Programming Living Machines: The Case Study of Escherichia Coli

  • Jole Costanza
  • Luca Zammataro
  • Giuseppe Nicosia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8608)


In 1952, Turing outlined computational processes in the morphogenesis [8], thus thinking of the biological evolution of an organism as a consequence of the computation that it can perform. Following Turing’s idea on morphogenesis, many biological processes have been recently analysed from a computational standpoint. In 1995, Bray [2] argued that a single protein is a computational or information carrying element, being able to convert input signals into an output signal. Evolution had already been associated with computation many years before, by von Neumann and Burks [9], who constructed a self-replicating cellular automaton with the aim of developing synthetic models of a living organism. Starting from this concept, in this work we propose a relation between computation and metabolism.


Pareto Front Turing Machine Flux Balance Analysis Molecular Machine Pareto Point 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Angione, C., et al.: Computing with metabolic machines. In: Voronkov, A. (ed.) Turing-100. EPiC Series, vol. 10, pp. 1–15 (2012)Google Scholar
  2. 2.
    Bray, D., et al.: Protein molecules as computational elements in living cells. Nature 376(6538), 307–312 (1995)CrossRefGoogle Scholar
  3. 3.
    Costanza, J., et al.: Robust design of microbial strains. Bioinformatics 28(23), 3097–3104 (2012)CrossRefGoogle Scholar
  4. 4.
    Cutello, V., Narzisi, G., Nicosia, G.: A class of pareto archived evolution strategy algorithms using immune inspired operators for ab-initio protein structure prediction. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 54–63. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Cutello, V., et al.: A multi-objective evolutionary approach to the protein structure prediction problem. Journal of the Royal Society Interface 3(6), 139–151 (2006)CrossRefGoogle Scholar
  6. 6.
    Orth, J.D., et al.: A comprehensive genome-scale reconstruction of Escherichia coli metabolism-2011. Molecular Systems Biology 77(Article number 535), 1–9 (2011)Google Scholar
  7. 7.
    Reed, J., et al.: An expanded genome-scale model of escherichia coli k-12 (ijr904 gsm/gpr). Genome Biology 4(9), R54 (2003)Google Scholar
  8. 8.
    Turing, A.M.: The chemical basis of morphogenesis. Bulletin of mathematical biology 52(1), 153–197 (1990)CrossRefGoogle Scholar
  9. 9.
    Von Neumann, J., Burks, A.W., et al.: Theory of self-reproducing automata (1966)Google Scholar
  10. 10.
    Yim, H.R., et al.: Metabolic engineering of escherichia coli for direct production of 1,4-butanediol. Nature Chemical Biology 7(7), 445–452 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jole Costanza
    • 1
  • Luca Zammataro
    • 1
  • Giuseppe Nicosia
    • 2
  1. 1.Italian Institute of TechnologyMilanItaly
  2. 2.Department of Mathematics and Computer ScienceUniversity of CataniaItaly

Personalised recommendations