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The sociobiology of molecular systems

Nature Reviews Genetics volume 12, pages 193–203 (2011)Cite this article

Subjects

Key Points

  • It is often assumed that molecular systems are optimized to maximize the competitive ability of the organism.

  • Sociobiology predicts that molecular systems are shaped by cooperative and competitive phenotypes across multiple levels of biological organization.

  • Low-level selection for competition can disrupt social systems, as illustrated by transposable elements, cancer and disease.

  • High-level selection for cooperation can promote social systems, as illustrated by genomes, multicellularity and animal societies.

  • Cooperative and competitive forces shape four features of molecular systems: their functional scale, connections, diversity and rate of change.

  • The functional scale of molecular systems — the number of genes that work together towards a common goal — is shaped by the potential for competition.

  • The number and nature of connections in molecular networks is shaped by competition through links to modularity, robustness and the specificity of interactions.

  • Natural selection to generate diverse interacting phenotypes has strongly shaped the molecular networks of cooperation systems, including multicellular development.

  • Social evolution influences the rates at which molecular networks change in real and evolutionary time.

  • There is a strong case for integrating the theories of sociobiology into the study of molecular systems.

Abstract

It is often assumed that molecular systems are designed to maximize the competitive ability of the organism that carries them. In reality, natural selection acts on both cooperative and competitive phenotypes, across multiple scales of biological organization. Here I ask how the potential for social effects in evolution has influenced molecular systems. I discuss a range of phenotypes, from the selfish genetic elements that disrupt genomes, through metabolism, multicellularity and cancer, to behaviour and the organization of animal societies. I argue that the balance between cooperative and competitive evolution has shaped both form and function at the molecular scale.

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Figure 1: Multilevel selection.
Figure 2: Properties of biological networks.
Figure 3: Hamilton's classes of social phenotype.

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References

  1. Huxley, J. Evolution: the Modern Synthesis (Harper & Brothers, 1942).

    Google Scholar 

  2. Hamilton, W. D. The genetical evolution of social behaviour. I & II. J. Theor. Biol. 7, 1–52 (1964). This seminal paper shows how natural selection can favour the evolution of cooperative and altruistic phenotypes.

    Article  CAS  PubMed  Google Scholar 

  3. Wilson, D. S. A theory of group selection. Proc. Natl Acad. Sci. USA 72, 143–146 (1975).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Okasha, S. Evolution and the Levels of Selection (Oxford Univ. Press, USA, 2006). This book works through the theoretical basis for natural selection at multiple levels of biological organization.

    Google Scholar 

  5. Robinson, G. E. Integrative animal behaviour and sociogenomics. Trends Ecol. Evol. 14, 202–205 (1999).

    CAS  PubMed  Google Scholar 

  6. Smith, C. R., Toth, A. L., Suarez, A. V. & Robinson, G. E. Genetic and genomic analyses of the division of labour in insect societies. Nature Rev. Genet. 9, 735–748 (2008).

    CAS  PubMed  Google Scholar 

  7. Robinson, G. E., Grozinger, C. M. & Whitfield, C. W. Sociogenomics: social life in molecular terms. Nature Rev. Genet. 6, 257–270 (2005).

    CAS  PubMed  Google Scholar 

  8. Owens, I. P. F. Where is behavioural ecology going? Trends Ecol. Evol. 21, 356–361 (2006).

    PubMed  Google Scholar 

  9. Foster, K. R., Parkinson, K. & Thompson, C. R. What can microbial genetics teach sociobiology? Trends Genet. 23, 74–80 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Grafen, A. in Behavioural Ecology: An Evolutionary Approach (eds Krebs, J. R. & Davies, N. B.) 62–84 (Blackwell, Oxford, 1984).

    Google Scholar 

  11. Banga, J. Optimization in computational systems biology. BMC Syst. Biol. 2, 47 (2008).

    PubMed  PubMed Central  Google Scholar 

  12. Hartwell, L., Hopfield, J., Leibler, S. & Murray, A. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    CAS  PubMed  Google Scholar 

  13. Gardner, A. Adaptation as organism design. Biol. Lett. 5, 861–864 (2009). This article discusses the links between adaptation and the appearance of design in biological systems.

    PubMed  PubMed Central  Google Scholar 

  14. Medina, M. Genomes, phylogeny, and evolutionary systems biology. Proc. Natl Acad. Sci. USA 102, 6630–6635 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Soyer, O. The promise of evolutionary systems biology: Lessons from bacterial chemotaxis. Sci. STKE 3, pe23 (2010).

    Google Scholar 

  16. Lynch, M. The evolution of genetic networks by non-adaptive processes. Nature Rev. Genet. 8, 803–813 (2007).

    CAS  PubMed  Google Scholar 

  17. Grafen, A. A first formal link between the price equation and an optimization program. J. Theor. Biol. 217, 75–91 (2002).

    PubMed  Google Scholar 

  18. Sutherland, W. The best solution. Nature 435, 569 (2005).

    CAS  PubMed  Google Scholar 

  19. Krebs, J., Kacelnik, A. & Taylor, P. Test of optimal sampling by foraging great tits. Nature 275, 27–31 (1978).

    Google Scholar 

  20. Pérez-Escudero, A., Rivera-Alba, M. & de Polavieja, G. Structure of deviations from optimality in biological systems. Proc. Natl Acad. Sci. USA 106, 20544–20549 (2009).

    PubMed  PubMed Central  Google Scholar 

  21. Grafen, A. Optimization of inclusive fitness. J. Theor. Biol. 238, 541–563 (2006).

    PubMed  Google Scholar 

  22. Gardner, A. & Grafen, A. Capturing the superorganism: a formal theory of group adaptation. J. Evol. Biol. 22, 659–671 (2009).

    CAS  PubMed  Google Scholar 

  23. Foster, K. R. A defense of sociobiology. Cold Spring Harb. Symp. Quant. Biol. LXXIV, 403–418 (2009). A review of the debates in social evolution that highlights the common principles underlying all sociobiological theory.

    Google Scholar 

  24. El-Samad, H., Kurata, H., Doyle, J., Gross, C. & Khammash, M. Surviving heat shock: control strategies for robustness and performance. Proc. Natl Acad. Sci. USA 102, 2736–2741 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nature Rev. Genet. 8, 921–931 (2007).

    CAS  PubMed  Google Scholar 

  26. Burt, A. & Trivers, R. L. Genes in Conflict: The Biology of Selfish Genetic Elements (Harvard Univ. Press, Harvard, 2006).

    Google Scholar 

  27. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  28. Proulx, S. R., Promislow, D. E. L. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evol. 20, 345–353 (2005).

    PubMed  Google Scholar 

  29. Tringe, S. G. et al. Comparative metagenomics of microbial communities. Science 308, 554–557 (2005).

    CAS  PubMed  Google Scholar 

  30. Foster, K. R. & Wenseleers, T. A general model for the evolution of mutualisms. J. Evol. Biol. 19, 1283–1293 (2006).

    CAS  PubMed  Google Scholar 

  31. Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Nadell, C. D., Xavier, J. B. & Foster, K. R. The sociobiology of biofilms. FEMS Microbiol. Rev. 33, 206–224 (2009).

    CAS  PubMed  Google Scholar 

  33. Edwards, J. & Palsson, B. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinformatics 1, 1 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Edwards, J., Ibarra, R. & Palsson, B. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotech. 19, 125–130 (2001).

    CAS  Google Scholar 

  35. Reed, T. et al. In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study. BMC Syst. Biol. 3, 114 (2009).

    Google Scholar 

  36. Schuetz, R., Kuepfer, L. & Sauer, U. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Mol. Syst. Biol. 3, 119 (2007). This paper on flux balance analysis shows that bacterial metabolism seems to be designed to solve more than one biological problem.

    PubMed  PubMed Central  Google Scholar 

  37. Postma, E., Verduyn, C., Scheffers, W. & Van Dijken, J. Enzymic analysis of the crabtree effect in glucose-limited chemostat cultures of Saccharomyces cerevisiae. Appl. Environ. Microbiol. 55, 468–477 (1989).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Pfeiffer, T., Schuster, S. & Bonhoeffer, S. Cooperation and competition in the evolution of ATP-producing pathways. Science 292, 504–507 (2001). Theoretical study that shows the importance of social evolution for metabolic systems.

    CAS  PubMed  Google Scholar 

  39. MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 441, 498–501 (2006).

    CAS  PubMed  Google Scholar 

  40. Hsu, P. P. & Sabatini, D. M. Cancer cellmetabolism: Warburg and beyond. Cell 134, 703–707 (2008).

    CAS  PubMed  Google Scholar 

  41. Warburg, O. Uber die letzte Ursache und die entfernten Ursachen des Krebses (Verlag K. Triltsch, Wurzburg, 1966); English translation in Burk, D. The Prime Cause and Prevention of Cancer (1969).

    Google Scholar 

  42. Tyler, A., Asselbergs, F., Williams, S. & Moore, J. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy. BioEssays 31, 220–227 (2009).

    PubMed  PubMed Central  Google Scholar 

  43. Wagner, G. Homologues, natural kinds and the evolution of modularity. Integr. Comp. Biol. 36, 36–43 (1996).

    Google Scholar 

  44. Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl Acad. Sci. USA 102, 13773–13778 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Solé, R. & Valverde, S. Spontaneous emergence of modularity in cellular networks. J. R. Soc. Interface 5, 129–133 (2008).

    PubMed  Google Scholar 

  46. Rumbaugh, K. P. et al. Quorum sensing and the social evolution of bacterial virulence. Curr. Biol. 19, 341–345 (2009).

    CAS  PubMed  Google Scholar 

  47. Foster, K. R., Shaulsky, G., Strassmann, J. E., Queller, D. C. & Thompson, C. R. L. Pleiotropy as a mechanism to stabilize cooperation. Nature 431, 693–696 (2004).

    CAS  PubMed  Google Scholar 

  48. Merlo, L. M., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nature Rev. Cancer 6, 924–935 (2006).

    CAS  Google Scholar 

  49. Park, S. Y., Gonen, M., Kim, H. J., Michor, F. & Polyak, K. Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. J. Clin. Invest. 120, 636–644 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Vogelstein, B. & Kinzler, K. Cancer genes and the pathways they control. Nature Med. 10, 789–799 (2004).

    CAS  PubMed  Google Scholar 

  51. Reuschenbach, M., von Knebel Doeberitz, M. & Wentzensen, N. A systematic review of humoral immune responses against tumor antigens. Cancer Immunol. Immunother. 58, 1535–1544 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Krakauer, D. & Plotkin, J. Redundancy, antiredundancy, and the robustness of genomes. Proc. Natl Acad. Sci. USA 99, 1405–1409 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Wilke, C., Wang, J., Ofria, C., Lenski, R. & Adami, C. Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412, 331–333 (2001).

    CAS  PubMed  Google Scholar 

  54. Salathe, M. & Soyer, O. S. Parasites lead to evolution of robustness against gene loss in host signaling networks. Mol. Syst. Biol. 4, 202 (2008). Theoretical study showing a link between parasites and the evolution of mutational robustness in hosts.

    PubMed  PubMed Central  Google Scholar 

  55. Wagner, A. & Wright, J. Alternative routes and mutational robustness in complex regulatory networks. Biosystems 88, 163–172 (2007).

    PubMed  Google Scholar 

  56. Soyer, O. & Pfeiffer, T. Evolution under fluctuating environments explains observed robustness in metabolic networks. PLoS Comput. Biol. 6, e1000907 (2010).

    PubMed  PubMed Central  Google Scholar 

  57. Wagner, A. Distributed robustness versus redundancy as causes of mutational robustness. BioEssays 27, 176–188 (2005).

    CAS  PubMed  Google Scholar 

  58. Ihmels, J., Collins, S., Schuldiner, M., Krogan, N. & Weissman, J. Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss. Mol. Syst. Biol. 3, 86 (2007).

    PubMed  PubMed Central  Google Scholar 

  59. Macia, J. & Solé, R. Distributed robustness in cellular networks: insights from synthetic evolved circuits. J. R. Soc. Interface 6, 393–400 (2009).

    PubMed  Google Scholar 

  60. Sachs, J. L. & Bull, J. J. Experimental evolution of conflict mediation between genomes. Proc. Natl Acad. Sci. USA 102, 390–395 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Smukalla, S. et al. FLO1 is a variable green beard gene that drives biofilm-like cooperation in budding yeast. Cell 135, 727–736 (2008).

    Google Scholar 

  62. Garneau, J. et al. The CRISPR/Cas bacterial immune system cleaves bacteriophage and plasmid DNA. Nature 468, 67–71 (2010).

    CAS  PubMed  Google Scholar 

  63. Marraffini, L. & Sontheimer, E. CRISPR interference limits horizontal gene transfer in staphylococci by targeting DNA. Science 322, 1843–1845 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Gardner, A., West, S. A. & Buckling, A. Bacteriocins, spite and virulence. Proc. Biol. Sci. 271, 1529–1535 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Cascales, E. et al. Colicin biology. Microbiol. Mol. Biol. Rev. 71, 158–229 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Nakayama, K. et al. The R-type pyocin of Pseudomonas aeruginosa is related to P2 phage, and the F-type is related to lambda phage. Mol. Microbiol. 38, 213–231 (2000).

    CAS  PubMed  Google Scholar 

  67. Kohler, T., Donner, V. & van Delden, C. Lipopolysaccharide as shield and receptor for R-pyocin-mediated killing in Pseudomonas aeruginosa. J. Bacteriol. 192, 1921 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Nicotra, M. L. et al. A hypervariable invertebrate allodeterminant. Curr. Biol. 19, 583–589 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. De Tomaso, A. W. et al. Isolation and characterization of a protochordate histocompatibility locus. Nature 438, 454–459 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Hedrick, P. W. Evolutionary genetics of the major histocompatibility complex. Am. Nat. 143, 945–964 (1994).

    Google Scholar 

  71. Siddle, H. V. et al. Transmission of a fatal clonal tumor by biting occurs due to depleted MHC diversity in a threatened carnivorous marsupial. Proc. Natl Acad. Sci. USA 104, 16221–16226 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Rousset, F. & Roze, D. Constraints on the origin and maintenance of genetic kin recognition. Evolution 61, 2320–2330 (2007).

    PubMed  Google Scholar 

  73. Slack, E. et al. Innate and adaptive immunity cooperate flexibly to maintain host-microbiota mutualism. Science 325, 617–620 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Rakoff-Nahoum, S., Paglino, J., Eslami-Varzaneh, F., Edberg, S. & Medzhitov, R. Recognition of commensal microflora by Toll-like receptors is required for intestinal homeostasis. Cell 118, 229–241 (2004).

    CAS  PubMed  Google Scholar 

  75. Mandeville, B. The Fable of the Bees: of Private Vices, Public Benefits (Oxford: Clarendon Press, 1732).

    Google Scholar 

  76. Scheffer, M. & van Nes, E. H. Self-organized similarity, the evolutionary emergence of groups of similar species. Proc. Natl Acad. Sci. USA 103, 6230–6235 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Shoresh, N., Hegreness, M. & Kishony, R. Evolution exacerbates the paradox of the plankton. Proc. Natl Acad. Sci. USA 105, 12365–12369 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Queller, D. C. Relatedness and the fraternal major transitions. Phil. Trans. R. Soc. Lond. B 355, 1647–1655 (2000). A discussion paper that highlights the differences between coalitions of genes and species versus coalitions of cells and organisms of one species.

    CAS  Google Scholar 

  79. Süel, G., Garcia-Ojalvo, J., Liberman, L. & Elowitz, M. An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440, 545–550 (2006).

    PubMed  Google Scholar 

  80. Elango, N., Hunt, B. G., Goodisman, M. A. D. & Yi, S. V. DNA methylation is widespread and associated with differential gene expression in castes of the honeybee, Apis mellifera. Proc. Natl Acad. Sci. USA 106, 11206–11211 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Behura, S. K. & Whitfield, C. W. Correlated expression patterns of microRNA genes with age-dependent behavioural changes in honeybee. Insect Mol. Biol. 19, 431–439 (2010).

    CAS  PubMed  Google Scholar 

  82. Kucharski, R., Maleszka, J., Foret, S. & Maleszka, R. Nutritional control of reproductive status in honeybees via DNA methylation. Science 319, 1827–1830 (2008). An RNAi study showing the importance of DNA methylation for queen–worker determination in honeybees.

    CAS  PubMed  Google Scholar 

  83. Aamodt, R. M. The caste- and age-specific expression signature of honeybee heat shock genes shows an alternative splicing-dependent regulation of Hsp90. Mech. Ageing Dev. 129, 632–637 (2008).

    CAS  PubMed  Google Scholar 

  84. Calarco, J. A. et al. Regulation of vertebrate nervous system alternative splicing and development by an SR-related protein. Cell 138, 898–910 (2009).

    CAS  PubMed  Google Scholar 

  85. Penny, D., Hoeppner, M. P., Poole, A. M. & Jeffares, D. C. An overview of the introns-first theory. J. Mol. Evol. 69, 527–540 (2009).

    CAS  PubMed  Google Scholar 

  86. Agrawal, A., Eastman, Q. M. & Schatz, D. G. Transposition mediated by RAG1 and RAG2 and its implications for the evolution of the immune system. Nature 394, 744–751 (1998).

    CAS  PubMed  Google Scholar 

  87. Bourke, A. F. G. & Franks, N. R. Social Evolution in Ants (Princeton Univ. Press, Princeton, New Jersey, 1995).

    Google Scholar 

  88. Ratnieks, F. L. W., Foster, K. R. & Wenseleers, T. Conflict resolution in insect societies. Annu. Rev. Entomol. 51, 581–608 (2006).

    CAS  PubMed  Google Scholar 

  89. Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211, 1390–1396 (1981).

    CAS  PubMed  Google Scholar 

  90. Perkins, T. J. & Swain, P. S. Strategies for cellular decision-making. Mol. Syst. Biol. 5, 326 (2009).

    PubMed  PubMed Central  Google Scholar 

  91. Fuqua, W. C., Winans, S. C. & Greenberg, E. P. Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators. J. Bacteriol. 176, 269–275 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Keller, L. & Surette, M. G. Communication in bacteria: an ecological and evolutionary perspective. Nature Rev. Microbiol. 4, 249–258 (2006).

    CAS  Google Scholar 

  93. Miller, M. B. & Bassler, B. L. Quorum sensing in bacteria. Annu. Rev. Microbiol. 55, 165–199 (2001).

    CAS  PubMed  Google Scholar 

  94. Xavier, J., Kim, W. & Foster, K. A molecular mechanism that stabilizes cooperative secretions in Pseudomonas aeruginosa. Mol. Microbiol. 79, 166–179 (2011). An empirical study that shows a link between transcriptional regulation and the stabilization of cooperative phenotypes against the evolution of selfish phenotypes.

    CAS  PubMed  Google Scholar 

  95. Ketting, R. F., Haverkamp, T. H. A., van Luenen, H. & Plasterk, R. H. A. Mut-7 of C. elegans, required for transposon silencing and RNA interference, is a homolog of Werner syndrome helicase and RNaseD. Cell 99, 133–141 (1999).

    CAS  PubMed  Google Scholar 

  96. Korb, J., Weil, T., Hoffmann, K., Foster, K. R. & Rehli, M. A gene necessary for reproductive suppression in termites. Science 324, 758 (2009).

    CAS  PubMed  Google Scholar 

  97. Hunt, B. G. et al. Sociality is linked to rates of protein evolution in a highly social insect. Mol. Biol. Evol. 27, 497–500 (2010).

    CAS  PubMed  Google Scholar 

  98. Buttery, N. J., Rozen, D. E., Wolf, J. B. & Thompson, C. R. L. Quantification of social behavior in D. discoideum reveals complex fixed and facultative strategies. Curr. Biol. 19, 1373–1377 (2009).

    CAS  PubMed  Google Scholar 

  99. Santorelli, L. A. et al. Facultative cheater mutants reveal the genetic complexity of cooperation in social amoebae. Nature 451, 1107–1110 (2008).

    CAS  PubMed  Google Scholar 

  100. Fiegna, F., Yu, Y. T., Kadam, S. V. & Velicer, G. J. Evolution of an obligate social cheater to a superior cooperator. Nature 441, 310–314 (2006).

    CAS  PubMed  Google Scholar 

  101. Khare, A. et al. Cheater-resistance is not futile. Nature 461, 980–982 (2009).

    CAS  PubMed  Google Scholar 

  102. Foster, K. R. Sociobiology: the Phoenix effect. Nature 441, 291–292 (2006).

    CAS  PubMed  Google Scholar 

  103. Borghans, J., Beltman, J. & Boer, R. MHC polymorphism under host-pathogen coevolution. Immunogenetics 55, 732–739 (2004).

    CAS  PubMed  Google Scholar 

  104. Petersen, L., Bollback, J. P., Dimmic, M., Hubisz, M. & Nielsen, R. Genes under positive selection in Escherichia coli. Genome Res. 17, 1336–1343 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Dorus, S., Wasbrough, E. R., Busby, J., Wilkins, E. C. & Karr, T. L. Sperm proteomics reveals intensified selection on mouse sperm membrane and acrosome genes. Mol. Biol. Evol. 27, 1235–1246 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Tuemmler, B. & Cornelis, P. Pyoverdine receptor: a case of positive Darwinian selection in Pseudomonas aeruginosa. J. Bacteriol. 187, 3289–3292 (2005).

    CAS  Google Scholar 

  107. Drummond, D. A. & Wilke, C. O. Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134, 341–352 (2008). A theoretical and bioinformatics paper that shows the importance of gene expression level for understanding evolutionary rates.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Julenius, K. & Pedersen, A. G. Protein evolution is faster outside the cell. Mol. Biol. Evol. 23, 2039–2048 (2006).

    CAS  PubMed  Google Scholar 

  109. Linksvayer, T. A. & Wade, M. J. Genes with social effects are expected to harbor more sequence variation within and between species. Evolution 63, 1685–1696 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Bensasson, D., Zarowiecki, M., Burt, A. & Koufopanou, V. Rapid evolution of yeast centromeres in the absence of drive. Genetics 178, 2161–2167 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Shi, J. et al. Widespread gene conversion in centromere cores. PLoS Biol. 8, e1000327 (2010).

    PubMed  PubMed Central  Google Scholar 

  112. Tirosh, I., Barkai, N. & Verstrepen, K. Promoter architecture and the evolvability of gene expression. J. Biol. 8, 95 (2009).

    PubMed  PubMed Central  Google Scholar 

  113. Carroll, S. Evo-devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution. Cell 134, 25–36 (2008).

    CAS  PubMed  Google Scholar 

  114. West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford Univ. Press, USA, 2003).

    Google Scholar 

  115. Couzin, I. D., Krause, J., James, R., Ruxton, G. D. & Franks, N. R. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218, 1–11 (2002).

    PubMed  Google Scholar 

  116. Sumpter, D. J. T. Collective Animal Behavior (Princeton Univ. Press, 2010).

    Google Scholar 

  117. Ben-Zvi, D. & Barkai, N. Scaling of morphogen gradients by an expansion-repression integral feedback control. Proc. Natl Acad. Sci. USA 107, 6924–6929 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Suel, G. M., Garcia-Ojalvo, J., Liberman, L. M. & Elowitz, M. B. An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440, 545–550 (2006).

    PubMed  Google Scholar 

  119. Xavier, J. B., Martinez-Garcia, E. & Foster, K. R. Social evolution of spatial patterns in bacterial biofilms: when conflict drives disorder. Am. Nat. 174, 1–12 (2009).

    PubMed  Google Scholar 

  120. DeDeo, S., Krakauer, D. C. & Flack, J. C. Inductive game theory and the dynamics of animal conflict. PLoS Comput. Biol. 6, e1000782 (2010).

    PubMed  PubMed Central  Google Scholar 

  121. Frank, S. A. The Foundations of Social Evolution (Princeton Univ. Press, Princeton, New Jersey, 1998).

    Google Scholar 

  122. Wenseleers, T., Gardner, A. & Foster, K. R. in Social Behaviour: Genes, Ecology and Evolution (eds Szekely, T., Komdeur, J. & Moore, A. J.) 132–158 (Cambridge Univ. Press, 2010).

    Google Scholar 

  123. Rousset, F. Genetic Structure and Selection in Subdivided Populations (Princeton Univ. Press, 2004). This theory book shows the links among population genetics theory, multilevel selection theory, game theory, neighbour-modulated fitness theory and inclusive fitness theory.

    Google Scholar 

  124. Doebeli, M. & Hauert, C. Models of cooperation based on the prisoner's dilemma and the snowdrift game. Ecol. Lett. 8, 748–766 (2005).

    Google Scholar 

  125. Krebs, J. R. & Davies, N. B. An Introduction to Behavioural Ecology (Blackwell, Oxford, 1993).

    Google Scholar 

  126. Krieger, M. J. & Ross, K. G. Identification of a major gene regulating complex social behavior. Science 295, 328–332 (2002).

    CAS  PubMed  Google Scholar 

  127. Wilson, D. S. Social semantics: toward a genuine pluralism in the study of social behaviour. J. Evol. Biol. 21, 368–373 (2008).

    CAS  PubMed  Google Scholar 

  128. Foster, K. R. Diminishing returns in social evolution: the not-so-tragic commons. J. Evol. Biol. 17, 1058–1072 (2004).

    CAS  PubMed  Google Scholar 

  129. Gardner, A. & Foster, K. R. in Ecology of Social Evolution (eds Korb, J. & Heinze, J.) 1–35 (Springer, Berlin, Heidelberg, 2008).

    Google Scholar 

  130. Beekman, M., Kondeur, J. & Ratnieks, F. L. W. Reproductive conflicts in social animals: who has power. Trends Ecol. Evol. 18, 277–282 (2003).

    Google Scholar 

  131. Smith, J. The social evolution of bacterial pathogenesis. Proc. Biol. Sci. 268, 61–69 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Boomsma, J. J. Lifetime monogamy and the evolution of eusociality. Phil. Trans. R. Soc. B 364, 3191–3207 (2009).

    PubMed  PubMed Central  Google Scholar 

  133. Alexander, R. D. The evolution of social behavior. Annu. Rev. Ecol. Syst. 5, 325–383 (1974).

    Google Scholar 

  134. Linksvayer, T. A. & Wade, M. J. The evolutionary origin and elaboration of sociality in the aculeate Hymenoptera: maternal effects, sib-social effects, and heterochrony. Q. Rev. Biol. 80, 317–336 (2005).

    PubMed  Google Scholar 

  135. Frost, L. S., Leplae, R., Summers, A. O. & Toussaint, A. Mobile genetic elements: the agents of open source evolution. Nature Rev. Microbiol. 3, 722–732 (2005).

    CAS  Google Scholar 

  136. Nowak, M. A., Tarnita, C. E. & Wilson, E. O. The evolution of eusociality. Nature 466, 1057–1062 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Wilson, D. S. & Wilson, E. O. Rethinking the theoretical foundation of sociobiology. Q. Rev. Biol. 82, 327–348 (2007).

    PubMed  Google Scholar 

  138. Wilson, E. O. & Holldobler, B. Eusociality: origin and consequences. Proc. Natl Acad. Sci. USA 102, 13367–13371 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Krakauer, D. C. & Flack, J. C. Better living through physics. Nature 467, 661–661 (2010).

    CAS  PubMed  Google Scholar 

  140. Dixon, T. The Invention of Altruism: Making Moral Meanings in Victorian Britain (Oxford Univ. Press for the British Academy, Oxford, 2008).

    Google Scholar 

  141. Ishii, S., Nishi, Y. & Egami, F. The fine structure of a pyocin. J. Mol. Biol. 13, 428–431 (1965)

    CAS  PubMed  Google Scholar 

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Acknowledgements

Many thanks to W. Kim, S. Knowles, S. Mitri, G. Robinson, J. Schluter, O. Soyer, B. Stern, C. Thompson and K. Verstrepen for comments. I would also like to thank two referees for their comments on an earlier version of the paper, which were important in shaping the final presentation. K.R.F. is supported by European Research Council Grant 242670.

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  1. kevin.foster@zoo.ox.ac.uk

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  1. Department of Zoology, Oxford University, South Parks Road, Oxford, OX1 3PS, UK

    Kevin R. Foster

  2. Oxford Centre for Integrative Systems Biology, Oxford University, South Parks Road, Oxford, OX1 3QU, UK

    Kevin R. Foster

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Glossary

Modern synthesis

The evolutionary synthesis in the early twentieth century that brought together Mendelian genetics with natural selection theory, giving birth to population genetics theory.

Sociobiology

The study of social phenotypes, including both competitive and cooperative phenotypes.

Sociogenomics

The study of the genomics and genetics of social behaviour. Key premises are that social behaviours have some genetic basis and that genes are functionally conserved across species.

Inclusive fitness theory

Theory in which natural selection is analysed in terms of the effects of an actor on its own reproduction as well as its effects on other individuals; the latter effects are weighted by the genetic similarity (relatedness) between the actor and others.

Multilevel selection theory

Theory in which natural selection is analysed in terms of the effects of an actor on its own reproduction as well as on members of its social group; the latter effects are weighted by the genetic similarity (relatedness) between the actor and the group.

Evolutionary game theory

A theory designed to recognize frequency-dependent fitness effects in evolution, which occur because the strategies of one individual affect the fitness of another individual. Game theory logic is used in both inclusive fitness and multilevel selection.

Functional scale

The number of genes, cells or organisms that operate towards a common evolutionary goal (the scale of agency). For example, the genes of a transposable element typically operate at a much lower functional scale than the genetic networks underlying cellular growth and division.

Agent

A gene, cell, organism or any group of these that has the same evolutionary interests. Natural selection on agents leads to them to behave as though they are striving to maximize their genetic representation in later generations.

Scale-free network

A network in which the distribution of the number of connections from each node follows a power law. This means that some nodes are highly connected (hubs), and paths through the network are shorter than those in highly ordered networks.

Cheater mutant

A mutant that does not invest in a public good but benefits from the investment of others, such as a bacterial mutant that does not produce a secreted enzyme but uses the enzyme of wild-type cells.

Eusocial insects

Social insects that display a division between work and reproduction among individuals. The clearest examples have morphologically distinct workers, as seen in many ants, bees, wasps and termites.

Neighbour-modulated fitness theory

Closely aligned to inclusive fitness theory, this framework analyses natural selection in terms of the effects on the actor of its social trait, combined with the effects of the social trait in other individuals on the actor, where the latter effects are weighted by the genetic similarity (relatedness) between the actor and others.

Social phenotype

A phenotype in one individual that affects the fitness of other individuals. 'Individual' here could mean a gene, cell, organism or even a group — whatever is appropriate for the analysis of the phenotype.

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Foster, K. The sociobiology of molecular systems. Nat Rev Genet 12, 193–203 (2011). https://doi.org/10.1038/nrg2903

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