Organically Grown Architectures: Creating Decentralized, Autonomous Systems by Embryomorphic Engineering

  • René Doursat
Part of the Understanding Complex Systems book series (UCS)


Exploding growth growth in computational systems forces us to gradually replace rigid design and control with decentralization and autonomy. Information technologies will progress, instead, by“meta-designing” mechanisms of system self-assembly, self-regulation and evolution. Nature offers a great variety of efficient complex systems, in which numerous small elements form large-scale, adaptive patterns. The new engineering challenge is to recreate this self-organization and let it freely generate innovative designs under guidance. This article presents an original model of artificial system growth inspired by embryogenesis. A virtual organism is a lattice of cells that proliferate, migrate and self-pattern into differentiated domains. Each cell’s fate is controlled by an internal gene regulatory network network. Embryomorphic engineering emphasizes hyperdistributed architectures, and their development as a prerequisite of evolutionary design.

complex systems artificial development evolutionary computation systems embryomorphic engineering 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    H. Abelson, D. Allen, D. Coore, C. Hanson, G. Homsy, T. Knight Jr., R. Nagpal, E. Rauch, G. Sussman, and R. Weiss. Amorphous Computing. MIT Artificial Intelligence Laboratory memo no. 1665, Aug. 1999.Google Scholar
  2. 2.
    W. S. Bainbridge, and M. C. Roco, eds. Managing Nano-Bio-Info-Cogno Innovations: Converging Technologies in Society. Berlin: Springer Science and Business Media, 2006.Google Scholar
  3. 3.
    P. Ball. The Self-Made Tapestry: Pattern Formation in Nature. Oxford University Press, 1999.Google Scholar
  4. 4.
    A.-L. Barabási, and R. Albert. Emergence of scaling in random networks. Science 286: 509-512, 1999.MathSciNetADSCrossRefGoogle Scholar
  5. 5.
    Beyond the Horizon: Anticipating Future and Emerging Information Society Technologies Final Report, ERCIM, FET, IST, EU, 2006.Google Scholar
  6. 6.
    W. Callebaut, and D. Rasskin-Gutman, eds. Modularity. The MIT Press, 2005.Google Scholar
  7. 7.
    S. B. Carroll. Endless Forms Most Beautiful: The New Science of Evo Devo and the Making of the Animal Kingdom. W. W. Norton & Company, 2005.Google Scholar
  8. 8.
    S. B. Carroll, J. K. Grenier, and S. D. Weatherbee. From DNA to Diversity, Blackwell Scientific (Malden, MA), 2001.Google Scholar
  9. 9.
    E. Coen. The Art of Genes. Oxford University Press, 2000.Google Scholar
  10. 10.
    R. Dawkins. Climbing Mount Improbable. W. W. Norton & Company, 1996.Google Scholar
  11. 11.
    R. Doursat. The growing canvas of biological development: Multiscale pattern generation on an expanding lattice of gene regulatory networks. InterJournal: Complex Systems 1809, 2006.Google Scholar
  12. 12.
    R. Doursat, and E. Bienenstock. Neocortical self-structuration as a basis for learning. 5th International Conference on Development and Learning (ICDL), Indiana University, Bloomington, Indiana, May 31-June 3, 2006.Google Scholar
  13. 13.
    G. M. Edelman. Topobiology: An Introduction to Molecular Emrbyology. Basic Books, 1988.Google Scholar
  14. 14.
    A. Gierer, and H. Meinhardt. A theory of biological pattern formation, Kybernetik 12: 30-39, 1972.CrossRefGoogle Scholar
  15. 15.
    S. A. Kauffman. Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 22: 437–467, 1969.MathSciNetCrossRefGoogle Scholar
  16. 16.
    M. W. Kirschner, and J. C. Gerhart. The Plausibility of Life: Resolving Darwin’s Dilemma. New Haven and London: Yale University Press, 2005.Google Scholar
  17. 17.
    M. Komosinski, and S. Ulatowski. Framsticks: Towards a simulation of a nature-like world, creatures and evolution. In D. Floreano, J.-D. Nicoud, and F. Mondada, eds., 5th European Conference on Advances in Artificial Life (ECAL-99), pp261-265, Lausanne, Sept. 13-17, 1999.Google Scholar
  18. 18.
    S. Kondo, and R. Asai. A reaction-diffusion wave on the skin of the marine angelfish Pomacanthus. Nature 376: 765-768, 1995.ADSCrossRefGoogle Scholar
  19. 19.
    R. E. Lenski, C. Ofria, R. T. Pennock, and C. Adami. The evolutionary origin of complex features. Nature 423: 139-144, 2003.ADSCrossRefGoogle Scholar
  20. 20.
    H. Lipson, and J. B. Pollack. Automatic design and manufacture of robotic lifeforms. Nature 406: 974-978, 2000.ADSCrossRefGoogle Scholar
  21. 21.
    H. Meinhardt. The Algorithmic Beauty of Sea Shells. Springer-Verlag, 1998.Google Scholar
  22. 22.
    J. F. Miller, and W. Banzhaf. Evolving the program for a cell: from French flags to Boolean circuits. In S. Kumar and P. J. Bentley, eds., On Growth, Form and Computers. Academic Press, 2003.Google Scholar
  23. 23.
    A. A. Minai, D. Braha, and Y. Bar-Yam. Complex engineered systems. In D. Braha, Y. Bar-Yam and A. A. Minai, eds., Complex Engineered Systems: Science Meets Technology. Springer Verlag, 2006.Google Scholar
  24. 24.
    E. Mjolsness, D. H. Sharp, and J. Reinitz. A connectionist model of development. Journal of Theoretical Biology, 152: 429–453, 1991.CrossRefGoogle Scholar
  25. 25.
    R. Nagpal. Programmable self-assembly using biologically-inspired multi-agent control. 1st Int Conf on Autonomous Agents, Bologna, Italy, July 15-19, 2002.Google Scholar
  26. 26.
    H. F. Nijhout. A comprehensive model for colour pattern formation in butterflies. Proc. R. Soc. Lond. B 239: 81-113, 1990.ADSCrossRefGoogle Scholar
  27. 27.
    D. E. Nilsson, and S. Pelger. A pessimistic estimate of the time required for an eye to evolve. Proceedings of the Royal Society of London B 256: 53-58, 1994.Google Scholar
  28. 28.
    L. Nunes de Castro. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall/Crc Computer and Information Sciences, 2006.Google Scholar
  29. 29.
    J. E. Pearson. Complex patterns in a simple system. Science 261: 189-192, 1993.ADSCrossRefGoogle Scholar
  30. 30.
    I. Salazar-Ciudad, J. Garcia-Fernández, and R. Solé. Gene networks capable of pattern formation. Journal of Theoretical Biology 205: 587–603, 2000.CrossRefGoogle Scholar
  31. 31.
    G. Schlosser, and G. P. Wagner, eds. Modularity in Development and Evolution. The University of Chicago Press, 2004.Google Scholar
  32. 32.
    P. Siero, G. Rozenberg, and A. Lindenmayer. Cell division patterns: syntactical description and implementation. Computer Graphics and Image Processing 18: 329-346, 1982.CrossRefGoogle Scholar
  33. 33.
    K. Stanley, B. Bryant, and R. Miikkulainen. Real-time evolution in the NERO video game. IEEE Symposium on Computational Intelligence and Games, pp182-189, Essex University, Colchester, UK, April 4-6, 2005.Google Scholar
  34. 34.
    H. L. Swinney, and V. I. Krinsky, eds. Waves and patterns in chemical and biological media. The MIT Press, 1991.Google Scholar
  35. 35.
    A. M. Turing. The chemical basis of morphogenesis. Phil. Trans. R. Soc. London B 237: 37-72, 1952.ADSCrossRefGoogle Scholar
  36. 36.
    G. von Dassow, E. Meir, E. M. Munro, and G. M. Odell. The segment polarity network is a robust developmental module. Nature 406: 188–192, 2000.ADSCrossRefGoogle Scholar
  37. 37.
    C. von der Malsburg, R. P. Würtz, and A. Schäfer. The Organic Computing Group,, 2006.
  38. 38.
    D. J. Watts, and S. H. Strogatz. Collective dynamics of “small-world” networks. Nature 393: 440-442, 1998.ADSCrossRefGoogle Scholar
  39. 39.
    G. Webster, and B. Goodwin. Form and Transformation: Generative and Relational Principles in Biology. Cambridge University Press, 1996.Google Scholar
  40. 40.
    M. Weiser. Some computer science issues in ubiquitous computing. Communications of the ACM 36: 75-84, 1993.CrossRefGoogle Scholar
  41. 41.
    J. Werfel, and R. Nagpal. Extended stigmergy in collective construction. IEEE Intelligent Systems 21(2): 20-28, 2006.CrossRefGoogle Scholar
  42. 42.
    A. Winfree. The geometry of biological time. Springer-Verlag, 1980, 2001.Google Scholar
  43. 43.
    L. Wolpert. Positional information and the spatial pattern of cellular differentiation development. Journal of Theoretical Biology 25: 1–47, 1969.CrossRefGoogle Scholar
  44. 44.
    D. Young. A local activator-inhibitor model of vertebrate skin patterns. Mathematical Biosciences 72: 51-58, 1984.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • René Doursat
    • 1
  1. 1.Centre de Recherche en Epistèmologie Appliquée (CREA)Institut des Systèmes Complexes (ISC),CNRS and Ecole Polytechnique75005 ParisFrance

Personalised recommendations