Simulating Evolution’s First Steps

  • Tim J. Hutton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)


We demonstrate a simple artificial chemistry environment in which two small evolutionary transitions from the simplest self-replicators to larger ones are observed. The replicators adapt to increasingly harsh environments, where they must synthesise the components they need for replication. The evolution of a biosynthetic pathway of increasing length is thus achieved, through the use of simple chemical rules for catalytic action.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cairns-Smith, A.G.: Seven clues to the origin of life. Cambridge University Press, Cambridge (1985)Google Scholar
  2. 2.
    Channon, A.: Improving and still passing the ALife test: Component-normalised activity statistics classify evolution in Geb as unbounded. In: Standish, R., Bedau, M.A., Abbass, H.A. (eds.) Proc. Artificial Life VIII, pp. 173–181. MIT Press, Cambridge (2002)Google Scholar
  3. 3.
    Dittrich, P., Ziegler, J., Banzhaf, W.: Artificial chemistries - a review. Artificial Life 7(3), 225–275 (2001)CrossRefGoogle Scholar
  4. 4.
    Fontana, W., Buss, L.W.: What would be conserved if “the tape were played twice”? Proc. Nat. Acad. Sci. 91, 757–761 (1994)CrossRefGoogle Scholar
  5. 5.
    Horowitz, N.H.: On the evolution of biochemical synthesis. Proc. Nat. Acad. Sci. 31, 153–157 (1945)CrossRefGoogle Scholar
  6. 6.
    Hutton, T.J.: Evolvable self-replicating molecules in an artificial chemistry. Artificial Life 8(4), 341–356 (2002)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Joyce, G.F., Orgel, L.: Prospects for understanding the origin of the RNA world. In: Gesteland, R.F., Cech, T.R., Atkins, J.F. (eds.) The RNA World, pp. 49–77. Cold Spring Harbor Laboratory Press, New York (1999)Google Scholar
  8. 8.
    Lenski, R.E., Ofria, C., Pennock, R.T., Adami, C.: The evolutionary origin of complex features. Nature 423, 139–144 (2003)CrossRefGoogle Scholar
  9. 9.
    Margulis, L.: Symbiosis in Cell Evolution. Freeman, New York (1981)Google Scholar
  10. 10.
    Mayer, B., Rasmussen, S.: Dynamics and simulation of micellar selfreproduction. International Journal of Modern Physics C 11(4), 809–826 (2000)CrossRefGoogle Scholar
  11. 11.
    McMullin, B.: John von Neumann and the evolutionary growth of complexity: Looking backwards, looking forwards. Artificial Life 6(4), 347–361 (2000)CrossRefGoogle Scholar
  12. 12.
    Ono, N., Ikegami, T.: Artificial chemistry: Computational studies on the emergence of self-reproducing units. In: Kelemen, J., Sosík, P. (eds.) Proc. European Conference on Artificial Life, pp. 186–195. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Orgel, L.E.: Selection in vitro. Proceedings of the Royal Society B 205, 435–442 (1979)CrossRefGoogle Scholar
  14. 14.
    Paun, G.: Membrane Computing. An Introduction. Springer, Berlin (2002)zbMATHGoogle Scholar
  15. 15.
    Sayama, H.: A new structurally dissolvable self-reproducing loop evolving in a simple cellular automata space. Artificial Life 5(4), 343–365 (1999)CrossRefGoogle Scholar
  16. 16.
    Suzuki, Y., Tanaka, H.: Chemical evolution among artificial proto-cells. In: Bedau, M.A., McCaskill, J.S., Packard, N.H., Rasmussen, S. (eds.) Proc. Artificial Life VII, pp. 54–64. MIT Press, Cambridge (2000)Google Scholar
  17. 17.
    Szathmáry, E., Demeter, L.: Group selection of early replicators and the origin of life. Journal of Theoretical Biology 128, 463–486 (1987)CrossRefGoogle Scholar
  18. 18.
    Szostak, J.W., Bartel, D.P., Luisi, P.L.: Synthesizing life. Nature 409, 387–390 (2001)CrossRefGoogle Scholar
  19. 19.
    Taylor, T.: Creativity in evolution: Individuals, interactions and environment. In: Bentley, P., Corne, D. (eds.) Proceedings of the AISB 1999 Symposium on Creative Evolutionary Systems. The Society for the Study of Artificial Intelligence and Simulation of Behaviour, Morgan Kaufman, San Francisco (1999)Google Scholar
  20. 20.
    The IBM Blue Gene team. Blue gene: A vision for protein science using a petaflop supercomputer. IBM Systems Journal, 40(2) (2001) Google Scholar
  21. 21.
    Wilke, C.O., Adami, C.: The biology of digital organisms. Trends in Ecology and Evolution 17(11), 528–532 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Tim J. Hutton
    • 1
  1. 1.Biomedical Informatics Unit, Eastman Dental Institute for Oral Health Care SciencesUniversity College LondonLondonUK

Personalised recommendations