The Evolutionary Resilience of Distributed Cellular Computing

  • Matteo CavaliereEmail author
  • Alvaro Sanchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10105)


Individual cells process environmental information relevant to their functions using biochemical processes and signalling networks that implement a flow of information from the extracellular environment, across the cell membrane to the cytoplasm in which the actual cellular computation takes place (in the form of gene expression). In many cases, the environmental information to be processed are either molecules produced by other cells or shared extracellular molecules - in this case the processing of the environmental information is a distributed, highly parallel computing process, in which cells must synchronize, coordinate and cooperate. While the ability of cells to cooperate can increase their overall computational power, it also raises an evolutionary stability issue - population of cooperating cells are at risk of cheating cells invasions, cells that do not cooperate but exploit the benefits of the population. The bridge between membrane computing (as a mathematical formalization of cellular computing) and evolutionary dynamics (as mathematical formalization of natural selection) could lead to interesting insights on the evolutionary stability of cellular computing.


Public Good Cellular Population Ecological Resilience Register Machine Membrane Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



M.C. acknowledges the support from the Engineering and Physical Sciences Research Council (EPSRC) grant EP/J02175X/1. Work in the Sanchez laboratory is supported by a Young Investigator grant from the Human Frontiers Science Project and a Scialog seed grant from Simons Foundation.


  1. 1.
    Cavaliere, M., Mardare, R., Sedwards, S.: A multiset-based model of synchronizing agents: computability and robustness. Theor. Comput. Sci. 391(3), 216–238 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Cavaliere, M., Poyatos, J.F.: Plasticity facilitates sustainable growth in the commons. J. Roy. Soc. Inter. 10(81), 20121006 (2013)CrossRefGoogle Scholar
  3. 3.
    Feinerman, O., Korman, A.: Theoretical distributed computing meets biology: a review. In: Hota, C., Srimani, P.K. (eds.) ICDCIT 2013. LNCS, vol. 7753, pp. 1–18. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36071-8_1 CrossRefGoogle Scholar
  4. 4.
    Harrington, K.I., Sanchez, A.: Eco-evolutionary dynamics of complex social strategies in microbial communities. Commun. Integr. Biol. 7(1), e28230 (2014)CrossRefGoogle Scholar
  5. 5.
    Harrison, F., Paul, J., Massey, R.C., Buckling, A.: Interspecific competition and siderophore-mediated cooperation in pseudomonas aeruginosa. ISME J. 2(1), 49–55 (2008)CrossRefGoogle Scholar
  6. 6.
    Hauert, C., Holmes, M., Doebeli, M.: Evolutionary games and population dynamics: maintenance of cooperation in public goods games. Proc. Roy. Soc. Lon. B: Biol. Sci. 273(1600), 2565–2571 (2006)CrossRefGoogle Scholar
  7. 7.
    Hauert, C., Wakano, J.Y., Doebeli, M.: Ecological public goods games: cooperation and bifurcation. Theor. Popul. Biol. 73(2), 257–263 (2008)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation, 3rd edn. Addison-Wesley Longman Publishing Co. Inc., Boston (2006)zbMATHGoogle Scholar
  9. 9.
    Ilachinski, A.: Cellular Automata: A Discrete Universe. World Scientific, River Edge (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kari, L., Rozenberg, G.: The many facets of natural computing. Commun. ACM 51(10), 72 (2008)CrossRefGoogle Scholar
  11. 11.
    Levin, S.A.: Public goods in relation to competition, cooperation, and spite. Proc. Natl. Acad. Sci. 111(Supplement_3), 10838–10845 (2014)CrossRefGoogle Scholar
  12. 12.
    Macía, J., Posas, F., Solé, R.V.: Distributed computation: the new wave of synthetic biology devices. Trends Biotechnol. 30(6), 342–349 (2012)CrossRefGoogle Scholar
  13. 13.
    Mehta, P., Schwab, D.J.: Energetic costs of cellular computation. Proc. Natl. Acad. Sci. 109(44), 17978–17982 (2012)CrossRefGoogle Scholar
  14. 14.
    Navlakha, S., Bar-Joseph, Z.: Distributed information processing in biological and computational systems. Commun. ACM 58(1), 94–102 (2015)CrossRefGoogle Scholar
  15. 15.
    Paun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press Inc., Oxford (2010)CrossRefzbMATHGoogle Scholar
  16. 16.
    Perkins, T.J., Swain, P.S.: Strategies for cellular decision-making. Mol. Syst. Biol. 5, 326 (2009)CrossRefGoogle Scholar
  17. 17.
    Rauch, J., Kondev, J., Sanchez, A.: Cooperators trade off ecological resilience and evolutionary stability in public goods games. J. R. Soc. Interface (2017).
  18. 18.
    Ross-Gillespie, A., Gardner, A., Buckling, A., West, S.A., Griffin, A.S.: Density dependence and cooperation: theory and a test with bacteria. Evolution 63(9), 2315–2325 (2009)CrossRefGoogle Scholar
  19. 19.
    Ross-Gillespie, A., Gardner, A., West, S.A., Griffin, A.S.: Frequency dependence and cooperation: theory and a test with bacteria. Am. Nat. 170(3), 331–342 (2007)CrossRefGoogle Scholar
  20. 20.
    Simon, H.A.: Models of Man; Social and Rational. Wiley, New York (1957)zbMATHGoogle Scholar
  21. 21.
    Sober, E.: The Nature of Selection: Evolutionary Theory in Philosophical Focus. University of Chicago Press, Chicago (1993)Google Scholar
  22. 22.
    Soloveichik, D., Cook, M., Winfree, E., Bruck, J.: Computation with finite stochastic chemical reaction networks. Nat. Comput. 7(4), 615–633 (2008)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of EdinburghEdinburghUK
  2. 2.Yale UniversityNew HavenUSA

Personalised recommendations