Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Steering and controlling evolution — from bioengineering to fighting pathogens

Nature Reviews Genetics volume 24, pages 851–867 (2023)Cite this article

Subjects

Abstract

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost–benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Examples of evolutionary control.
Fig. 2: Concepts and key steps of evolutionary control.
Fig. 3: Directed evolution versus escape control.
Fig. 4: Monitoring, adaptive learning and prediction shape control protocols.
Fig. 5: Computing and optimizing control.

Similar content being viewed by others

References

  1. Carroll, S. P. et al. Applying evolutionary biology to address global challenges. Science 346, 1245993 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  2. Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Jouhten, P. et al. Predictive evolution of metabolic phenotypes using model-designed environments. Mol. Syst. Biol. 18, e10980 (2022). This study develops control protocols using environment switching and trait co-variation to elicit traits that are uncorrelated with cell fitness.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Esfahani, K. et al. A review of cancer immunotherapy: from the past, to the present, to the future. Curr. Oncol. 27, S87–S97 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Lässig, M. & Mustonen, V. Eco-evolutionary control of pathogens. Proc. Natl Acad. Sci. USA 117, 19694–19704 (2020). This study establishes optimal strategies for eco-evolutionary control that depend on the rate and size of the target population, quantifying how monitoring and computational prediction affect protocols and efficiency of control.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Nourmohammad, A. & Eksin, C. Optimal evolutionary control for artificial selection on molecular phenotypes. Phys. Rev. X 11, 011044 (2021). This study proposes an optimal control formalism to direct the evolution of multivariate traits with collateral effects, and discusses how to use predictive information to schedule monitoring of a population for control by artificial selection.

    CAS  Google Scholar 

  7. Lässig, M., Mustonen, V. & Walczak, A. M. Predicting evolution. Nat. Ecol. Evol. 1, 77 (2017).

    Article  PubMed  Google Scholar 

  8. Molina, R. S. et al. In vivo hypermutation and continuous evolution. Nat. Rev. Methods Prim. 2, 1–22 (2022).

    Google Scholar 

  9. Esvelt, K. M., Carlson, J. C. & Liu, D. R. A system for the continuous directed evolution of biomolecules. Nature 472, 499–503 (2011). This study presents an experimental platform for the directed evolution of molecules, using bacteriophages for feedback-controlled cell-to-cell transfer of genetic material.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Toprak, E. et al. Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat. Protoc. 8, 555–567 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  11. Badran, A. H. & Liu, D. R. In vivo continuous directed evolution. Curr. Opin. Chem. Biol. 24, 1–10 (2015).

    Article  CAS  PubMed  Google Scholar 

  12. Packer, M. S., Rees, H. A. & Liu, D. R. Phage-assisted continuous evolution of proteases with altered substrate specificity. Nat. Commun. 8, 956 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  13. Zhong, Z. et al. Automated continuous evolution of proteins in vivo. ACS Synth. Biol. 9, 1270–1276 (2020). This study presents an experimental platform for directed evolution of biomolecules in yeast, using targeted mutagenesis combined with artificial selection tuned by feedback from the molecular activity of interest.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Parts, L. et al. Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res. 21, 1131–1138 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Iwasawa, J. et al. Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape. PLoS Biol. 20, e3001920 (2022). This study infers phenotype-based fitness landscapes for antibiotic resistance evolution, quantifying primary and collateral effects across different drugs.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Klumpp, S., Zhang, Z. & Hwa, T. Growth rate-dependent global effects on gene expression in bacteria. Cell 139, 1366–1375 (2009).

    Article  PubMed Central  PubMed  Google Scholar 

  17. Ceroni, F., Algar, R., Stan, G.-B. & Ellis, T. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12, 415–418 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Fedorec, A. J. H., Karkaria, B. D., Sulu, M. & Barnes, C. P. Single strain control of microbial consortia. Nat. Commun. 12, 1977 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Aoki, S. K. et al. A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570, 533–537 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Khammash, M. H. Perfect adaptation in biology. Cell Syst. 12, 509–521 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Bier, E. Gene drives gaining speed. Nat. Rev. Genet. 23, 5–22 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Unckless, R. L., Clark, A. G. & Messer, P. W. Evolution of resistance against CRISPR/Cas9 gene drive. Genetics 205, 827–841 (2017).

    Article  PubMed  Google Scholar 

  23. Hutchings, M. I., Truman, A. W. & Wilkinson, B. Antibiotics: past, present and future. Curr. Opin. Microbiol. 51, 72–80 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Yang, D., Biragyn, A., Kwak, L. W. & Oppenheim, J. J. Mammalian defensins in immunity: more than just microbicidal. Trends Immunol. 23, 291–296 (2002).

    Article  CAS  PubMed  Google Scholar 

  26. Selsted, M. E. & Ouellette, A. J. Mammalian defensins in the antimicrobial immune response. Nat. Immunol. 6, 551–557 (2005).

    Article  CAS  PubMed  Google Scholar 

  27. Adyns, L., Proost, P. & Struyf, S. Role of defensins in tumor biology. Int. J. Mol. Sci. 24, 5268 (2023).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Murray, C. J. L. et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399, 629–655 (2022).

    Article  CAS  Google Scholar 

  29. Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. V. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2014).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  31. Andersson, D. I., Hughes, D. & Kubicek-Sutherland, J. Z. Mechanisms and consequences of bacterial resistance to antimicrobial peptides. Drug Resist. Updat. 26, 43–57 (2016).

    Article  CAS  PubMed  Google Scholar 

  32. Pinheiro, F., Warsi, O., Andersson, D. I. & Lässig, M. Metabolic fitness landscapes predict the evolution of antibiotic resistance. Nat. Ecol. Evol. 5, 677–687 (2021).

    Article  PubMed  Google Scholar 

  33. Greulich, P., Scott, M., Evans, M. R. & Allen, R. J. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol. Syst. Biol. 11, 796 (2015). This study establishes a computable metabolic model of drug action and dosage response that can inform control protocols.

    Article  PubMed  Google Scholar 

  34. Roemhild, R., Bollenbach, T. & Andersson, D. I. The physiology and genetics of bacterial responses to antibiotic combinations. Nat. Rev. Microbiol. 20, 478–490 (2022).

    Article  CAS  PubMed  Google Scholar 

  35. Hansen, E., Karslake, J., Woods, R. J., Read, A. F. & Wood, K. B. Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations. PLoS Biol. 18, e3000713 (2020). This study establishes control strategies for antibiotic interventions that focus on containment rather than eradication of the target pathogen and delay the evolution of resistance.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  37. Hollingsworth, R. E. & Jansen, K. Turning the corner on therapeutic cancer vaccines. npj Vaccines 4, 1–10 (2019).

    Article  Google Scholar 

  38. Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017). This study establishes a predictive fitness model for cancer antigens interacting with T cell immune receptors that can guide cancer vaccine selection.

    Article  PubMed Central  PubMed  Google Scholar 

  39. Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Saxena, M., van der Burg, S. H., Melief, C. J. M. & Bhardwaj, N. Therapeutic cancer vaccines. Nat. Rev. Cancer 21, 360–378 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Hoyos, D. et al. Fundamental immune–oncogenicity trade-offs define driver mutation fitness. Nature 606, 172–179 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Zapata, L. et al. Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors. Nat. Genet. 55, 451–460 (2023).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Łuksza, M. et al. Neoantigen quality predicts immunoediting in survivors of pancreatic cancer. Nature 606, 389–395 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  44. Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382.e4 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Lakatos, E. et al. Evolutionary dynamics of neoantigens in growing tumors. Nat. Genet. 52, 1057–1066 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Sahin, U. & Türeci, Ö. Personalized vaccines for cancer immunotherapy. Science 359, 1355–1360 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Rojas, L. A. et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature 618, 144–150 (2023).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Kolios, A. G. A., Tsokos, G. C. & Klatzmann, D. Interleukin-2 and regulatory T cells in rheumatic diseases. Nat. Rev. Rheumatol. 17, 749–766 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Schwartz, R. N., Stover, L. & Dutcher, J. P. Managing toxicities of high-dose interleukin-2. Oncology 16, 11–20 (2002).

    PubMed  Google Scholar 

  50. Achar, S. R. et al. Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics. Science 376, 880–884 (2022).

    Article  CAS  PubMed  Google Scholar 

  51. Nourmohammad, A. T cell immune responses deciphered. Science 376, 796–797 (2022).

    Article  CAS  PubMed  Google Scholar 

  52. Łuksza, M. & Lässig, M. A predictive fitness model for influenza. Nature 507, 57–61 (2014). This study predicts the antigenic evolution of influenza from one year to the next and is used to inform the biannual selection of global influenza vaccines.

    Article  PubMed  Google Scholar 

  53. Morris, D. H. et al. Predictive modeling of influenza shows the promise of applied evolutionary biology. Trends Microbiol. 26, 102–118 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Huddleston, J. et al. Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution. eLife 9, e60067 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Wen, F. T., Bell, S. M., Bedford, T. & Cobey, S. Estimating vaccine-driven selection in seasonal influenza. Viruses 10, 509 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  56. Meijers, M., Ruchnewitz, D., Łuksza, M. & Lässig, M. Vaccination shapes evolutionary trajectories of SARS-CoV-2. Preprint at bioRxiv https://doi.org/10.1101/2022.07.19.500637 (2022).

    Article  Google Scholar 

  57. Jardine, J. et al. Rational HIV immunogen design to target specific germline B cell receptors. Science 340, 711–716 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  58. Escolano, A. et al. Sequential immunization elicits broadly neutralizing anti-HIV-1 antibodies in Ig knockin mice. Cell 166, 1445–1458.e12 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  59. Saunders, K. O. et al. Targeted selection of HIV-specific antibody mutations by engineering B cell maturation. Science 366, eaay7199 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  60. Steichen, J. M. et al. A generalized HIV vaccine design strategy for priming of broadly neutralizing antibody responses. Science 366, eaax4380 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  61. Corey, L. et al. Two randomized trials of neutralizing antibodies to prevent HIV-1 acquisition. N. Engl. J. Med. 384, 1003–1014 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  62. Gilbert, P. B. et al. Neutralization titer biomarker for antibody-mediated prevention of HIV-1 acquisition. Nat. Med. 28, 1924–1932 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Haynes, B. F. et al. Strategies for HIV-1 vaccines that induce broadly neutralizing antibodies. Nat. Rev. Immunol. 23, 142–158 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  64. Hai, R. et al. Influenza viruses expressing chimeric hemagglutinins: globular head and stalk domains derived from different subtypes. J. Virol. 86, 5774–5781 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Yassine, H. M. et al. Hemagglutinin-stem nanoparticles generate heterosubtypic influenza protection. Nat. Med. 21, 1065–1070 (2015).

    Article  CAS  PubMed  Google Scholar 

  66. Krammer, F., García-Sastre, A. & Palese, P. Is it possible to develop a “universal” influenza virus vaccine? Potential target antigens and critical aspects for a universal influenza vaccine. Cold Spring Harb. Perspect. Biol. 10, a028845 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  67. Corbett Kizzmekia, S. et al. Design of nanoparticulate group 2 influenza virus hemagglutinin stem antigens that activate unmutated ancestor B cell receptors of broadly neutralizing antibody lineages. MBio 10, e02810–e02818 (2019).

    PubMed Central  PubMed  Google Scholar 

  68. Wu, N. C. & Wilson, I. A. Influenza hemagglutinin structures and antibody recognition. Cold Spring Harb. Perspect. Med. 10, a038778 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  69. Arevalo, C. P. et al. A multivalent nucleoside-modified mRNA vaccine against all known influenza virus subtypes. Science 378, 899–904 (2022).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  71. Shaffer, J. S., Moore, P. L., Kardar, M. & Chakraborty, A. K. Optimal immunization cocktails can promote induction of broadly neutralizing Abs against highly mutable pathogens. Proc. Natl Acad. Sci. USA 113, E7039–E7048 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  72. Sprenger, K. G., Louveau, J. E., Murugan, P. M. & Chakraborty, A. K. Optimizing immunization protocols to elicit broadly neutralizing antibodies. Proc. Natl Acad. Sci. USA 117, 20077–20087 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  73. Zhou, T. et al. Structural basis for broad and potent neutralization of HIV-1 by antibody VRC01. Science 329, 811–817 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  74. Klein, F. et al. Antibodies in HIV-1 vaccine development and therapy. Science 341, 1199–1204 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  75. Subbaraman, H., Schanz, M. & Trkola, A. Broadly neutralizing antibodies: what is needed to move from a rare event in HIV-1 infection to vaccine efficacy? Retrovirology 15, 52 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  76. Luo, S. & Perelson, A. S. Competitive exclusion by autologous antibodies can prevent broad HIV-1 antibodies from arising. Proc. Natl Acad. Sci. USA 112, 11654–11659 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  77. 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).

    Article  PubMed Central  PubMed  Google Scholar 

  78. Planas, D. et al. Considerable escape of SARS-CoV-2 Omicron to antibody neutralization. Nature 602, 671–675 (2021).

    Article  PubMed  Google Scholar 

  79. Garcia-Beltran, W. F. et al. mRNA-based COVID-19 vaccine boosters induce neutralizing immunity against SARS-CoV-2 Omicron variant. Cell 185, 457–466.e4 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  80. Gruell, H. et al. mRNA booster immunization elicits potent neutralizing serum activity against the SARS-CoV-2 Omicron variant. Nat. Med. 28, 477–480 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  81. Hachmann, N. P. et al. Neutralization escape by SARS-CoV-2 omicron subvariants BA.2.12.1, BA.4, and BA.5. N. Engl. J. Med. 387, 86–88 (2022).

    Article  PubMed  Google Scholar 

  82. Yang, L. et al. Antigen presentation dynamics shape the antibody response to variants like SARS-CoV-2 Omicron after multiple vaccinations with the original strain. Cell Rep. 42, 112256 (2023).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  83. Schaefer-Babajew, D. et al. Antibody feedback regulates immune memory after SARS-CoV-2 mRNA vaccination. Nature 613, 735–742 (2023).

    Article  CAS  PubMed  Google Scholar 

  84. Futuyma, D. J. Evolutionary constraint and ecological consequences. Evolution 64, 1865–1884 (2010).

    Article  PubMed  Google Scholar 

  85. Jia, X., Ma, Y., Bu, R., Zhao, T. & Wu, K. Directed evolution of a transcription factor PbrR to improve lead selectivity and reduce zinc interference through dual selection. AMB Express 10, 67 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  86. Yokobayashi, Y. & Arnold, F. H. A dual selection module for directed evolution of genetic circuits. Nat. Comput. 4, 245–254 (2005).

    Article  CAS  Google Scholar 

  87. 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).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  88. Hansen, E., Woods, R. J. & Read, A. F. How to use a chemotherapeutic agent when resistance to it threatens the patient. PLoS Biol. 15, e2001110 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  89. Li, X. et al. Mitochondria shed their outer membrane in response to infection-induced stress. Science 375, eabi4343 (2022).

    Article  CAS  PubMed  Google Scholar 

  90. Gatenby, R. A., Gillies, R. J. & Brown, J. S. The evolutionary dynamics of cancer prevention. Nat. Rev. Cancer 10, 526–527 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  91. Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1–9 (2017). This study introduces an eco-evolutionary protocol for cancer control that adaptively incorporates feedback from the target cell population, resulting in substantial clinical improvements over previous approaches.

    Article  Google Scholar 

  92. Day, T., Huijben, S. & Read, A. F. Is selection relevant in the evolutionary emergence of drug resistance? Trends Microbiol. 23, 126–133 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  95. 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).

    Article  PubMed Central  PubMed  Google Scholar 

  96. Gajewski, T. F., Schreiber, H. & Fu, Y.-X. Innate and adaptive immune cells in the tumor microenvironment. Nat. Immunol. 14, 1014–1022 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  97. Wang, S. & Dai, L. Evolving generalists in switching rugged landscapes. PLoS Comput. Biol. 15, e1007320 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  98. Sachdeva, V., Husain, K., Sheng, J., Wang, S. & Murugan, A. Tuning environmental timescales to evolve and maintain generalists. Proc. Natl Acad. Sci. USA 117, 12693–12699 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  99. Yang, L., Caradonna, T. M., Schmidt, A. G. & Chakraborty, A. K. Mechanisms that promote the evolution of cross-reactive antibodies upon vaccination with designed influenza immunogens. Cell Rep. 42, 112160 (2023). This study combines theory with experiments to show that influenza vaccines containing a chimera of multiple epitopes can induce broadly reactive antibodies.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  100. Brown, S. P., Le Chat, L., De Paepe, M. & Taddei, F. Ecology of microbial invasions: amplification allows virus carriers to invade more rapidly when rare. Curr. Biol. 16, 2048–2052 (2006).

    Article  CAS  PubMed  Google Scholar 

  101. Duerkop, B. A., Clements, C. V., Rollins, D., Rodrigues, J. L. M. & Hooper, L. V. A composite bacteriophage alters colonization by an intestinal commensal bacterium. Proc. Natl Acad. Sci. USA 109, 17621–17626 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  102. Gama, J. A. et al. Temperate bacterial viruses as double-edged swords in bacterial warfare. PLoS ONE 8, e59043 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  103. Li, X.-Y. et al. Temperate phages as self-replicating weapons in bacterial competition. J. R. Soc. Interface 14, 20170563 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  104. Frazão, N. et al. Two modes of evolution shape bacterial strain diversity in the mammalian gut for thousands of generations. Nat. Commun. 13, 5604 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  105. Lei, J. et al. The antimicrobial peptides and their potential clinical applications. Am. J. Transl. Res. 11, 3919–3931 (2019).

    CAS  PubMed Central  PubMed  Google Scholar 

  106. Lazzaro, B. P., Zasloff, M. & Rolff, J. Antimicrobial peptides: application informed by evolution. Science 368, eaau5480 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  107. Baym, M. et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science 353, 1147–1151 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  108. Castle, S. D., Grierson, C. S. & Gorochowski, T. E. Towards an engineering theory of evolution. Nat. Commun. 12, 3326 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  109. Xie, L. & Shou, W. Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities. Nat. Commun. 12, 6799 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  110. Kuosmanen, T. et al. Drug-induced resistance evolution necessitates less aggressive treatment. PLoS Comput. Biol. 17, e1009418 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  111. Lin, A. et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci. Transl. Med. 11, eaaw8412 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  112. Force, T. & Kolaja, K. L. Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes. Nat. Rev. Drug Discov. 10, 111–126 (2011).

    Article  CAS  PubMed  Google Scholar 

  113. Harrison, R. K. Phase II and phase III failures: 2013–2015. Nat. Rev. Drug Discov. 15, 817–818 (2016).

    Article  CAS  PubMed  Google Scholar 

  114. Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).

    Article  CAS  PubMed  Google Scholar 

  115. Cairns, J., Jokela, R., Becks, L., Mustonen, V. & Hiltunen, T. Repeatable ecological dynamics govern the response of experimental communities to antibiotic pulse perturbation. Nat. Ecol. Evol. 4, 1385–1394 (2020).

    Article  PubMed  Google Scholar 

  116. Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  117. Janeway, C. A., Jr, Travers, P., Walport, M. & Shlomchik, M. J. Immunobiology (Garland Science, 2001).

  118. Shinnakasu, R. et al. Regulated selection of germinal-center cells into the memory B cell compartment. Nat. Immunol. 17, 861–869 (2016).

    Article  CAS  PubMed  Google Scholar 

  119. Viant, C. et al. Antibody affinity shapes the choice between memory and germinal center B cell fates. Cell 183, 1298–1311.e11 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  120. Mayer, A., Balasubramanian, V., Walczak, A. M. & Mora, T. How a well-adapting immune system remembers. Proc. Natl Acad. Sci. USA 116, 8815–8823 (2019). This theoretical work studies how adaptive immune repertoires should be organized to minimize the cost of infections in a given environment of pathogens.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  121. Röschinger, T., Tovar, R. M., Pompei, S. & Lässig, M. Adaptive ratchets and the evolution of molecular complexity. Preprint at arXiv https://doi.org/10.48550/arXiv.2111.09981 (2021).

    Article  Google Scholar 

  122. Schnaack, O. H. & Nourmohammad, A. Optimal evolutionary decision-making to store immune memory. eLife 10, e61346 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  123. Schnaack, O. H., Peliti, L. & Nourmohammad, A. Learning and organization of memory for evolving patterns. Phys. Rev. X 12, 021063 (2022).

    CAS  Google Scholar 

  124. Chardès, V., Vergassola, M., Walczak, A. M. & Mora, T. Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints. Proc. Natl Acad. Sci. USA 119, e2113512119 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  125. Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  126. Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010). This study establishes a quantitative model for growth-dependent allocation of proteome resources, which is an important prerequisite for metabolic control approaches.

    Article  CAS  PubMed  Google Scholar 

  127. Weiße, A. Y., Oyarzún, D. A., Danos, V. & Swain, P. S. Mechanistic links between cellular trade-offs, gene expression, and growth. Proc. Natl Acad. Sci. USA 112, E1038–E1047 (2015). This study develops a growth model for microbial cells that includes cell metabolism and nutrient intake, providing a computable link between environmental changes and eco-evolutionary dynamics.

    Article  PubMed Central  PubMed  Google Scholar 

  128. Dourado, H., Mori, M., Hwa, T. & Lercher, M. J. On the optimality of the enzyme-substrate relationship in bacteria. PLoS Biol. 19, e3001416 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  129. Posfai, A., Taillefumier, T. & Wingreen, N. S. Metabolic trade-offs promote diversity in a model ecosystem. Phys. Rev. Lett. 118, 028103 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  130. Good, B. H., Martis, S. & Hallatschek, O. Adaptation limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources. Proc. Natl Acad. Sci. USA 115, E10407–E10416 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  131. Ansari, A. F., Reddy, Y. B. S., Raut, J. & Dixit, N. M. An efficient and scalable top-down method for predicting structures of microbial communities. Nat. Comput. Sci. 1, 619–628 (2021).

    Article  Google Scholar 

  132. van den Berg, N. I. et al. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat. Ecol. Evol. 6, 855–865 (2022).

    Article  PubMed Central  PubMed  Google Scholar 

  133. Mora, T., Walczak, A. M., Bialek, W. & Callan, C. G. Jr. Maximum entropy models for antibody diversity. Proc. Natl Acad. Sci. USA 107, 5405–5410 (2010).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  134. Desponds, J., Mora, T. & Walczak, A. M. Fluctuating fitness shapes the clone-size distribution of immune repertoires. Proc. Natl Acad. Sci. USA 113, 274–279 (2016).

    Article  CAS  PubMed  Google Scholar 

  135. DeWitt, W. S. et al. Dynamics of the cytotoxic T cell response to a model of acute viral infection. J. Virol. 89, 4517–4526 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  136. Pogorelyy, M. V. et al. Detecting T cell receptors involved in immune responses from single repertoire snapshots. PLoS Biol. 17, e3000314 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  137. Nourmohammad, A., Otwinowski, J., Łuksza, M., Mora, T. & Walczak, A. M. Fierce selection and interference in B-cell repertoire response to chronic HIV-1. Mol. Biol. Evol. 36, 2184–2194 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  138. Snyder, T. M. et al. Magnitude and dynamics of the T-cell response to SARS-CoV-2 infection at both individual and population levels. Preprint at medRxiv https://doi.org/10.1101/2020.07.31.20165647 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  139. Nolan, S. et al. A large-scale database of T-cell receptor β (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2. Res. Sq. https://doi.org/10.21203/rs.3.rs-51964/v1 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  140. Minervina, A. A. et al. Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones. eLife 9, e53704 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  141. Montague, Z. et al. Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19. Cell Rep. 35, 109173 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  142. Minervina, A. A. et al. Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T-cell memory formation after mild COVID-19 infection. eLife 10, e63502 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  143. Pogorelyy, M. V. et al. Resolving SARS-CoV-2 CD4+ T cell specificity via reverse epitope discovery. Cell Rep. Med. 3, 100697 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  144. Mayer, A., Balasubramanian, V., Mora, T. & Walczak, A. M. How a well-adapted immune system is organized. Proc. Natl Acad. Sci. USA 112, 5950–5955 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  145. Bradde, S., Nourmohammad, A., Goyal, S. & Balasubramanian, V. The size of the immune repertoire of bacteria. Proc. Natl Acad. Sci. USA 117, 5144–5151 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  146. 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).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  147. 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).

    Article  PubMed  Google Scholar 

  148. Melnyk, A. H., Wong, A. & Kassen, R. The fitness costs of antibiotic resistance mutations. Evol. Appl. 8, 273–283 (2015).

    Article  PubMed  Google Scholar 

  149. Lawley, T. D. et al. Targeted restoration of the intestinal microbiota with a simple, defined bacteriotherapy resolves relapsing Clostridium difficile disease in mice. PLoS Pathog. 8, e1002995 (2012).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  151. Bitbol, A.-F. & Schwab, D. J. Quantifying the role of population subdivision in evolution on rugged fitness landscapes. PLoS Comput. Biol. 10, e1003778 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  152. Freitas, O., Wahl, L. M. & Campos, P. R. A. Robustness and predictability of evolution in bottlenecked populations. Phys. Rev. E 103, 042415 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed Central  PubMed  Google Scholar 

  154. Sella, G. & Hirsh, A. E. The application of statistical physics to evolutionary biology. Proc. Natl Acad. Sci. USA 102, 9541–9546 (2005).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  155. Rotem, A. et al. Evolution on the biophysical fitness landscape of an RNA virus. Mol. Biol. Evol. 35, 2390–2400 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  156. Maynard Smith, J. Evolution and the Theory of Games (Cambridge Univ. Press, 1982).

  157. Stanková, K., Brown, J. S., Dalton, W. S. & Gatenby, R. A. Optimizing cancer treatment using game theory: a review. JAMA Oncol. 5, 96–103 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  158. LaMont, C. et al. Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1. eLife 11, e76004 (2022). This study introduces a computational population genetics model to predict HIV escape from bNAbs and to devise optimal combination therapies of bNAbs that suppress HIV escape and rebound within patients.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  159. Meijers, M., Vanshylla, K., Gruell, H., Klein, F. & Lässig, M. Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody. Proc. Natl Acad. Sci. USA 118, e2104651118 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  160. Lee, J. M. et al. Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants. Proc. Natl Acad. Sci. USA 115, E8276–E8285 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  161. Wu, N. C. et al. Major antigenic site B of human influenza H3N2 viruses has an evolving local fitness landscape. Nat. Commun. 11, 1233 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  162. Starr, T. N. et al. Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell 182, 1295–1310.e20 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  163. Wang, Y., Lei, R., Nourmohammad, A. & Wu, N. C. Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing. eLife 10, e72516 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  164. Starr, T. N. et al. Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science 377, 420–424 (2022).

    Article  CAS  PubMed  Google Scholar 

  165. Phillips, A. M. et al. Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies. eLife 10, e71393 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  166. Moulana, A. et al. Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1. Nat. Commun. 13, 7011 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  167. Maher, M. C. et al. Predicting the mutational drivers of future SARS-CoV-2 variants of concern. Sci. Transl. Med. 14, eabk3445 (2022).

    Article  CAS  PubMed  Google Scholar 

  168. Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01618-2 (2023).

    Article  PubMed Central  PubMed  Google Scholar 

  169. Hie, B., Zhong, E. D., Berger, B. & Bryson, B. Learning the language of viral evolution and escape. Science 371, 284–288 (2021).

    Article  CAS  PubMed  Google Scholar 

  170. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  171. Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  172. Pun, M. N. et al. Learning the shape of protein micro-environments with a holographic convolutional neural network. Preprint at arXiv https://doi.org/10.48550/arXiv.2211.02936 (2022).

    Article  Google Scholar 

  173. Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  174. Vaishnav, E. D. et al. The evolution, evolvability and engineering of gene regulatory DNA. Nature 603, 455–463 (2022).

    Article  CAS  PubMed  Google Scholar 

  175. Treloar, N. J., Fedorec, A. J. H., Ingalls, B. & Barnes, C. P. Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comput. Biol. 16, e1007783 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  176. Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019). This study demonstrates that machine learning can guide the directed evolution of proteins by in silico fitness predictions.

    Article  CAS  PubMed  Google Scholar 

  177. Sinai, S. & Kelsic, E. D. A primer on model-guided exploration of fitness landscapes for biological sequence design. Preprint at arXiv https://doi.org/10.48550/arXiv.2010.10614 (2020).

    Article  Google Scholar 

  178. Udrescu, S.-M. & Tegmark, M. AI Feynman: a physics-inspired method for symbolic regression. Sci. Adv. 6, eaay2631 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  179. Stengel, R. F. Optimal Control and Estimation (Courier, 1994).

  180. Black, F. & Scholes, M. The pricing of options and corporate liabilities. J. Polit. Econ. 81, 637–654 (1973).

    Article  Google Scholar 

  181. Merton, R. C. Theory of rational option pricing. Bell J. Econ. Manag. Sci. 4, 141–183 (1973).

    Article  Google Scholar 

  182. Bellman, R. On the theory of dynamic programming. Proc. Natl Acad. Sci. USA 38, 716–719 (1952).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  183. Kappen, H. J. An introduction to stochastic control theory, path integrals and reinforcement learning. AIP Conf. Proc. 887, 149–181 (2007).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  185. Iram, S. et al. Controlling the speed and trajectory of evolution with counterdiabatic driving. Nat. Phys. 17, 135–142 (2020).

    Article  Google Scholar 

  186. Champer, J. et al. Molecular safeguarding of CRISPR gene drive experiments. eLife 8, e41439 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  187. Wright, O., Stan, G.-B. & Ellis, T. Building-in biosafety for synthetic biology. Microbiology 159, 1221–1235 (2013).

    Article  CAS  PubMed  Google Scholar 

  188. Daley, G. Q., Lovell-Badge, R. & Steffann, J. After the storm — a responsible path for genome editing. N. Engl. J. Med. 380, 897–899 (2019).

    Article  PubMed  Google Scholar 

  189. Mandell, D. J. et al. Biocontainment of genetically modified organisms by synthetic protein design. Nature 518, 55–60 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  190. Chan, C. T. Y., Lee, J. W., Cameron, D. E., Bashor, C. J. & Collins, J. J. “Deadman” and “Passcode” microbial kill switches for bacterial containment. Nat. Chem. Biol. 12, 82–86 (2016).

    Article  CAS  PubMed  Google Scholar 

  191. zur Wiesch, P. A., 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  PubMed  Google Scholar 

  192. Larsen, A. C. et al. A general strategy for expanding polymerase function by droplet microfluidics. Nat. Commun. 7, 11235 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  193. Chen, H. et al. Efficient, continuous mutagenesis in human cells using a pseudo-random. DNA editor. Nat. Biotechnol. 38, 165–168 (2020).

    Article  CAS  PubMed  Google Scholar 

  194. Cravens, A., Jamil, O. K., Kong, D., Sockolosky, J. T. & Smolke, C. D. Polymerase-guided base editing enables in vivo mutagenesis and rapid protein engineering. Nat. Commun. 12, 1579 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  195. Rix, G. & Liu, C. C. Systems for in vivo hypermutation: a quest for scale and depth in directed evolution. Curr. Opin. Chem. Biol. 64, 20–26 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  196. Shi, C., Wang, C., Lu, J., Zhong, B. & Tang, J. Protein sequence and structure co-design with equivariant translation. Paper presented at the 11th International Conference on Learning Representations https://openreview.net/forum?id=pRCMXcfdihq (2023).

  197. Schuler, T. H., Poppy, G. M., Kerry, B. R. & Denholm, I. Insect-resistant transgenic plants. Trends Biotechnol. 16, 168–175 (1998).

    Article  CAS  Google Scholar 

  198. Castle, L. A. et al. Discovery and directed evolution of a glyphosate tolerance gene. Science 304, 1151–1154 (2004).

    Article  CAS  PubMed  Google Scholar 

  199. Douglas, A. E. Strategies for enhanced crop resistance to insect pests. Annu. Rev. Plant. Biol. 69, 637–660 (2018).

    Article  CAS  PubMed  Google Scholar 

  200. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  201. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    Article  CAS  PubMed  Google Scholar 

  202. Perelson, A. S. et al. Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387, 188–191 (1997).

    Article  CAS  PubMed  Google Scholar 

  203. Baym, M., Stone, L. K. & Kishony, R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science 351, aad3292 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  204. Wang, K. K. et al. A hybrid drug limits resistance by evading the action of the multiple antibiotic resistance pathway. Mol. Biol. Evol. 33, 492–500 (2016).

    Article  CAS  PubMed  Google Scholar 

  205. Feder, A. F. et al. More effective drugs lead to harder selective sweeps in the evolution of drug resistance in HIV-1. eLife 5, e10670 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  206. Zhang, F. et al. Optimal combination treatment regimens of vaccine and radiotherapy augment tumor-bearing host immunity. Commun. Biol. 4, 78 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  207. Malherbe, D. C. et al. Sequential immunization with a subtype B HIV-1 envelope quasispecies partially mimics the in vivo development of neutralizing antibodies. J. Virol. 85, 5262–5274 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  208. Klasse, P. J. et al. Sequential and simultaneous immunization of rabbits with HIV-1 envelope glycoprotein SOSIP.664 trimers from clades A, B and C. PLoS Pathog. 12, e1005864 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  209. Mohan, T., Berman, Z., Kang, S.-M. & Wang, B.-Z. Sequential immunizations with a panel of HIV-1 Env virus-like particles coach immune system to make broadly neutralizing antibodies. Sci. Rep. 8, 7807 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  210. Miyamoto, S. et al. Vaccination-infection interval determines cross-neutralization potency to SARS-CoV-2 Omicron after breakthrough infection by other variants. Med 3, 249–261.e4 (2022).

    Article  CAS  PubMed  Google Scholar 

  211. Lu, C.-L. et al. Enhanced clearance of HIV-1-infected cells by broadly neutralizing antibodies against HIV-1 in vivo. Science 352, 1001–1004 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  212. Ragheb, M. N. et al. Inhibiting the evolution of antibiotic resistance. Mol. Cell 73, 157–165.e5 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  213. Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).

    Article  PubMed Central  PubMed  Google Scholar 

  214. Marchi, J., Lässig, M., Walczak, A. M. & Mora, T. Antigenic waves of virus-immune coevolution. Proc. Natl Acad. Sci. USA 118, e2103398118 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  215. Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505–510 (1990).

    Article  CAS  PubMed  Google Scholar 

  216. Fernandez-Gacio, A., Uguen, M. & Fastrez, J. Phage display as a tool for the directed evolution of enzymes. Trends Biotechnol. 21, 408–414 (2003).

    Article  CAS  PubMed  Google Scholar 

  217. Brudno, Y., Birnbaum, M. E., Kleiner, R. E. & Liu, D. R. An in vitro translation, selection and amplification system for peptide nucleic acids. Nat. Chem. Biol. 6, 148–155 (2010).

    Article  CAS  PubMed  Google Scholar 

  218. van Bloois, E., Winter, R. T., Kolmar, H. & Fraaije, M. W. Decorating microbes: surface display of proteins on Escherichia coli. Trends Biotechnol. 29, 79–86 (2011).

    Article  PubMed  Google Scholar 

  219. Rovner, A. J. et al. Recoded organisms engineered to depend on synthetic amino acids. Nature 518, 89–93 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  220. Blind, M. & Blank, M. Aptamer selection technology and recent advances. Mol. Ther. Nucleic Acids 4, e223 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  221. Wong, B. G., Mancuso, C. P., Kiriakov, S., Bashor, C. J. & Khalil, A. S. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat. Biotechnol. 36, 614–623 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  222. Rice, L. B. The clinical consequences of antimicrobial resistance. Curr. Opin. Microbiol. 12, 476–481 (2009).

    Article  CAS  PubMed  Google Scholar 

  223. Laxminarayan, R. Antibiotic effectiveness: balancing conservation against innovation. Science 345, 1299–1301 (2014).

    Article  CAS  PubMed  Google Scholar 

  224. Moura de Sousa, J., Balbontín, R., Durão, P. & Gordo, I. Multidrug-resistant bacteria compensate for the epistasis between resistances. PLoS Biol. 15, e2001741 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  225. Wistrand-Yuen, E. et al. Evolution of high-level resistance during low-level antibiotic exposure. Nat. Commun. 9, 1599 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  226. Durão, P., Balbontín, R. & Gordo, I. Evolutionary mechanisms shaping the maintenance of antibiotic resistance. Trends Microbiol. 26, 677–691 (2018).

    Article  PubMed  Google Scholar 

  227. Vasan, N., Baselga, J. & Hyman, D. M. A view on drug resistance in cancer. Nature 575, 299–309 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  228. Szybalski, W. & Bryson, V. Genetic studies on microbial cross resistance to toxic agents. I. Cross resistance of Escherichia coli to fifteen antibiotics. J. Bacteriol. 64, 489–499 (1952).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  229. Oz, T. et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol. Biol. Evol. 31, 2387–2401 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  230. Levin-Reisman, I. et al. Antibiotic tolerance facilitates the evolution of resistance. Science 355, 826–830 (2017).

    Article  CAS  PubMed  Google Scholar 

  231. Levin-Reisman, I., Brauner, A., Ronin, I. & Balaban, N. Q. Epistasis between antibiotic tolerance, persistence, and resistance mutations. Proc. Natl Acad. Sci. USA 116, 14734–14739 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  232. Vega, N. M. & Gore, J. Collective antibiotic resistance: mechanisms and implications. Curr. Opin. Microbiol. 21, 28–34 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  233. Sorg, R. A. et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLoS Biol. 14, e2000631 (2016).

    Article  PubMed Central  PubMed  Google Scholar 

  234. de Vos, M. G. J., Zagorski, M., McNally, A. & Bollenbach, T. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. Proc. Natl Acad. Sci. USA 114, 10666–10671 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  235. Klümper, U. et al. Selection for antimicrobial resistance is reduced when embedded in a natural microbial community. ISME J. 13, 2927–2937 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  236. Bottery, M. J., Pitchford, J. W. & Friman, V.-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 15, 939–948 (2020).

    Article  PubMed Central  PubMed  Google Scholar 

  237. Witte, W. Medical consequences of antibiotic use in agriculture. Science 279, 996–997 (1998).

    Article  CAS  PubMed  Google Scholar 

  238. Bawa, A. S. & Anilakumar, K. R. Genetically modified foods: safety, risks and public concerns — a review. J. Food Sci. Technol. 50, 1035–1046 (2013).

    Article  CAS  PubMed  Google Scholar 

  239. Gilbert, N. Case studies: a hard look at GM crops. Nature https://doi.org/10.1038/497024a (2013).

    Article  PubMed  Google Scholar 

  240. Hawkins, N. J., Bass, C., Dixon, A. & Neve, P. The evolutionary origins of pesticide resistance. Biol. Rev. Camb. Philos. Soc. 94, 135–155 (2018).

    Article  PubMed Central  PubMed  Google Scholar 

  241. Aarestrup, F. M. and Schwarz, S. in Antimicrobial Resistance in Bacteria of Animal Origin (ed. Aarestrup, F. M.) 187–212 (ASM Press, 2019).

  242. Mann, A., Nehra, K., Rana, J. S. & Dahiya, T. Antibiotic resistance in agriculture: perspectives on upcoming strategies to overcome upsurge in resistance. Curr. Res. Microb. Sci. 2, 100030 (2021).

    CAS  PubMed Central  PubMed  Google Scholar 

  243. Flynn, J. L. & Chan, J. Tuberculosis: latency and reactivation. Infect. Immun. 69, 4195–4201 (2001).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  244. Bailey, J., Blankson, J. N., Wind-Rotolo, M. & Siliciano, R. F. Mechanisms of HIV-1 escape from immune responses and antiretroviral drugs. Curr. Opin. Immunol. 16, 470–476 (2004).

    Article  CAS  PubMed  Google Scholar 

  245. Lin, P. L. & Flynn, J. L. Understanding latent tuberculosis: a moving target. J. Immunol. 185, 15–22 (2010).

    Article  CAS  PubMed  Google Scholar 

  246. Perng, G.-C. & Jones, C. Towards an understanding of the herpes simplex virus type 1 latency-reactivation cycle. Interdiscip. Perspect. Infect. Dis. 2010, 262415 (2010).

    Article  PubMed Central  PubMed  Google Scholar 

  247. Cohn, L. B., Chomont, N. & Deeks, S. G. The biology of the HIV-1 latent reservoir and implications for cure strategies. Cell Host Microbe 27, 519–530 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  248. Chen, Y., Jungsuwadee, P., Vore, M., Butterfield, D. A. & St Clair, D. K. Collateral damage in cancer chemotherapy: oxidative stress in nontargeted tissues. Mol. Interv. 7, 147–156 (2007).

    Article  CAS  PubMed  Google Scholar 

  249. Kostine, M. et al. Opportunistic autoimmunity secondary to cancer immunotherapy (OASI): an emerging challenge. Rev. Med. Interne 38, 513–525 (2017).

    Article  CAS  PubMed  Google Scholar 

  250. Pauken, K. E., Dougan, M., Rose, N. R., Lichtman, A. H. & Sharpe, A. H. Adverse events following cancer immunotherapy: obstacles and opportunities. Trends Immunol. 40, 511–523 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  251. Albero, B., Tadeo, J. L., Escario, M., Miguel, E. & Pérez, R. A. Persistence and availability of veterinary antibiotics in soil and soil-manure systems. Sci. Total. Environ. 643, 1562–1570 (2018).

    Article  CAS  PubMed  Google Scholar 

  252. Iwu, C. D., Korsten, L. & Okoh, A. I. The incidence of antibiotic resistance within and beyond the agricultural ecosystem: a concern for public health. Microbiologyopen 9, e1035 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  253. Galon, J., Angell, H. K., Bedognetti, D. & Marincola, F. M. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 39, 11–26 (2013).

    Article  CAS  PubMed  Google Scholar 

  254. Gire, S. K. et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 (2014).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  255. Gardy, J., Loman, N. J. & Rambaut, A. Real-time digital pathogen surveillance — the time is now. Genome Biol. 16, 155 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  256. Zanini, F. et al. Population genomics of intrapatient HIV-1 evolution. eLife 4, e11282 (2015).

    Article  PubMed Central  PubMed  Google Scholar 

  257. Kugelman, J. R. et al. Monitoring of Ebola virus Makona evolution through establishment of advanced genomic capability in Liberia. Emerg. Infect. Dis. 21, 1135–1143 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  258. Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  259. Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  260. Wyres, K. L. et al. Genomic surveillance of antimicrobial resistant bacterial colonisation and infection in intensive care patients. BMC Infect. Dis. 21, 683 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  261. Lam, M. M. C. et al. A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex. Nat. Commun. 12, 4188 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  262. Chen, Z. et al. Global landscape of SARS-CoV-2 genomic surveillance and data sharing. Nat. Genet. 54, 499–507 (2022).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  263. Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    Article  CAS  PubMed  Google Scholar 

  264. Scott, M. & Hwa, T. Bacterial growth laws and their applications. Curr. Opin. Biotechnol. 22, 559–565 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  265. Dourado, H. & Lercher, M. J. An analytical theory of balanced cellular growth. Nat. Commun. 11, 1226 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  266. Gowda, K., Ping, D., Mani, M. & Kuehn, S. Genomic structure predicts metabolite dynamics in microbial communities. Cell 185, 530–546.e25 (2022).

    Article  CAS  PubMed  Google Scholar 

  267. Kinney, J. B. & McCandlish, D. M. Massively parallel assays and quantitative sequence-function relationships. Annu. Rev. Genomics Hum. Genet. 20, 99–127 (2019).

    Article  CAS  PubMed  Google Scholar 

  268. Verkuil, R. et al. Language models generalize beyond natural proteins. Preprint at bioRxiv https://doi.org/10.1101/2022.12.21.521521 (2022).

    Article  Google Scholar 

  269. Bialek, W. & Tishby, N. Predictive Information. Preprint at arXiv https://doi.org/10.48550/arXiv.cond-mat/9902341 (1999).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank P. Jouhten and A. Khalil for additional information on their work, and M. Łuksza for input to Fig. 1. The authors’ work has been funded in part by Deutsche Forschungsgemeinschaft (grant CRC 1310 to M.L. and A.N.), Academy of Finland (grant no. 339496 and 346128 to V.M.), CAREER award from the National Science Foundation (grant 2045054 to A.N.), the National Institutes of Health MIRA award (R35 GM142795 to A.N.) and the Department of Physics and the College of Arts and Sciences at the University of Washington.

Author information

Author notes

  1. These authors contributed equally: Michael Lässig, Ville Mustonen, Armita Nourmohammad.

Authors and Affiliations

  1. Institute for Biological Physics, University of Cologne, Cologne, Germany

    Michael Lässig

  2. Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, Helsinki, Finland

    Ville Mustonen

  3. Department of Physics, University of Washington, Seattle, WA, USA

    Armita Nourmohammad

  4. Department of Applied Mathematics, University of Washington, Seattle, WA, USA

    Armita Nourmohammad

  5. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Armita Nourmohammad

  6. Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Armita Nourmohammad

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. Armita Nourmohammad
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to all aspects of the article.

Corresponding authors

Correspondence to Michael Lässig, Ville Mustonen or Armita Nourmohammad.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Genetics thanks J. Arjan G. M. de Visser and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Action threshold

A boundary between parameter regimes of control protocols with higher/lower payoff than in the absence of control.

Adaptive evolution

The accumulation of heritable genetic changes that increase fitness in a given environment.

Adaptive learning

Evolutionary processes where the increase of information is coupled to a fitness benefit.

Artificial selection

Fitness effects in a target population induced by human intervention (in contrast to natural selection).

Co-evolution

The coupled evolution of two or more species interacting by natural selection, biological interactions and dependencies.

Directed evolution experiments

Laboratory protocols where organisms or biomolecules with desired traits are generated and amplified through iterative rounds of mutation and selection.

Eco-evolutionary dynamics

The coupled dynamics of population sizes, genetic changes and interactions between multiple species in an ecosystem.

Fitness seascape

A moving fitness landscape, generating selective forces that explicitly depend on time.

Greedy control

Algorithms with update rules that increase the instantaneous payoff.

Immunotherapy

The prevention or treatment of disease with substances that invoke immune responses.

Microbial communities

Multiple species of microorganisms that live together in a shared environment and interact with each other.

Molecular traits

Components of the molecular machinery of the cell relevant for a specific function. Examples include gene expression levels, binding affinities and activities of enzymes.

Nash equilibria

States of a game where no player can increase their payoff by unilaterally changing their strategy.

Prediction horizon

The timescale over which a computational model provides significant information about future evolutionary trajectories.

Pre-emptive control

Algorithms with update rules that increase payoff over future time periods.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lässig, M., Mustonen, V. & Nourmohammad, A. Steering and controlling evolution — from bioengineering to fighting pathogens. Nat Rev Genet 24, 851–867 (2023). https://doi.org/10.1038/s41576-023-00623-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-023-00623-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer