Artificial Chemistry and Molecular Network

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 320)

Abstract

This chapter focuses on artificial chemistry, a research approach for constructing life-like systems in artificial environments, and presents its fundamental concepts and system design requirements. Based on this discussion, we move on to evaluate typical artificial chemistry systems: We propose 13 conditions necessary for emergent evolution and three topological conditions that must be met by the rules for transport of symbols. We also introduce the concept of a molecular network, which emulates the spatial relationship of the molecules. With hard-sphere random-walk simulations, we show seven topological conditions that the molecular network must satisfy. In the latter half of this chapter, we feature the example of network artificial chemistry (NAC), in which a molecular network is used for spatial representation, and present some of the research results derived since this was first proposed. We begin by introducing a model wherein a cluster formed by folding a node chain functions as an active machine. We then overview studies on rewiring rules for weak edges, which form the basis for network dynamics. We discuss the merits and demerits of the method expressing spatial constraints derived from the network energy, then introduce models devised to circumvent the difficulties: a model that implements active functions (programs) in the nodes and an improved model that implements the programs in agents to allow them to move within the network. The improved model, called “program-flow computing,” is expected to be refined into a new computing model.

Keywords

Artificial chemistry Chemical reaction Molecular interaction Self-organization 

References

  1. 1.
    C. Adami, C.T. Brown, Evolutionary learning in the 2D artificial life system “Avida”, in Artificial Life IV: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systemsed, ed. by R. Brooks, P. Maes (MIT Press, Cambridge, 1994), pp. 377–381Google Scholar
  2. 2.
    C. Adami, Learning and complexity in genetic auto-adaptive systems. Physica D 80, 154–170 (1995)MATHCrossRefGoogle Scholar
  3. 3.
    C. Adami, Introduction to Artificial Life (Springer-Verlag, Santa Clara, CA, 1998)MATHGoogle Scholar
  4. 4.
    C. Adami, C. Ofria, T.C. Collier, Evolution of biological complexity. Proc. Natl. Acad. Sci. U S A 97, 4463–4468 (2000)CrossRefGoogle Scholar
  5. 5.
    aiSee: Commercial software for visualizing graphs with various algorithms such as rubberband. http://www.aisee.com/
  6. 6.
    U. Alon, An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall (Crc Mathematical and Computational Biology Series, 2006)Google Scholar
  7. 7.
    Y. Asada, R. Suzuki, T. Arita, A simple artificial chemistry model based on the relationships between particles. In: Proceedings of the 36th SICE Symposium on Intelligent Systems, The Society of Instrument and Control Engineers (2009), pp. 227–232Google Scholar
  8. 8.
    G.M. Barrow, Physical Chemistry (McGraw-Hill Education, New York, 1988), Chapters 15–17. Japanese translation: Barrow, translated into Japanese by H. Daimon, K. Domen, Butsuri Kagaku Dai 6 Han (Ge) (Physical Chemistry, 6th edn.). (Tokyo Kagaku Dojin, Tokyo, 1999)Google Scholar
  9. 9.
    M. Bedau, P. Husbands, T. Hutton, S. Kumar, H. Suzuki (eds.) Workshop and Tutorial Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (Alife IX). (2004)Google Scholar
  10. 10.
    P. Dittrich, W. Banzhaf, Self-evolution in a constructive binary string system. Artificial Life 4, 203–220 (1998)CrossRefGoogle Scholar
  11. 11.
    P. Dittrich, J. Ziegler, W. Banzhaf, Artificial chemistries—a review. Artificial Life 7, 225–275 (2001)CrossRefGoogle Scholar
  12. 12.
    P. Espanol, P.B. Warren, Statistical-mechanics of dissipative particle dynamics. Europhysics Letters 30(4), 191–196 (1995)CrossRefGoogle Scholar
  13. 13.
    R. Ewaschuk, P.D. Turney, Self-replication and self-assembly for manufacturing. Artificial Life 12(3), 411–433 (2006)CrossRefGoogle Scholar
  14. 14.
    W. Fontana, Algorithmic chemistry, in Artificial Life II: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, ed. by C.G. Langton et al. Santa Fe Institute Studies in the Sciences of Complexity, Vol. 10 (Addison-Wesley, 1992), pp. 159–209Google Scholar
  15. 15.
    W. Fontana, L.W. Buss, ‘The arrival of the fittest’: toward a theory of biological organization. Bull. Math. Biol. 56, 1–64 (1994)MATHGoogle Scholar
  16. 16.
    T. Gánti, Organisation of chemical reactions into dividing and metabolizing units: the chemotons. BioSystems 7, 189–195 (1975)CrossRefGoogle Scholar
  17. 17.
    J.A. Glazier, F. Graner, Simulation of the differential adhesion driven rearrangement of biological cells. Phys. Rev. E 47, 2128–2154 (1993)CrossRefGoogle Scholar
  18. 18.
    F. Graner, J.A. Glazier, Simulation of biological cell sorting using a two-dimensional extended Potts model. Phys. Rev. Lett. 69, 2013–2016 (1992)CrossRefGoogle Scholar
  19. 19.
    R.D. Groot, P.B. Warren, Dissipative particle dynamics: bridging the gap between atomistic and mesoscopic simulation. Journal of Chemical Physics 107, 4423–4435 (1997)CrossRefGoogle Scholar
  20. 20.
    N. Hirokawa, Kinesin and Dynein Superfamily Proteins and the Mechanism of Organelle Transport. Science 279, 519–526 (1998)CrossRefGoogle Scholar
  21. 21.
    P.J. Hoogerbrugge, J.M.V.A. Koelman, Simulating microscopic hydrodynamic phenomena with dissipative particle dynamics. Europhysics Letters 19(3), 155–160 (1992)CrossRefGoogle Scholar
  22. 22.
    T.J. Hutton, Evolvable self-replicating molecules in an artificial chemistry. Artificial Life 8, 341–356 (2002)CrossRefMathSciNetGoogle Scholar
  23. 23.
    T.J. Hutton, Information-replicating molecules with programmable enzymes, in Proceedings of the Sixth International Conference on Humans and Computers (HC-2003), 2003, pp. 170–175Google Scholar
  24. 24.
    T.J. Hutton, Evolvable self-reproducing cells in a two-dimensional artificial chemistry. Artificial Life 13(1), 11–30 (2007)CrossRefMathSciNetGoogle Scholar
  25. 25.
    T.J. Hutton, The organic builder: a public experiment in artificial chemistries and self-replication. Artificial Life 15(1), 21–28 (2009)CrossRefGoogle Scholar
  26. 26.
    K. Imai, T. Hori, K. Morita, Self-reproduction in three-dimensional reversible cellular space. Artificial Life 8(2), 155–174 (2002)CrossRefGoogle Scholar
  27. 27.
    N. Ishii, K. Nakahigashi, T. Baba, M. Robert, T. Soga, A. Kanai, T. Hirasawa, M. Naba, K. Hirai, A. Hoque, P.Y. Ho, Y. Kakazu, K. Sugawara, S. Igarashi, S. Harada, T. Masuda, N. Sugiyama, T. Togashi, M. Hasegawa, Y. Takai, K. Yugi, K. Arakawa, N. Iwata, Y. Toya, Y. Nakayama, T. Nishioka, K. Shimizu, H. Mori, M. Tomita, Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316(5824), 593–597 (2007)CrossRefGoogle Scholar
  28. 28.
    H. Kitano, Systems biology: a brief overview. Science 295(5560), 1662–1664 (2002)CrossRefGoogle Scholar
  29. 29.
    C.G. Langton, Self-reproduction in cellular automata. Physica D 10, 135–144 (1984)CrossRefGoogle Scholar
  30. 30.
    J.D. Lohn, J.A. Reggia, Automatic discovery of self-replicating structures in cellular automata. IEEE Transactions on Evolutionary Computation 1, 165–178 (1997)CrossRefGoogle Scholar
  31. 31.
    D. Madina, N. Ono, T. Ikegami, Cellular evolution in a 3D lattice artificial chemistry, in Advances in Artificial Life (7th European Conference on Artificial Life Proceedings), ed. by W. Banzhaf, T. Christaller, P. Dittrich, J.T. Kim, J. Ziegler (Springer-Verlag, Berlin, 2003), pp. 59–68Google Scholar
  32. 32.
    T. Maeshiro, M. Kimura, The role of robustness and changeability on the origin and evolution of genetic codes. Proc. Nat. Acad. Sci. USA 95, 5088–5093 (1998)CrossRefGoogle Scholar
  33. 33.
    J. Maynard-Smith, E. Szathmáry, The Major Transitions in Evolution. Springer-Verlag, Berlin (1995). Japanese translation: J. Maynard-Smith, E. Szathmáry, translated into Japanese by K. Nagano, Shinka Suru Kaisou (Springer-Verlag Tokyo, 1997)Google Scholar
  34. 34.
    B. McMullin, F.R. Varela, Rediscovering computational autopoieses, in Proceedings of the 4th European Conference on Artificial Life, ed. by P. Husband, I. Harvey (MIT Press, Cambridge, MA, 1997), pp. 38–47Google Scholar
  35. 35.
    B. McMullin, D. Groß, D, Towards the Implementation of evolving autopoietic artificial agents, in Advances in Artificial Life (6th European Conference on Artificial Life Proceedings), ed. by J. Kelemen, P. Sosik (Springer-Verlag, Berlin, 2001), pp. 440–443Google Scholar
  36. 36.
    D. Noble, The Music of Life: Biology Beyond Genes (Oxford University Press, 2008). Japanese translation: D. Noble, translated into Japanese by Y. Kurachi, Seimei-no Ongaku – Genomu wo Koete System Biology Heno Shoutai (Shin-you sha, 2009)Google Scholar
  37. 37.
    H. Noguchi, M. Takasu, Self-assembly of amphiphiles into vesicles: a Brownian dynamics simulation. Phys. Rev. E 64 (2001) 041913Google Scholar
  38. 38.
    N. Ono, T. Ikegami, Model of self-replicating cell capable of self-maintenance, in Advances in Artificial Life (5th European Conference on Artificial Life Proceedings), ed. by D. Floreano (Springer-Verlag, Berlin, 1999), pp. 399–406Google Scholar
  39. 39.
    N. Ono, T. Ikegami, Self-maintenance and self-reproduction in an abstract cell model. J. Theor. Biol. 206, 243–253 (2000)CrossRefGoogle Scholar
  40. 40.
    N. Ono, T. Ikegami, Artificial chemistry: computational studies on the emergence of self-reproducing units, in Advances in Artificial Life (6th European Conference on Artificial Life Proceedings), ed. by J. Kelemen, P. Sosik (Springer-Verlag, Berlin, 2001), pp. 186–195Google Scholar
  41. 41.
    N. Ono, H. Suzuki, String-based artificial chemistry that allows maintenance of different types of self-replicators. The Journal of Three Dimensional Images 16(4), 148–153 (2002)Google Scholar
  42. 42.
    T. Oohashi, H. Sayama, O. Ueno, T. Maekawa, Programmed self-decomposition model and artificial life. Prodeedings of the 1995 International Workshop on Biologically Inspired Evolutionary Systems. Sony CSL, Tokyo (1995), pp. 85–92Google Scholar
  43. 43.
    T. Oohashi, T. Maekawa, O. Ueno, E. Nishina, N. Kawai, Requirements for immortal ALife to exterminate mortal ALife in one finite, heterogeneous ecosystem, in Advances in Artificial Life (5th European Conference on Artificial Life Proceedings), ed. by D. Floreano et al. (Springer-Verlag, Berlin, 1999), pp. 49–53Google Scholar
  44. 44.
    T. Oohashi, T. Maekawa, O. Ueno, N. Kawai, E. Nishina, K. Shimohara, Artificial life based on the programmed self-decomposition model: SIVA. Journal of Artificial Life and Robotics 5, 77–87 (2001)CrossRefGoogle Scholar
  45. 45.
    T. Oohashi, O. Ueno, T. Maekawa, N. Kawai, E. Nishina, M. Honda, An effective hierarchical model for the biomolecular covalent bond: an approach integrating artificial chemistry and an actual terrestrial life system. Artificial Life 15(1), 29–58 (2009)CrossRefGoogle Scholar
  46. 46.
    N.B. Ouchi, J.A. Glazier, J.P. Rieu, A. Upadhyaya, Y. Sawada, Improving the realism of the cellular Potts model in simulations of biological cells. Physica A 329(3–4), 451–458 (2003)MATHCrossRefMathSciNetGoogle Scholar
  47. 47.
    A.N. Pargellis, The spontaneous generation of digital ``Life’’. Physica D 91, 86–96 (1996a)MATHCrossRefGoogle Scholar
  48. 48.
    A.N. Pargellis, The evolution of self-replicating computer organisms. Physica D 98, 111–127 (1996b)MATHCrossRefGoogle Scholar
  49. 49.
    A.N. Pargellis, Digital life behavior in the Amoeba world. Artificial Life 7, 63–75 (2001)CrossRefGoogle Scholar
  50. 50.
    T.S. Ray, An approach to the synthesis of life, in Artificial Life II: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, ed. by C.G. Langton et al. Santa Fe Institute Studies in the Sciences of Complexity, Vol. 10 (Addison-Wesley, 1992), pp. 371–408Google Scholar
  51. 51.
    T.S. Ray, J. Hart, Evolution of differentiated multi-threaded digital organisms, in Artificial Life VI: Proceedings of the Sixth International Conference on Artificial Life, ed. by C. Adami, R.K. Belew, H. Kitano, C.E. Taylor (MIT Press, Cambridge, MA, 1998), pp. 295–304Google Scholar
  52. 52.
    H. Sayama, A new structurally dissolvable self-reproducing loop evolving in a simple cellular automata space. Artificial Life 5(4), 343–365 (2000)CrossRefGoogle Scholar
  53. 53.
    H. Sayama, Self-replicating worms that increase structural complexity through gene transmission, in Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life, ed. by M.A. Bedau et al. (MIT Press, Cambridge, 2000), pp. 21–30Google Scholar
  54. 54.
    H. Sayama, Swarm chemistry. Artificial Life 15(1), 105–114 (2009)CrossRefGoogle Scholar
  55. 55.
    A. Smith, P. Turney, R. Ewaschuk, Self-replicating machines in continuous space with virtual physics. Artificial Life 9(1), 21–40 (2003)CrossRefGoogle Scholar
  56. 56.
    P. Speroni di Fenizio, W. Banzhaf, Stability of metabolic and balanced organisations, in Advances in Artificial Life (6th European Conference on Artificial Life Proceedings), ed. by J. Kelemen, P. Sosik (Springer-Verlag, Berlin, 2001), pp. 196–205Google Scholar
  57. 57.
    P. Speroni di Fenizio, P. Dittrich, W. Banzhaf, Spontaneous formation of proto-cells in an universal artificial chemistry on a planar graph, in Advances in Artificial Life (6th European Conference on Artificial Life Proceedings), ed. by J. Kelemen, P. Sosik (Springer-Verlag, Berlin, 2001), pp. 206–215Google Scholar
  58. 58.
    H. Suzuki, An approach to biological computation: unicellular core-memory creatures evolved using genetic algorithms. Artificial Life 5(4), 367–386 (1999)CrossRefGoogle Scholar
  59. 59.
    H. Suzuki, Evolution of self-reproducing programs in a core propelled by parallel protein execution. Artificial Life 6(2), 103–108 (2000a)CrossRefGoogle Scholar
  60. 60.
    H. Suzuki, N. Ono, Universal replication in a string-based artificial chemistry system. The Journal of Three Dimensional Images 16(4), 154–159 (2002)Google Scholar
  61. 61.
    H. Suzuki, N. Ono, K. Yuta, Several necessary conditions for the evolution of complex forms of life in an artificial environment. Artificial Life 9(2), 537–558 (2003)CrossRefGoogle Scholar
  62. 62.
    H. Suzuki, Artificial chemistry on small-world networks, in Proceedings of the 18th Annual Conference of JSAI, 2H4-03 (2004)Google Scholar
  63. 63.
    H. Suzuki, Spacial representation for artificial chemistry based on small-world networks, in Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (Artificial Life IX) (2004), ed. by J. Pollack, M. Bedau, P. Husbands, T. Ikegami, R.A. Watson, pp. 507–513Google Scholar
  64. 64.
    H. Suzuki, Network artificial chemistry—molecular interaction represented by a graph, in Workshop and Tutorial Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (Alife IX) (2004). ed. by M. Bedau, P. Husbands, T. Hutton, S. Kumar, H. Suzuki), pp. 63–70Google Scholar
  65. 65.
    H. Suzuki, Computational folding of molecular chains in network artificial chemistry, in Proceedings of the 32th SICE Symposium on Intelligent Systems. The Society of Instrument and Control Engineers (2005), pp. 383–386Google Scholar
  66. 66.
    H. Suzuki, N. Ono, Statistical mechanical rewiring in network artificial chemistry. In: The 8th European Conference on Artificial Life (ECAL) Workshop Proceedings CD-ROM (Canterbury, UK, 2005)Google Scholar
  67. 67.
    H. Suzuki, N. Ono, Network rewiring rules representing molecular diffusion, in Proceedings of the 11th Emergent System Symposium (ESS) “Emergence Summer School 2005”, Toyama, Japan. The Society of Instrument and Control Engineers (2005), pp. 127–130Google Scholar
  68. 68.
    H. Suzuki, Mathematical folding of node chains in a molecular network. BioSystems 87, 125–135 (2007)CrossRefGoogle Scholar
  69. 69.
    H. Suzuki, An approach toward emulating molecular interaction with a graph. Australian Journal of Chemistry 59, 869–873 (2006)CrossRefGoogle Scholar
  70. 70.
    H. Suzuki, A node program that creates regular structure in a graph, in International Conference on Morphological Computation, Conference Proceedings, March 26–28, 2007, ECLT, Venice ItalyGoogle Scholar
  71. 71.
    H. Suzuki, A network cell with molecular tokens that divides from centrosome signals, in Proceedings of the Seventh International Workshop on Information Processing in Cells and Tissues (IPCAT), ed. by N. Crook, T. Scheper (Oxford Brookes University, 2007), pp. 293–304Google Scholar
  72. 72.
    H. Suzuki, Structural organization in network artificial chemistry by node programs and token flow, in SICE Annual Conference 2007 Proceedings. The Society of Instrument and Control Engineers (SICE), Japan (2007) 1C10, pp. 884–889Google Scholar
  73. 73.
    H. Suzuki, A network cell with molecular agents that divides from centrosome signals. BioSystems 94, 118–125 (2008)CrossRefGoogle Scholar
  74. 74.
    H. Suzuki, P. Dittrich (eds.), Special Issue on Artificial Chemistry. Artif. Life 15(1) (2009)Google Scholar
  75. 75.
    K. Suzuki, T. Ikegami, Shapes and self-movement in protocell systems. Artificial Life 15(1), 59–70 (2009)CrossRefGoogle Scholar
  76. 76.
    Y. Suzuki, S. Tsumoto, H. Tanaka, H, Analysis of cycles in symbolic chemical system based on abstract rewriting system on multisets, in Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems, ed. by C. Langton, K. Shimohara (MIT Press, Cambridge, MA, 1997), pp. 521–528Google Scholar
  77. 77.
    Y. Suzuki, H. Tanaka, Order parameter for a symbolic chemical system, in Artificial Life VI: Proceedings of the Sixth International Conference on Artificial Life, ed. by C. Adami, R.K. Belew, H. Kitano, C.E. Taylor (MIT Press, Cambridge, MA, 1998), pp. 130–139Google Scholar
  78. 78.
    Y. Suzuki, H. Tanaka, Chemical evolution among artificial proto-cells, in Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life, ed. by M.A. Bedau et al. (MIT Press, Cambridge, 2000), pp. 54–63Google Scholar
  79. 79.
    Y. Suzuki, Y. Fujiwara, Y., J. Takabayashi, H. Tanaka, Artificial life applications of a class of P systems: abstract rewriting systems on multisets. Lecture Notes in Computer Science, Vol. 2235 (Multiset Processing) Springer, Berlin/Heidelberg (2001), pp. 299–346Google Scholar
  80. 80.
    E. Szathmáry, J. Maynard-Smith, From replicators to reproducers: the first major transitions leading to life. J. theor. Biol. 187, 555–571 (1997)CrossRefGoogle Scholar
  81. 81.
    K. Tominaga, Modelling DNA computation by an artificial chemistry based on pattern matching and recombination, in Workshop and Tutorial Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (Alife IX), ed. by M. Bedau, P. Husbands, T. Hutton, S. Kumar, H. Suzuki (2004), pp. 56–62Google Scholar
  82. 82.
    K. Tominaga, T. Watanabe, K. Kobayashi, M. Nakamura, K. Kishi, M. Kazuno, Modeling molecular computing systems by an artificial chemistry—its expressive power and application. Artificial Life 13(3), 223–247 (2007)CrossRefGoogle Scholar
  83. 83.
    K. Tominaga, Y. Suzuki, K. Kobayashi, T. Watanabe, K. Koizumi, K. Kishi, Modeling biochemical pathways using an artificial chemistry. Artificial Life 15(1), 115–129 (2009)CrossRefGoogle Scholar
  84. 84.
    J. von Neumann, Theory of self-reproducing automata (University of Illinois Press, Urbana. Edited and completed by A.W. Burks, 1966)Google Scholar
  85. 85.
    S. Yamamoto, Y. Maruyama, S. Hyodo, The dissipative particle dynamics study of spontaneous vesicle formation of amphiphilic molecules. Journal of Chemical Physics 116(13), 5842–5849 (2002)CrossRefGoogle Scholar
  86. 86.
    T. Yamamoto, K. Kaneko, Tile automation: a model for an architecture of a living system. Artificial Life 5, 37–76 (1999)CrossRefGoogle Scholar
  87. 87.
    Barrow G. M. Physical chemistry, McGraw-Hill Education, Chapters 15–17, 1988. Japanese translation: Barrow, translated into Japanese by Hiroshi Daimon and Kazunari Domen, Butsuri Kagaku Dai 6 Han (Ge) (Physical Chemistry 6th Edition), Tokyo Kagaku Dojin, 1999Google Scholar
  88. 88.
    Masahiro Kotani, Kiyohiko Someda, and Seiichiro Kouda, edited by Tamotsu Kondo, Daigakuin Kogi Butsuri Kagaku (Graduate School Course Physical Chemistry), Tokyo Kagaku Dojin, 1997Google Scholar
  89. 89.
    Vemulapalli G.K. Physical chemistry, Prentice-Hall Inc., Chapters 23–33, 1993. Japanese translation: Vemulapalli, translated into Japanese and supervised by Ueno et al., Butsuri Kagaku III Kagaku Hanno Sokudoron to Tokei Netsurikigaku (Physical Chemistry III Chemical Reaction Velocity Theory and Statistical Thermodynamics), Maruzen Co. Ltd., 2000Google Scholar
  90. 90.
    R. Albert, A.L. Bárabasi, Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–98 (2002)CrossRefMathSciNetGoogle Scholar
  91. 91.
    J. Davidsen, H. Ebel, S. Bornholdt, Emergence of a small world from local interactions - Modeling acquaintance networks. Physical Review Letters 88(12), 128701 (2002)Google Scholar
  92. 92.
    Naoki Masuda, Norio Konno, Fukuzatsu Nettowaku no Kagaku (Science of Complex Networks), Sangyo Tosho, 2005Google Scholar
  93. 93.
    M.E.J Newman, The structure and function of complex networks. SIAM Review 45, 167–256 (2003)MATHCrossRefMathSciNetGoogle Scholar
  94. 94.
    Wataru Soma, Katsunori Shimohara, Sumoru Warudo Nettowaku no Yakuwari (Role of Small-world Networks), Documents for Category II Meeting of Institute of Electronics, Information and Communication Engineers, NGN2001-12, 13–20, 2001Google Scholar
  95. 95.
    D.J. Watts, S.H. Strogatz, Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)CrossRefGoogle Scholar
  96. 96.
    T.S. Ray, An approach to the synthesis of life, C.G. Langton , et al. (eds.). Artificial Life II: Proceedings of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems (Santa Fe Institute Studies in the Sciences of Complexity, 10), 371-408, Addison-Wesley, 1992Google Scholar
  97. 97.
    Yoichi Muraoka, Heiretsu Shori (Sofutowea Koza) (Parallel Processing (Software Course)), Shokodo Co., Ltd., Section 5.7, 1986Google Scholar
  98. 98.
    Sharp J.A. Data flow computing (Ellis Horwood Series in Computers and Their Applications), Ellis Horwood Ltd., 1985Google Scholar
  99. 99.
    Masahiro Sowa, Deta-furo Mashin to Gengo (Sofutowea Koza) (Data flow Machine and Languages (Software Course)), Shokodo Co., Ltd., 1986Google Scholar
  100. 100.
    Ryota Shioya, Hidetsugu Irie, Masahiro Goshima, Shuichi Sakai, Evaluation of Area-Oriented Register Cache, Proceedings of Information Processing Society of Japan, 2008-ARC-178, pp. 13–18, 2008Google Scholar
  101. 101.
    Abelson H., Allen D., Coore D., Hanson C., Homsy G., Knight T.F., Nagpal R., Rauch E., Sussman G.J., Weiss R. Amorphous computing, Communications of the ACM, vol. 43(5), pp. 74–82, 2000Google Scholar
  102. 102.
    The Bio FAB Group, Baker D., Church G., Collins J., Endy D., Jacobson J., Keasling J., Modrich P., Smolke C., Weiss R. Engineering life: building a fab for biology, Scientific American, vol. 294(6), pp. 44–51, 2006. Japanese translation: Bio FAB Group, Baker D., Church G., Collins J., Endy D., Jacobson J., Keasling J., Modrich P., Smolke C., Weiss R., Gosei Seibutsugaku wo Kasokusuru Baio Fabu, Nikkei Science, vol. 36 (9), pp. 32–41, 2006Google Scholar
  103. 103.
    Katsuhiko Ariga, Toyoki Kunitake, Iwanami Koza Gendai Kagaku eno Nyumon <16>Chobunsikagaku eno Tenkai (Iwanami Lecture Series, Introduction to Modern Chemistry<16> Development of Supermolecular Chemistry), Iwanami Shoten, 2000Google Scholar
  104. 104.
    Seiji Shinkai, Kazuki Sada, Masayuki Takeuchi, Norifumi Fujita, Bunshi Kikai: Seitai wo Tegakari to shite (Molecular Machine: Using Living Bodies as Clues), Edited by Naoki Sugimoto, Kagaku Furontia 13 Nano Baio Enjiniaringu - Seimei to Busshitsu no Yugo wo Mezasite (Chemistry Frontier 13 Nanobioengineering—Toward a Fusion of Life and Materials), Kagaku-Dojin Publishing Company, Inc., 50–60, 2004Google Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.National Institute of Information and Communications TechnologyKobeJapan

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