Engineering and Biology: Counsel for a Continued Relationship

Abstract

Biologists frequently draw on ideas and terminology from engineering. Evolutionary systems biology—with its circuits, switches, and signal processing—is no exception. In parallel with the frequent links drawn between biology and engineering, there is ongoing criticism against this cross-fertilization, using the argument that over-simplistic metaphors from engineering are likely to mislead us as engineering is fundamentally different from biology. In this article, we clarify and reconfigure the link between biology and engineering, presenting it in a more favorable light. We do so by, first, arguing that critics operate with a narrow and incorrect notion of how engineering actually works, and of what the reliance on ideas from engineering entails. Second, we diagnose and diffuse one significant source of concern about appeals to engineering, namely that they are inherently and problematically metaphorical. We suggest that there is plenty of fertile ground left for a continued, healthy relationship between engineering and biology.

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Notes

  1. 1.

    One may distinguish a representation that portrays a biological system as a network from the connections and interactions among the biological entities themselves. It is not obvious that biological systems are networks, or even what that would exactly mean. But we must bracket this interesting issue here.

  2. 2.

    This does not imply that we advocate a strong dissociation between claims about proximate and ultimate causation. There is a lively debate on that issue (Laland et al. 2013; O’Malley and Soyer 2012; Steinacher and Soyer 2012; Calcott 2013). We are merely making the modest claim that hypotheses about current behavior do not as such presuppose assumptions about evolutionary origins.

  3. 3.

    See http://en.wikipedia.org/wiki/Release_early,_release_often for a summary of these ideas.

  4. 4.

    It is worth pointing out that a charge of poor design is only possible if we have some standard of what good design is—so there is an implicit use of engineering in these arguments.

  5. 5.

    William Wimsatt’s work on generative entrenchment has, for many years, emphasized similarities between evolutionary and technological change (Wimsatt 2007).

  6. 6.

    “We…suggest that biological research and teaching could and should actually be done without much use of metaphorical thinking…” (Pigliucci and Boudry 2011, p. 455).

  7. 7.

    There are other forms of surrogative reasoning that share some properties with metaphor and some with models. A significant example is analogy. We leave it to the reader to extrapolate from what we say here to other cases of surrogation, since our aim is not to cover this topic in exhaustive detail.

  8. 8.

    Note that precise specification makes the content of the model uncontroversial. Whether and how the model matches the biological system may remain controversial.

  9. 9.

    As one reviewer suggested, a metaphor and a model might differ in other respects too, such as the status of vehicle of representation: a metaphor might be linguistic, while a model is often mathematical. We won’t delve into thorny issues concerning linguistic versus other forms of representation here. It suffices for our purposes that precision is one significant difference between models and metaphors.

  10. 10.

    As Steven Vogel would have it: “… biomechanics has mainly been the study of how nature does what engineers have shown to be possible. Nature may have gotten there first, but human engineers, not biologists, have provided us with both analytical tools and practical examples” (Vogel 2003, p. 11).

References

  1. Alon U (2003) Biological networks: the tinkerer as an engineer. Science 301:1866–1867

    Article  Google Scholar 

  2. Alon U (2006) An introduction to systems biology. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  3. Balch M (2003) Complete digital design. McGraw Hill Professional, New York

    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 44:660–668

    Article  Google Scholar 

  5. Bray D (1995) Protein molecules as computational elements in living cells. Nature 376:307–312

    Article  Google Scholar 

  6. Calcott B (2013) Why how and why aren’t enough: more problems with Mayr’s proximate-ultimate distinction. Biol Philos 28:767–780. doi:10.1007/s10539-013-9367-1

    Article  Google Scholar 

  7. Calcott B (2014) Engineering and evolvability. Biol Philos 29:293–313

    Article  Google Scholar 

  8. Cordero OX, Hogeweg P (2006) Feed-forward loop circuits as a side effect of genome evolution. Mol Biol Evol 23:1931–1936. doi:10.1093/molbev/msl060

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Ellis K (2008) Business analysis benchmark: the impact of business requirements on the success of technology projects. IAG Consulting. http://www.iag.biz/resources/library/business-analysis-benchmark.html. Accessed 9 Dec 2014

  12. Foote B, Yoder J (2000) Big ball of mud. Online version at http://www.laputan.org/mud/. Accessed 9 Dec 2014

  13. Godfrey-Smith P (2006) The strategy of model-based science. Biol Philos 21:725–740

    Article  Google Scholar 

  14. Green S, Fagan M, Jaeger J (2014a) Explanatory integration challenges in evolutionary systems biology. Biol Theory. doi:10.1007/s13752-014-0185-8

    Google Scholar 

  15. Green S, Levy A, Bechtel W (2014b) Design without adaptationism. Eur J Philos Sci. doi:10.1007/s13194-014-0096-3

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Kauffman SA (1969) Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 22:437–467

    Article  Google Scholar 

  20. Laland KN, Odling-Smee J, Hoppitt W, Uller T (2013) More on how and why: cause and effect in biology revisited. Biol Philos 28:719–745. doi:10.1007/s10539-012-9335-1

    Article  Google Scholar 

  21. Levy A (2014a) Modeling without models. Philos Stud. doi:10.1007/s11098-014-0333-9

    Google Scholar 

  22. Levy A (2014b) Machine-likeness and explanation by decomposition. Philos Impr 14(6):1–15

    Google Scholar 

  23. Lewens T (2005) Organisms and artifacts. MIT Press, Cambridge

    Google Scholar 

  24. Lewontin RC (1996) Evolution as engineering: integrative approaches to molecular biology. MIT Press, Cambridge

    Google Scholar 

  25. Leys SP, Yahel G, Reidenbach MA et al (2011) The sponge pump: the role of current induced flow in the design of the sponge body plan. PLoS ONE 6:e27787

    Article  Google Scholar 

  26. Lynch M (2007) The evolution of genetic networks by non-adaptive processes. Nat Rev Genet 8:803–813

    Article  Google Scholar 

  27. Macilwain C (2010) Scientists versus engineers: this time it’s financial. Nature 467:885

    Article  Google Scholar 

  28. McConnell S (2004) Code complete: a practical handbook of software construction, 2nd edn. Microsoft Press, Redmond

    Google Scholar 

  29. Nelson MR, King JR, Jensen OE (2013) Buckling of a growing tissue and the emergence of two-dimensional patterns. Math Biosci. doi:10.1016/j.mbs.2013.09.008

    Google Scholar 

  30. Nicholson DJ (2012) The concept of mechanism in biology. Stud Hist Philos Sci Part C 43:152–163

    Article  Google Scholar 

  31. O’Malley MA, Soyer OS (2012) The roles of integration in molecular systems biology. Stud Hist Philos Biol Biomed Sci 43:58–68. doi:10.1016/j.shpsc.2011.10.006

    Article  Google Scholar 

  32. Paley W (1817) Natural theology; or evidences of the existence and attributes of the Deity. Mason, London

    Google Scholar 

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

    Article  Google Scholar 

  34. Peter IS, Davidson EH (2011) Evolution of gene regulatory networks controlling body plan development. Cell 144:970–985

    Article  Google Scholar 

  35. Pigliucci M, Boudry M (2011) Why machine-information metaphors are bad for science and science education. Sci Educ 20:453–471

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Royce WW (1970) Managing the development of large software systems (vol. 26). Presented at the proceedings of IEEE WESCON, Los Angeles

  38. Rubinstein M, Colby RH (2003) Polymer physics. Oxford University Press, Oxford

    Google Scholar 

  39. Steinacher A, Soyer OS (2012) Evolutionary principles underlying structure and response dynamics of cellular networks. Adv Exp Med Biol 751:225–247. doi:10.1007/978-1-4614-3567-9

    Article  Google Scholar 

  40. Thieffry D, Romero D (1999) The modularity of biological regulatory networks. Biosystems 50:49–59

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  43. Wagner GP, Pavlicev M, Cheverud JM (2007) The road to modularity. Nat Rev Genet 8:921–931

    Article  Google Scholar 

  44. Weisberg M (2007) Who is a modeler? Br J Philos Sci 58:207–233

    Article  Google Scholar 

  45. Weisberg M (2013) Simulation and similarity. Oxford University Press, New York

    Google Scholar 

  46. Wimsatt WC (2007) Re-engineering philosophy for limited beings: piecewise approximations to reality. Harvard University Press, Cambridge

    Google Scholar 

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Correspondence to Brett Calcott.

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Calcott, B., Levy, A., Siegal, M.L. et al. Engineering and Biology: Counsel for a Continued Relationship. Biol Theory 10, 50–59 (2015). https://doi.org/10.1007/s13752-014-0198-3

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Keywords

  • Adaptationism
  • Design
  • Engineering
  • Evolvability
  • Gene regulation
  • Metaphor
  • Evolutionary systems biology