Skip to main content

Abstraction and Representation in Living Organisms: When Does a Biological System Compute?

  • Chapter
  • First Online:
Representation and Reality in Humans, Other Living Organisms and Intelligent Machines

Part of the book series: Studies in Applied Philosophy, Epistemology and Rational Ethics ((SAPERE,volume 28))

Abstract

Even the simplest known living organisms are complex chemical processing systems. But how sophisticated is the behaviour that arises from this? We present a framework in which even bacteria can be identified as capable of representing information in arbitrary signal molecules, to facilitate altering their behaviour to optimise their food supplies, for example. Known as Abstraction/Representation theory (AR theory), this framework makes precise the relationship between physical systems and abstract concepts. Originally developed to answer the question of when a physical system is computing, AR theory naturally extends to the realm of biological systems to bring clarity to questions of computation at the cellular level.

DH published previously as Clare Horsman.

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

Access this chapter

Institutional subscriptions

References

  1. Adamatzky, A.: Physarum Machines: Computers from Slime Mould. World Scientific (2010)

    Google Scholar 

  2. Amos, M.: Theoretical and Experimental DNA Computation. Springer (2005)

    Google Scholar 

  3. Brent, R., Bruck, J.: 2020 computing: can computers help to explain biology? Nature 440, 416–417 (2006)

    Article  Google Scholar 

  4. Bain, J.D., Switzer, C., Chamberlin, A.R., Benner, S.A.: Ribosome-mediated incorporation of a non-standard amino acid into a peptide through expansion of the genetic code. Nature 356(6369), 537–539 (1992)

    Article  Google Scholar 

  5. Cousot, P., Cousot, R.: Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In: Proceedings of the 4th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages, pp. 238–252. ACM (1977)

    Google Scholar 

  6. Chirico, N., Vianelli, A., Belshaw, R.: Why genes overlap in viruses. Proc. R. Soc. B: Biol. Sci. 277(1701), 17–3809 (2010)

    Article  Google Scholar 

  7. Cho, M., Vaswani, H.M., Stenger, J., Brixner, T., Fleming, G.R.: Exciton analysis in 2D electronic spectroscopy. J. Phys. Chem. B 109(21), 10542–10556 (2005)

    Article  Google Scholar 

  8. De Marse, T.B., Dockendorf, K.P.: Adaptive flight control with living neuronal networks on microelectrode arrays. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks. IJCNN’05, vol. 3, pp. 1548–1551. IEEE (2005)

    Google Scholar 

  9. Engel, G.S., Calhoun, T.R., Read, E.L., Ahn, T.-K., Cheng, Y.-C., Mancal, T., Blankenship, R.E., Fleming, G.R.: Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems. Nature 446, 782–786 (2007)

    Article  Google Scholar 

  10. He, J., Hoare, C.A.R., Sanders, J.W.: Data refinement refined resume. In: ESOP 86, pp. 187–196. Springer (1986)

    Chapter  Google Scholar 

  11. Horsman, D.C.: Abstraction/representation theory for heterotic physical computing. Philos. Trans. R. Soc. A 373, 20140224 (2015)

    Article  Google Scholar 

  12. Horsman, C., Stepney, S., Kendon, V.: When does an unconventional substrate compute? In: UCNC 2014 Poster Proceedings, University of Western Ontario Technical report #758 (2014)

    Google Scholar 

  13. Horsman, C., Stepney, S., Wagner, R.C., Kendon, V.: When does a physical system compute? Proce. R. Soc. A 470(2169), 20140182 (2014)

    Article  Google Scholar 

  14. Kendon, V., Sebald, A., Stepney, S.: Heterotic computing: past, present and future. Philos. Trans. R. Soci. A 373, 20140225 (2015)

    Article  Google Scholar 

  15. Malyshev, D.A., Dhami, K., Lavergne, T., Chen, T., Dai, N., Foster, J.M., Correa, I.R., Romesberg, F.E.: A semi-synthetic organism with an expanded genetic alphabet. Nature 509(7500), 385–388 (2014)

    Article  Google Scholar 

  16. Mohseni, M., Rebentrost, P., Lloyd, S., Aspuru-Guzik, A.: Environment-assisted quantum walks in photosynthetic energy transfer. J. Chem. Phys. 129, 174106 (2008)

    Article  Google Scholar 

  17. National Institute of General Medical Sciences. The New Genetics. NIH Publication No.10-662 (2010). http://www.nigms.nih.gov

  18. Navlakha, S., Bar-Joseph, Z.: Distributed information processing in biological and computational systems. Commun. ACM 58(1), 94–102 (2015)

    Article  Google Scholar 

  19. Osawa, S., Jukes, T.H., Watanabe, K., Muto, A.: Recent evidence for evolution of the genetic code. Microbiol. Rev. 56(1), 229–264 (1992)

    Google Scholar 

  20. Plenio, M.B., Huelga, S.F.: Dephasing assisted transport: quantum networks and biomolecules. New J. Phys. 10, 113019 (2008)

    Article  Google Scholar 

  21. Porter, S.L., Wadhams, G.H., Armitage, J.P.: Signal processing in complex chemotaxis pathways. Nat. Rev. Microbiol. 9(3), 153–165 (2011)

    Article  Google Scholar 

  22. Regev, A., Shapiro, E.: Cellular abstractions: cells as computation. Nature 419, 343 (2002)

    Article  Google Scholar 

  23. Wooley, J., Lin, H. (eds.): Catalyzing Inquiry at the Interface of Computing and Biology. National Academies Press (2005)

    Google Scholar 

  24. War, A.R., Paulraj, M.G., Ahmad, T., Buhroo, A.A., Hussain, B., Ignacimuthu, S., Sharma, H.C.: Mechanisms of plant defense against insect herbivores. Plant Signal. Behav. 7(10), 1306–1320 (2012)

    Article  Google Scholar 

  25. Wang, Q., Parrish, A.R., Wang, L.: Expanding the genetic code for biological studies. Chem. Biol. 16(3), 323–336 (2009)

    Article  Google Scholar 

  26. Wang, B., Kitney, R, Joly, N., Buck, M.: Engineering modular orthogonal genetic logic gates for robust digital-like synthetic biology. Nat. Commun. 2(508) (2011)

    Google Scholar 

  27. Yang, Z., Hutter, D., Sheng, P., Sismour, A.M., Benner, S.A.: Artificially expanded genetic information system: a new base pair with an alternative hydrogen bonding pattern. Nucleic Acids Res. 34(21), 6095–6101 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Susan Stepney .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Horsman, D., Kendon, V., Stepney, S., Young, J.P.W. (2017). Abstraction and Representation in Living Organisms: When Does a Biological System Compute?. In: Dodig-Crnkovic, G., Giovagnoli, R. (eds) Representation and Reality in Humans, Other Living Organisms and Intelligent Machines. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-43784-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43784-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43782-8

  • Online ISBN: 978-3-319-43784-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics