans. R. Soc. B 370, 20140243 (2015).

    Article  CAS  Google Scholar 

  35. 35

    Thomas, N. et al. Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinformatics 30, 3181–3188 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36

    Kvitek, D. J. & Sherlock, G. Whole genome, whole population sequencing reveals that loss of signaling networks is the major adaptive strategy in a constant environment. PLoS Genet. 9, e1003972 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. 37

    Barrick, J. E. & Lenski, R. E. Genome dynamics during experimental evolution. Nat. Rev. Genet. 14, 827–839 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38

    Kryazhimskiy, S., Rice, D. P., Jerison, E. R. & Desai, M. M. Microbial evolution. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39

    Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2012).

    CAS  Article  Google Scholar 

  40. 40

    Barroso-Batista, J., Demengeot, J. & Gordo, I. Adaptive immunity increases the pace and predictability of evolutionary change in commensal gut bacteria. Nat. Commun. 6, 8945 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41

    Christin, P.-A., Weinreich, D. M. & Besnard, G. Causes and evolutionary significance of genetic convergence. Trends Genet. 26, 400–405 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42

    Ramiro, R. S., Costa, H. & Gordo, I. Macrophage adaptation leads to parallel evolution of genetically diverse Escherichia coli small-colony variants with increased fitness in vivo and antibiotic collateral sensitivity. Evol. Appl. 9, 994–1004 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  Article  Google Scholar 

  44. 44

    Gerstung, M. et al. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nat. Commun. 6, 5901 (2014).

    Article  CAS  Google Scholar 

  45. 45

    Murali-Krishna, K. et al. Counting antigen-specific CD8 T cells: a reevaluation of bystander activation during viral infection. Immunity 8, 177–187 (1998).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46

    Butler, T. C., Kardar, M. & Chakraborty, A. K. Quorum sensing allows T cells to discriminate between self and nonself. Proc. Natl Acad. Sci. USA 110, 11833–11838 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47

    Schiffels, S., Szöllosi, G. J., Mustonen, V. & Lässig, M. Emergent neutrality in adaptive asexual evolution. Genetics 189, 1361–75 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  48. 48

    Good, B. H., Rouzine, I. M., Balick, D. J., Hallatschek, O. & Desai, M. M. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl Acad. Sci. USA 109, 4950–4955 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  49. 49

    Elena, S. F. & Lenski, R. E. Microbial genetics: evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).

  50. 50

    Betancourt, A. J. Genomewide patterns of substitution in adaptively evolving populations of the RNA bacteriophage MS2. Genetics 181, 1535–1544 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51

    Tenaillon, O., Toupance, B., Le Nagard, H., Taddei, F. & Godelle, B. Mutators, population size, adaptive landscape and the adaptation of asexual populations of bacteria. Genetics 152, 485–493 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Illingworth, C. J. R. & Mustonen, V. Components of selection in the evolution of the influenza virus: linkage effects beat inherent selection. PLoS Pathog. 8, e1003091 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53

    Nourmohammad, A., Held, T. & Lässig, M. Universality and predictability in molecular quantitative genetics. Curr. Opin. Genet. Dev. 23, 684–693 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  54. 54

    Zeldovich, K. B., Chen, P. & Shakhnovich, E. I. Protein stability imposes limits on organism complexity and speed of molecular evolution. Proc. Natl Acad. Sci. USA 104, 16152–16157 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  55. 55

    Marcusson, L. L., Frimodt-Møller, N. & Hughes, D. Interplay in the selection of fluoroquinolone resistance and bacterial fitness. PLoS Pathog. 5, e1000541 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. 56

    MacLean, R. C., Hall, A. R., Perron, G. G. & Buckling, A. The population genetics of antibiotic resistance: integrating molecular mechanisms and treatment contexts. Nat. Rev. Genet. 11, 405–414 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. 57

    Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–71 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  58. 58

    Wylie, C. S. & Shakhnovich, E. I. A biophysical protein folding model accounts for most mutational fitness effects in viruses. Proc. Natl Acad. Sci. USA 108, 9916–9921 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  59. 59

    Gong, L. I., Suchard, M. A. & Bloom, J. D. Stability-mediated epistasis constrains the evolution of an influenza protein. eLife 2, e00631 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60

    Rodrigues, J. V. et al. Biophysical principles predict fitness landscapes of drug resistance. Proc. Natl Acad. Sci. USA 113, E1470–E1478 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  61. 61

    Hughes, D. & Andersson, D. I. Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms. Nat. Rev. Genet. 16, 459–471 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  62. 62

    Jacquier, H. et al. Capturing the mutational landscape of the beta-lactamase TEM-1. Proc. Natl Acad. Sci. USA 110, 13067–13072 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  63. 63

    McFarland, C. D. et al. Passenger DNA alterations reduce cancer fitness in cell culture and mouse models. Preprint at bioRxiv https://dx.doi.org/10.1101/026302 (2015).

  64. 64

    McFarland, C. D., Mirny, L. A. & Korolev, K. S. Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc. Natl Acad. Sci. USA 111, 15138–15143 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  65. 65

    Koelle, K. & Rasmussen, D. A. The effects of a deleterious mutation load on patterns of influenza A/H3N2's antigenic evolution in humans. eLife 4, e07361 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  66. 66

    Weinreich, D. M., Delaney, N. F., Depristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).

    CAS  Article  Google Scholar 

  67. 67

    Comas, I. et al. Whole-genome sequencing of rifampicin-resistant Mycobacterium tuberculosis strains identifies compensatory mutations in RNA polymerase genes. Nat. Genet. 44, 106–110 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68

    Palmer, A. C. & Kishony, R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat. Rev. Genet. 14, 243–248 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69

    Rodríguez-Verdugo, A., Gaut, B. S. & Tenaillon, O. Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress. BMC Evol. Biol. 13, 50 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70

    Weinreich, D., Lan, Y., Wylie, C. S. & Heckendorn, R. B. Should evolutionary geneticists worry about higher-order epistasis? Curr. Opin. Genet. Dev. 23, 700–707 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71

    Schenk, M. F. & de Visser, J. A. G. M. Predicting the evolution of antibiotic resistance. BMC Biol. 11, 14 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. 72

    Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).

    CAS  Article  Google Scholar 

  73. 73

    McKeown, A. N. et al. Evolution of DNA Specificity in a transcription factor family produced a new gene regulatory module. Cell 159, 58–68 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74

    Couce, A. & Tenaillon, O. A. The rule of declining adaptability in microbial evolution experiments. Front. Genet. 6, 99 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. 75

    Press, M. O., Queitsch, C. & Borenstein, E. Evolutionary assembly patterns of prokaryotic genomes. Genome Res. 26, 826–833 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76

    Heckmann, D. et al. Predicting C4 photosynthesis evolution: modular, individually adaptive steps on a Mount Fuji fitness landscape. Cell 153, 1579–1588 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  77. 77

    de Visser, J. A. G. M. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  78. 78

    Bank, C., Matuszewski, S., Hietpas, R. T. & Jensen, J. D. On the (un)predictability of a large intragenic fitness landscape. Proc. Natl Acad. Sci. USA 113, 14085–14090 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  79. 79

    Kondrashov, D. A. & Kondrashov, F. A. Topological features of rugged fitness landscapes in sequence space. Trends Genet. 31, 24–33 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  80. 80

    du Plessis, L., Leventhal, G. & Bonhoeffer, S. How good are statistical models at approximating complex fitness landscapes. Mol. Biol. Evol. 33, 2454–2468 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81

    Levin, B. R., Perrot, V. & Walker, N. Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. Genetics 154, 985–997 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Luciani, F., Sisson, S. A., Jiang, H., Francis, A. R. & Tanaka, M. M. The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 106, 14711–14715 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  83. 83

    Vogwill, T. & MacLean, R. C. The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol. Appl. 8, 284–295 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  84. 84

    Reynolds, M. G. Compensatory evolution in rifampin-resistant Escherichia coli. Genetics 156, 1471–1481 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85

    Brown, K. M., Depristo, M. A., Weinreich, D. M. & Hartl, D. L. Temporal constraints on the incorporation of regulatory mutants in evolutionary pathways. Mol. Biol. Evol. 26, 2455–2462 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86

    Poon, A. & Chao, L. The rate of compensatory mutation in the DNA bacteriophage X174. Genetics 170, 989–999 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87

    Maisnier-Patin, S., Berg, O. G., Liljas, L. & Andersson, D. I. Compensatory adaptation to the deleterious effect of antibiotic resistance in Salmonella typhimurium. Mol. Microbiol. 46, 355–366 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  88. 88

    Sousa, A., Magalhães, S. & Gordo, I. Cost of antibiotic resistance and the geometry of adaptation. Mol. Biol. Evol. 29, 1417–1428 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  89. 89

    Aakre, C. D. et al. Evolving new protein-protein interaction specificity through promiscuous intermediates. Cell 163, 594–606 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. 90

    Burke, M. K., Liti, G. & Long, A. D. Standing genetic variation drives repeatable experimental evolution in outcrossing populations of Saccharomyces cerevisiae. Mol. Biol. Evol. 31, 3228–3239 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91

    Vázquez-García, I. et al. Background-dependent effects of selection on subclonal heterogeneity. Preprint at bioRxiv https://dx.doi.org/10.1101/039859 (2016).

  92. 92

    Barroso-Batista, J. et al. The first steps of adaptation of Escherichia coli to the gut are dominated by soft sweeps. PLoS Genet. 10, e1004182 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  93. 93

    Jiang, N. et al. Lineage structure of the human antibody repertoire in response to influenza vaccination. Sci. Transl. Med. 5, 171ra19 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  94. 94

    Mustonen, V. & Lässig, M. From fitness landscapes to seascapes: non-equilibrium dynamics of selection and adaptation. Trends Genet. 25, 111–119 (2009).

    CAS  Article  Google Scholar 

  95. 95

    Hallatschek, O., Hersen, P., Ramanathan, S. & Nelson, D. R. Genetic drift at expanding frontiers promotes gene segregation. Proc. Natl Acad. Sci. USA 104, 19926–19930 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  96. 96

    Tenaillon, O. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536, 165–170 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97

    Levy, S. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98

    Venkataram, S. et al. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell 166, 1585–1589.e22 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99

    Adams, R., Kinney, J. B., Mora, T. & Walczak, A. M. Measuring the sequence-affinity landscape of antibodies with massively parallel titration curves. eLife http://dx.doi.org/10.7554/eLife.23156 (2016).

  100. 100

    Birnbaum, M. E. et al. Deconstructing the peptide-MHC specificity of T cell recognition. Cell 157, 1073–1087 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101

    Maerkl, S. & Quake, S. A systems approach to measuring the binding energy landscapes of transcription factors. Science 315, 233–237 (2007).

    CAS  Article  Google Scholar 

  102. 102

    Bulyk, M. DNA microarray technologies for measuring protein–dna interactions. Curr. Opin. Biotechnol. 17, 422–430 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103

    Kinney, J., Murugan, A., Callan Curtis, G. J. & Cox, E. C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA 107, 9158–9163 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  104. 104

    Thyagarajan, B. & Bloom, J. D. The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin. eLife 3, e03300 (2014).

    PubMed Central  Article  CAS  Google Scholar 

  105. 105

    Halpern, A. L. & Bruno, W. J. Evolutionary distances for protein-coding sequences: modeling site- specific residue frequencies. Mol. Biol. Evol. 15, 910–917 (1962).

    Article  Google Scholar 

  106. 106

    Berg, J., Willmann, S. & Lässig, M. Adaptive evolution of transcription factor binding sites. BMC Evol. Biol. 4, 42 (2004).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107

    Kouyos, R. D. et al. Exploring the complexity of the HIV-1 fitness landscape. PLoS Genet. 8, e1002551 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108

    Lässig, M. From biophysics to evolutionary genetics: statistical aspects of gene regulation. BMC Bioinform. 8(suppl. 6), S7 (2007).

    Article  CAS  Google Scholar 

  109. 109

    Mustonen, V., Kinney, J. B., Callan Curtis, G. J. & Lässig, M. Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites. Proc. Natl Acad. Sci. USA 105, 12376–12381 (2008).

    CAS  Article  Google Scholar 

  110. 110

    Serohijos, A. W. R. & Shakhnovich, E. I. Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics. Curr. Opin. Struct. Biol. 26, 84–91 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111

    Moses, A., Chiang, D., Pollard, D., Iyer, V. & Eisen, M. MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model. Genome 5, R98 (2004).

    Google Scholar 

  112. 112

    Liberles, D. A. et al. The interface of protein structure, protein biophysics, and molecular evolution. Protein Sci. 21, 769–785 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  113. 113

    Gerland, U. & Hwa, T. On the selection and evolution of regulatory DNA motifs. J. Mol. Evol. 55, 386–400 (2002).

    CAS  Article  Google Scholar 

  114. 114

    Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  115. 115

    Fisher, R. A. The Genetical Theory of Natural Selection. (Clarendon, 1930).

    Google Scholar 

  116. 116

    Trindade, S., Sousa, A. & Gordo, I. Antibiotic resistance and stress in the light of Fisher's model. Evolution 66, 3815–3824 (2012).

    PubMed  Article  PubMed Central  Google Scholar 

  117. 117

    Hermsen, R., Deris, J. B. & Hwa, T. On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc. Natl Acad. Sci. USA 109, 10775–10780 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  118. 118

    Greulich, P., Waclaw, B. & Allen, R. J. Mutational pathway determines whether drug gradients accelerate evolution of drug-resistant cells. Phys. Rev. Lett. 109, 88101 (2012).

    Article  CAS  Google Scholar 

  119. 119

    Smith, D. J., Lapedes, A. S. & De Jong, J. C. Mapping the antigenic and genetic evolution of influenza virus. Science 305, 371–377 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  120. 120

    Bedford, T. et al. Integrating influenza antigenic dynamics with molecular evolution. eLife 3, e01914 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  121. 121

    Neher, R. A., Bedford, T., Daniels, R. S., Russell, C. A. & Shraiman, B. I. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc. Natl Acad. Sci. USA 113, E1701–E1709 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  122. 122

    Papp, B., Notebaart, R. A. & Pál, C. Systems-biology approaches for predicting genomic evolution. Nat. Rev. Genet. 12, 591–602 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  123. 123

    Wang, S. et al. Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies. Cell 160, 785–797 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124

    Nourmohammad, A., Otwinowski, J. & Plotkin, J. B. Host-pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections. PLoS Genet. 12, e1006171 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  125. 125

    Gandon, S., Day, T., Metcalf, C. J. E. & Grenfell, B. T. Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends Ecol. Evol. 31, 1–13 (2016).

    Article  Google Scholar 

  126. 126

    Torres-Barceló, C. & Hochberg, M. E. Evolutionary rationale for phages as complements of antibiotics. Trends Microbiol. 24, 249–56 (2016).

    Article  CAS  Google Scholar 

  127. 127

    Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. 128

    Bialek, W., Nemenman, I. & Tishby, N. Predictability, complexity, and learning. Neural Comput. 13, 2409–2463 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  129. 129

    Mustonen, V. & Lässig, M. Fitness flux and ubiquity of adaptive evolution. Proc. Natl Acad. Sci. USA 107, 4248–4253 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  130. 130

    Read, A. F., Day, T. & Huijben, S. The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc. Natl Acad. Sci. USA 108, 10871–10877 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  131. 131

    Ankomah, P. & Levin, B. R. Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections. Proc. Natl Acad. Sci. USA 111, 8331–8338 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  132. 132

    Das Thakur, M. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  133. 133

    Mayer, A., Mora, T., Rivoire, O. & Walczak, A. M. Diversity of immune strategies explained by adaptation to pathogen statistics. Proc. Natl Acad. Sci. USA 113, 8630–8635 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  134. 134

    Fischer, A., Vázquez-García, I. & Mustonen, V. The value of monitoring to control evolving populations. Proc. Natl Acad. Sci. USA 112, 1007–1012 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  135. 135

    Perelson, A. S., Neumann, A. U., Markowitz, M., Leonard, J. M. & Ho, D. D. HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Science 271, 1582–1586 (1996).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  136. 136

    Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  137. 137

    Caskey, M. et al. Viraemia suppressed in HIV-1-infected humans by broadly neutralizing antibody 3BNC117. Nature 522, 487–491 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  138. 138

    Perfeito, L., Ghozzi, S., Berg, J., Schnetz, K. & Lässig, M. Nonlinear fitness landscape of a molecular pathway. PLoS Genet. 7, e1002160 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  139. 139

    Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  140. 140

    Abel Zur Wiesch, P., Kouyos, R., Engelstädter, J., Regoes, R. R. & Bonhoeffer, S. Population biological principles of drug-resistance evolution in infectious diseases. Lancet Infect. Dis. 11, 236–247 (2011).

    Article  Google Scholar 

  141. 141

    Fischer, A., Vázquez-García, I., Illingworth, C. J. R. & Mustonen, V. High-definition reconstruction of clonal composition in cancer. Cell Rep. 7, 1740–1752 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  142. 142

    Liao, H.-X. et al. Co-evolution of a broadly neutralizing HIV-1 antibody and founder virus. Nature 496, 469–476 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank M. Desai, I. Gordo, M. Łuksza, T. Mora and A. Nourmohammad for comments on the manuscript. M. Desai, M. Łuksza and A. Nourmohammad also provided important input to illustrations. This work has been partially supported by Deutsche Forschungs-gemeinschaft grant SFB 680 (M.L.), Wellcome Trust grant 098051 (V.M.), and European Research Council ERCStG 306312 (A.M.W.).

Author information

Author notes

  1. Ville Mustonen

    Present address: †Present address: Department of Biosciences, University of Helsinki, PO Box 65, 00014, Finland.,

Affiliations

  1. Institute of Theoretical Physics, University of Cologne, 50937 Cologne, Germany

    Michael Lässig

  2. Wellcome Trust Sanger Institute, Cambridge, CB10 1SA, UK

    Ville Mustonen

  3. Laboratoire de Physique Théorique CNRS, Ecole Normale Supérieure, 75005 Paris, France

    Aleksandra M. Walczak

Authors
  1. Michael Lässig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ville Mustonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aleksandra M. Walczak
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors developed concepts and wrote the paper. Authors are listed in alphabetical order.

Corresponding authors

Correspondence to Michael Lässig or Ville Mustonen or Aleksandra M. Walczak.

Ethics declarations

Competing interests

The author declares no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lässig, M., Mustonen, V. & Walczak, A. Predicting evolution. Nat Ecol Evol 1, 0077 (2017). https://doi.org/10.1038/s41559-017-0077

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing