The Modeling and the Simulation of the Fluid Machines of Synthetic Biology

  • Jean-Louis Giavitto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7184)


In the past century, several conceptual and technological breakthroughs produced the digital computers and open the digital information age. At the very same time, the Watson – Crick model of the digital coding of the genetic information was developed. Despite this parallel development, biology as long focused in the understanding of existing systems shaped by natural evolution whilst computer science has built its own (hardware and software) objects from scratch.

This situation is no longer true: the emergence of synthetic biology opens the doors to the systematic design and construction of biological (fluid) machines. However, even if fluid machines can be based on a kind of digital information processing, they differ from the discrete dynamical systems we are used in computer science: they have a dynamical structure.

In this paper, we stress the parallel between the development of digital information processing and genetic information processing. We sketch some tools developed or appropriated in computer science that can be used to model and specify such fluid machines. We show through an example the use of mgs, a domain specific language, in the proof of concept of a “multicellular bacterium” designed at the 2007 iGEM competition.


fluid machines synthetic biology computer modeling and simulation ds2: dynamical systems with a dynamical structure spatial computing topological rewriting domain specific language (DSL) MGS 


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  1. 1.
    Amos, M.: Cellular computing. Series in systems biology. Oxford University Press (2004)Google Scholar
  2. 2.
    Bikard, D., Caffin, F., Chiaruttini, N., Clozel, T., Guegan, D., Landrain, T., Puyraimond, D., Rizk, A., Shotar, E., Vieira, G.: The SMB: Synthetic Multicellular Bacterium (iGEM 2007 Paris team web site) (2007), (visited in July 2011)
  3. 3.
    Brown, J.: The iGEM competition: building with biology. Synthetic Biology, IET 1(1.2), 3–6 (2007)CrossRefGoogle Scholar
  4. 4.
    Canton, B., Labno, A., Endy, D.: Refinement and standardization of synthetic biological parts and devices. Nature Biotechnology 26(7), 787–793 (2008)CrossRefGoogle Scholar
  5. 5.
    Chin, J.: Programming and engineering biological networks. Current Opinion in Structural Biology 16(4), 551–556 (2006)CrossRefGoogle Scholar
  6. 6.
    Eigen, M., Schuster, P.: The Hypercycle: A Principle of Natural Self-Organization. Springer, Heidelberg (1979)CrossRefGoogle Scholar
  7. 7.
    Elowitz, M., Leibler, S.: A synthetic oscillatory network of transcriptional regulators. J. Biol. Chem. 274, 6074–6079 (1999)CrossRefGoogle Scholar
  8. 8.
    Endy, D.: Foundations for engineering biology. Nature 438(7067), 449–453 (2005)CrossRefGoogle Scholar
  9. 9.
    Fontana, W., Buss, L.W.: The arrival of the fittest: Toward a theory of biological organization. Bulletin of Mathematical Biology (1994)Google Scholar
  10. 10.
    Gánti, T.: Chemoton theory. Theoretical Foundations of Fluid Machineries, Theory of Living Systems, vol. 1, 2. Kluwer Academic/Plenum (2003)Google Scholar
  11. 11.
    Gardner, T., Cantor, C., Collins, J.: Construction of a genetic toggle switch inescherichia coli. Nature 403, 339–342 (2000)CrossRefGoogle Scholar
  12. 12.
    Giavitto, J.L.: Topological Collections, Transformations and Their Application to the Modeling and the Simulation of Dynamical Systems. In: Nieuwenhuis, R. (ed.) RTA 2003. LNCS, vol. 2706, pp. 208–233. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Giavitto, J.L., Godin, C., Michel, O., Prusinkiewicz, P.: Computational Models for Integrative and Developmental Biology. In: Modeling and Simulation of Biological Processes in the Context of Genomics, Hermes (July 2002), also republished as an high-level course in the proceedings of the Dieppe spring school on Modeling and simulation of biological processes in the context of genomics, Dieppes, France, May 12-17 (2003)Google Scholar
  14. 14.
    Giavitto, J.L., Michel, O.: Data Structure as Topological Spaces. In: Calude, C.S., Dinneen, M.J., Peper, F. (eds.) UMC 2002. LNCS, vol. 2509, pp. 137–150. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  15. 15.
    Giavitto, J.L., Michel, O.: Modeling the topological organization of cellular processes. BioSystems 70(2), 149–163 (2003)CrossRefGoogle Scholar
  16. 16.
    Giavitto, J.L., Michel, O., Delaplace, F.: Declarative simulation of dynamicals systems: the 8 1/2 programming language and its application to the simulation of genetic networks. BioSystems 68(2-3), 155–170 (2003)CrossRefGoogle Scholar
  17. 17.
    Giavitto, J.L.: Simulation de systèmes à structure dynamique: modélisation en morphogenèse et application à la conception de machines fluide. In: Colloque National des systèmes complexes: Vers une science et ingénierie des systèmes complexes (SISC 2009), May 27-29 (2009), invited Speaker. Video published at address
  18. 18.
    Giavitto, J.L., Michel, O.: Mgs: a rule-based programming language for complex objects and collections. In: van den Brand, M., Verma, R. (eds.) Electronic Notes in Theoretical Computer Science, vol. 59. Elsevier Science Publishers (2001)Google Scholar
  19. 19.
    Giavitto, J.L., Michel, O.: The topological structures of membrane computing. Fundamenta Informaticae 49, 107–129 (2002)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Giavitto, J.L., Spicher, A.: Topological rewriting and the geometrization of programming. Physica D 237(9), 1302–1314 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Guet, C., Elowitz, M., Hsing, W., Leibler, S.: Combinatorial synthesis of genetic networks. Science 296(5572), 1466 (2002)CrossRefGoogle Scholar
  22. 22.
    Heinemann, M., Panke, S.: Synthetic biology – putting engineering into biology. Bioinformatics 22(22), 2790 (2006)CrossRefGoogle Scholar
  23. 23.
    Henle, M.: A combinatorial introduction to topology. Dover Publications, New-York (1994)zbMATHGoogle Scholar
  24. 24.
    TESSY EU-NEST PathFinder initiative: Towards a european strategy for synthetic biology. See especially Deliverable 2.6: Final Roadmap towards Synthetic Biology in Europe (2008), (visited in July 2011)
  25. 25.
    Keller, E.F.: Refiguring Life: Metaphors of Twentieth-century Biology. Columbia University Press (1995)Google Scholar
  26. 26.
    Knight, T.: Idempotent vector design for standard assembly of biobricks. Tech. rep., DTIC Document (2003),
  27. 27.
    Kobayashi, H., Kærn, M., Araki, M., Chung, K., Gardner, T., Cantor, C., Collins, J.: Programmable cells: interfacing natural and engineered gene networks. Proceedings of the National Academy of Sciences of the United States of America 101(22), 8414 (2004)CrossRefGoogle Scholar
  28. 28.
    Michel, O., Spicher, A., Giavitto, J.L.: Rule-based programming for integrative biological modeling – application to the modeling of the λ phage genetic switch. Natural Computing 8(4), 865–889 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Norris, V., Zemirline, A., Amar, P., Audinot, J., Ballet, P., Ben-Jacob, E., Bernot, G., Beslon, G., Cabin, A., Fanchon, E., Giavitto, J.L., Glade, N., Greussay, P., Grondin, Y., Foster, J., Hutzler, G., Jost, J., Kepes, F., Michel, O., Molina, F., Signorini, J., Stano, P., Thierry, A.: Computing with bacterial constituents, cells and populations: from bioputing to bactoputing. Theory in Biosciences, 1–18 (2011)Google Scholar
  30. 30.
    Păun, G.: From cells to computers: computing with membranes (P systems). Biosystems 59(3), 139–158 (2001)CrossRefGoogle Scholar
  31. 31.
    Raoult, J.C., Voisin, F.: Set-theoretic Graph Rewriting. In: Ehrig, H., Schneider, H.-J. (eds.) Dagstuhl Seminar 1993. LNCS, vol. 776, pp. 312–325. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  32. 32.
    Barbier de Reuille, P., Bohn-Courseau, I., Ljung, K., Morin, H., Carraro, N., Godin, C., Traas, J.: Computer simulations reveal properties of the cell-cell signaling network at the shoot apex in Arabidopsis. PNAS 103(5), 1627–1632 (2006), CrossRefGoogle Scholar
  33. 33.
    Ronzenberg, G., Salomaa, A. (eds.): L systems: from formalism to programming languages. Springer, Heidelberg (1992)Google Scholar
  34. 34.
    Schrödinger, E.: What is Life? Cambridge University Press, Cambridge (1944)Google Scholar
  35. 35.
    Spicher, A., Michel, O., Cieslak, M., Giavitto, J.L., Prusinkiewicz, P.: Stochastic p systems and the simulation of biochemical processes with dynamic compartments. BioSystems 91(3), 458–472 (2008)CrossRefGoogle Scholar
  36. 36.
    Spicher, A., Michel, O., Giavitto, J.-L.: Declarative Mesh Subdivision Using Topological Rewriting in MGS. In: Ehrig, H., Rensink, A., Rozenberg, G., Schürr, A. (eds.) ICGT 2010. LNCS, vol. 6372, pp. 298–313. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  37. 37.
    Spicher, A., Michel, O., Giavitto, J.L.: Interaction-Based Simulations for Integrative Spatial Systems Biology. In: Understanding the Dynamics of Biological Systems: Lessons Learned from Integrative Systems Biology. Springer, Heidelberg (2011)Google Scholar
  38. 38.
    Utkin, V.: Variable structure systems with sliding modes. IEEE Transactions on Automatic Control 22(2), 212–222 (1977)MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Varela, F.J.: Principle of Biological Autonomy. McGraw-Hill/Appleton & Lange (1979)Google Scholar
  40. 40.
    Von Neumann, J.: Theory of Self-Reproducing Automata. Univ. of Illinois Press (1966)Google Scholar
  41. 41.
    Weiss, R., Basu, S., Hooshangi, S., Kalmbach, A., Karig, D., Mehreja, R., Netravali, I.: Genetic circuit building blocks for cellular computation, communications, and signal processing. Natural Computing 2(1), 47–84 (2003)CrossRefGoogle Scholar
  42. 42.
    Weiss, R., Knight, T., Sussman, G.: Genetic process engineering. In: Cellular Computing. Series in Systems Biology, pp. 43–73. Orford university Press (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jean-Louis Giavitto
    • 1
  1. 1.Ircam, UMR STMS 9912 Ircam – CNRS – UPMCParisFrance

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