Engineering and evolvability

Abstract

Comparing engineering to evolution typically involves adaptationist thinking, where well-designed artifacts are likened to well-adapted organisms, and the process of evolution is likened to the process of design. A quite different comparison is made when biologists focus on evolvability instead of adaptationism. Here, the idea is that complex integrated systems, whether evolved or engineered, share universal principles that affect the way they change over time. This shift from adaptationism to evolvability is a significant move for, as I argue, we can make sense of these universal principles without making any adaptationism claims. Furthermore, evolvability highlights important aspects of engineering that are ignored in the adaptationist debates. I introduce some novel engineering examples that incorporate these key neglected aspects, and use these examples to challenge some commonly cited contrasts between engineering and evolution, and to highlight some novel resemblances that have gone unnoticed.

This is a preview of subscription content, log in to check access.

Notes

  1. 1.

    Work on evolutionary search algorithms is largely done in computer science, rather than software engineering. For some discussion on the difference between the two, see Connell (2009).

  2. 2.

    The notion of “mechanism” has become a hotly debated term recently (Dupré 2013; Woodward 2013). I use the term loosely here to mean a collection of parts that act in concert to reliably produce some effect.

  3. 3.

    Bill Wimsatt’s work is one exception to this claim. His work on generative entrenchment is meant to span both evolved and engineered systems, and clearly has a connection to how systems change over time.

  4. 4.

    Much (perhaps all) of what follows may apply to other engineering disciplines. I take it this would count as more evidence of the link, rather than an objection to the core ideas in the paper. I focus on software engineering as it is a particularly powerful example, and one that I have some background in.

  5. 5.

    For an interesting discussion of just how ubiquitous this is, see http://www.laputan.org/mud/.

  6. 6.

    Car manufacturing is a better example. I say more about this in the next section.

  7. 7.

    I sometimes talk of properties rather than principles. Here is the connection: Design principles describe the properties needed to make a system evolvable. But the principles might say more, such as the environmental conditions required for the properties to confer evolvability.

  8. 8.

    I suspect the change in terminology was made to avoid confusion over the different uses of the term “evolvability”.

  9. 9.

    For a discussion of the connection between development and the genotype-phenotype map, see Pigliucci (2010).

  10. 10.

    The fact that neutrality can be realised in many different ways is a positive feature of the theory, as it enables Wagner to apply these ideas across many different levels of organisation.

  11. 11.

    Programmers might note that this simple example could describe a “function”, rather than a module. True, but I am trying to keep things simple here. Just imagine it is a module with a single function.

References

  1. Altshuler DL, Dickson WB, Vance JT, Roberts SP, Dickinson MH (2005) Short-amplitude high-frequency wing strokes determine the aerodynamics of honeybee flight. Proc Natl Acad Sci 102:18213–18218

    Article  Google Scholar 

  2. Autumn K, Gravish N (2008) Gecko adhesion: evolutionary nanotechnology. Philos Trans R Soc A Math Phys Eng Sci 366:1575–1590

    Article  Google Scholar 

  3. Autumn K, Peattie A (2002) Mechanisms of adhesion in geckos. Integr Comp Biol 42:1081–1090

    Article  Google Scholar 

  4. Boudry M, Pigliucci M (2013) The mismeasure of machine: Synthetic biology and the trouble with engineering metaphors. Stud Hist Philos Sci Part C Stud Hist Philos Biol Biomed Sci 44:660–668

  5. Breivold HP, Crnkovic I, Eriksson PJ (2008) Analyzing software evolvability. Presented at the computer software and applications, 2008. COMPSAC’08. 32nd Annual IEEE International, pp 327–330

  6. Brown RL (2013) What evolvability really is. Br J Philos Sci doi:10.1093/bjps/axt014

  7. Calcott B (2009) Lineage explanations: explaining how biological mechanisms change. Br J Philos Sci 60:51–78

    Article  Google Scholar 

  8. Calcott B (2013a) Why how and why aren’t enough: more problems with Mayr’s proximate-ultimate distinction. Biol Philos 28:767–780

    Google Scholar 

  9. Calcott B (2013b) Engineering: biologists borrow more than words. Nature 502:170

    Article  Google Scholar 

  10. Ciliberti S, Martin OC, Wagner A (2007) Innovation and robustness in complex regulatory gene networks. Proc Natl Acad Sci 104:13591–13596

    Article  Google Scholar 

  11. Connell C (2009) Software engineering !=computer science. Dr Dobb’s: the world of software development. Retrieved 4 Mar 2011, from http://www.ddj.com/architecture-and-design/217701907

  12. Cook S, Ji H, Harrison R (2000) Software evolution and software evolvability. University of Reading, UK

    Google Scholar 

  13. Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664–1669

    Article  Google Scholar 

  14. Dennett DC (1995) Darwin’s dangerous idea. Simon & Schuster, New York City

    Google Scholar 

  15. Dupré J (2013) I-living causes. Aristot Soc Suppl 87:19–37

    Article  Google Scholar 

  16. Eldar A, Elowitz MB (2010) Functional roles for noise in genetic circuits. Nature 467:167–173

    Article  Google Scholar 

  17. French MJ (1994) Invention and evolution. Cambridge University Press, Cambridge

    Google Scholar 

  18. Gamma E, Helm R, Johnson R, Vlissides J (1994) Design patterns. Pearson Education, Pearson

    Google Scholar 

  19. Geim AK, Dubonos SV, Grigorieva IV, Novoselov KS, Zhukov AA, Shapoval SY (2003) Microfabricated adhesive mimicking gecko foot-hair. Nat Mater 2:461–463

    Article  Google Scholar 

  20. Gerhart J, Kirschner M (2007) The theory of facilitated variation. Proc Natl Acad Sci 104(Suppl 1):8582–8589

    Article  Google Scholar 

  21. Gilbert SF (2000) Developmental biology, 7th edition, 9th edn. Sinauer Associates, Sunderland

    Google Scholar 

  22. Greiner C (2010) Gecko-inspired Nanomaterials. In: Kuma CSSR (ed) Biomimetic and bioinspired nanomaterials. Wiley-VCH, New York

    Google Scholar 

  23. Griffiths PE (1996) The historical turn in the study of adaptation. Br J Philos Sci 47:511–532

    Article  Google Scholar 

  24. Hendrikse JL, Parsons TE, Hallgrímsson B (2007) Evolvability as the proper focus of evolutionary developmental biology. Evol Dev 9:393–401

    Article  Google Scholar 

  25. Jacob F (1977) Evolution and tinkering. Science 196:1161–1166

    Article  Google Scholar 

  26. Kirschner M, Gerhart J (1998) Evolvability. Proc Natl Acad Sci 95:8420–8427

    Article  Google Scholar 

  27. Kirschner MW, Gerhart JC (2006) The plausibility of life: resolving Darwin’s dilemma. Yale University Press, New Haven

    Google Scholar 

  28. Kitano H (2004) Biological robustness. Nat Rev Genet 5:826–837

    Article  Google Scholar 

  29. Lewens T (2002) Adaptationism and engineering. Biol Philos 17:1–31

    Article  Google Scholar 

  30. Lewens T (2004) Organisms and artifacts. Bradford Book

  31. Lewontin RC (1996) Evolution as engineering. In: Collado-Vides J, Magasanik B, Smith T (eds) Integrative approaches to molecular biology. MIT Press, Cambridge, MA

    Google Scholar 

  32. Lüer C, Rosenblum DS, van der Hoek A (2001) The evolution of software evolvability. Presented at the proceedings of the 4th international workshop on principles of software evolution, pp 134–137

  33. Mayr E (1961) Cause and effect in biology. Science 134:1501–1506

    Article  Google Scholar 

  34. Monteiro A (2012) Gene regulatory networks reused to build novel traits: co-option of an eye-related gene regulatory network in eye-like organs and red wing patches on insect wings is suggested by optix expression. BioEssays 34:181–186

    Article  Google Scholar 

  35. Munteanu A, Solé RV (2008) Neutrality and robustness in evo-devo: emergence of lateral inhibition. PLoS Comput Biol 4:e1000226

    Article  Google Scholar 

  36. Newman SA, Bhat R (2009) Dynamical patterning modules: a “pattern language” for development and evolution of multicellular form. Int J Dev Biol 53:693–705

    Article  Google Scholar 

  37. Parnas DL (1972) On the criteria to be used in decomposing systems into modules. Commun ACM 12:1053–1058

    Google Scholar 

  38. Parter M, Kashtan N, Alon U (2008) Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comput Biol 4:e1000206

    Article  Google Scholar 

  39. Pauwels E (2013) Communication: mind the metaphor. Nature 500:523–524

    Article  Google Scholar 

  40. Pigliucci M (2008) Is evolvability evolvable? Nat Rev Genet 9:75–82

    Article  Google Scholar 

  41. Pigliucci M (2010) Genotype-phenotype mapping and the end of the “genes as blueprint” metaphor. Philos Trans R Soc B Biol Sci 365:557–566

    Article  Google Scholar 

  42. Pigliucci M, Boudry M (2010) Why machine-information metaphors are bad for science and science education. Sci Educ 20:453–471

    Article  Google Scholar 

  43. Pugno NM (2007) Towards a spiderman suit: large invisible cables and self-cleaning releasable super adhesive materials. J Phys: Condens Matter 19:395001

    Google Scholar 

  44. Pugno NM (2008) Spiderman gloves. Nano Today 3:35–41

    Article  Google Scholar 

  45. Raman K, Wagner A (2011) Evolvability and robustness in a complex signalling circuit. Mol BioSyst 7:1081–1092

    Article  Google Scholar 

  46. Reeves JW (1992) What is software design. C++ J 2. Retrieved from http://user.it.uu.se/~carle/softcraft/notes/Reeve_SourceCodeIsTheDesign.pdf

  47. Shubin N (2008) Your inner fish. Pantheon Books, New York

    Google Scholar 

  48. Smith P (2011) Software build systems. Addison-Wesley Professional, Boston

    Google Scholar 

  49. Sterelny K (2004) Symbiosis, evolvability, and modularity. In: Schlosser G, Wagner GP (eds) Modularity in development and evolution. University of Chicago Press, Chicago

    Google Scholar 

  50. Swiegers GF (2012) Bioinspiration and biomimicry in chemistry. Wiley, New York

    Google Scholar 

  51. Tinbergen N (1963) On aims and methods of ethology. Zeitschrift für Tierpsychologie 20:410–433

    Article  Google Scholar 

  52. Vogel S (2003) Comparative biomechanics. Princeton University Press, Princeton

    Google Scholar 

  53. Wagner A (2007) Robustness and evolvability in living systems. Princeton University Press, Princeton

    Google Scholar 

  54. Wagner A (2011) The origins of evolutionary innovations: a theory of transformative change in living systems. Oxford University Press, Oxford

    Google Scholar 

  55. Wagner GP, Altenberg L (1996) Complex adaptations and the evolution of evolvability. Evolution 50:967–976

    Article  Google Scholar 

  56. Woodward J (2010) Causation in biology: stability, specificity, and the choice of levels of explanation. Biol Philos 25:287–318

    Article  Google Scholar 

  57. Woodward J (2013) II-Mechanistic explanation: its scope and limits. Aristot Soc Suppl 87:39–65

    Article  Google Scholar 

Download references

Acknowledgments

This paper has a long history. Early formulations benefited from feedback at the Philosophy of Biology at Dolphin Beach conference, at ISHPSSB, and at the “Progress by Design” conference in Bielefeld. A draft paper by Ian Wills, and a long café discussion with Dan Nicholson prompted me to think more deeply about the connection between engineering and evolved systems. Arnon Levy, Michael Weisberg, Maureen O’Malley, Emily Parke, Kim Sterelny and two anonymous reviewers provided useful comments (and words of encouragement) on later versions. This work was supported by a Australian Research Council Postdoctoral Fellowship and a Visiting Fellowship at the Konrad Lorenz Institute for Evolution and Cognition Research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Brett Calcott.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Calcott, B. Engineering and evolvability. Biol Philos 29, 293–313 (2014). https://doi.org/10.1007/s10539-014-9425-3

Download citation

Keywords

  • Evolvability
  • Adaptationism
  • Teleology
  • Engineering
  • Evolutionary systems biology
  • Evo-devo