transsys: A Generic Formalism for Modelling Regulatory Networks in Morphogenesis

  • Jan T. Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2159)


The formal language transsys is introduced as a tool for comprehensively representing regulatory gene networks in a way that makes them accessible to ALife modelling. As a first application, Linden-mayer systems are enhanced by integration with transsys. The resulting formalism, called L—transsys, is used to implement the ABC model of flower morphogenesis. This transsys ABC model is extensible and allows dynamical modelling on the molecular and on the morphological level.


Regulatory Network Factor Concentration Function Mutant ALife Modelling Promoter Statement 
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]
    Andrew Adamatzky. Simulation of inflorescence growth in cellular automata. Chaos, Solitons & Fractals, 7:1065–1094, 1996.CrossRefGoogle Scholar
  2. [2]
    Gerhard Buck-Sorlin and Konrad Bachmann. Simulating the morphology of barley spike phenotypes using genotype information. Agronomie, 20:691–702, 2000.CrossRefGoogle Scholar
  3. [3]
    Kurt Fleischer and Alan Barr. The multiple mechanisms of morphogenesis: A simulation testbed for the study of multicellular development. In Christopher G. Langton, editor, Artificial Life III, Santa Fe Institute Studies in the Sciences of Complexity, pages 379–416, Redwood City, CA, 1994. Addison Wesley Longman.Google Scholar
  4. [4]
    Christian Fournier and Bruno Andrieu. A 3D architectural and process-based model of maize development. Annals of Botany, 81:233–250, 1998.CrossRefGoogle Scholar
  5. [5]
    Christian Jacob. Evolution programs evolved. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, PPSN-IV, pages 42–51, Berlin, Germany, 1996. Springer Verlag.Google Scholar
  6. [6]
    Jaap A. Kaandorp. Simulation of environmentally induced growth forms in marine sessile organisms. Fractals, 1:375–379, 1993.CrossRefGoogle Scholar
  7. [7]
    Jan T. Kim. LindEvol: Artificial models for natural plant evolution. Künstliche Intelligenz, pages 26–32, 2000.Google Scholar
  8. [8]
    Winfried Kurth. Growth grammar interpreter GROGRA 2.4: A software tool for the 3-dimensional interpretation of stochastic, sensitive growth grammars in the context of plant modelling. introduction and reference manual. Technical report, Universität Göttingen, 1994.
  9. [9]
    Winfried Kurth. Towards universality of growth grammars: Models of Bell, Pagés, and Takenaka revisited. Ann. For. Sci., 57:543–554, 2000.CrossRefGoogle Scholar
  10. [10]
    Koji Kyoda and Hiroaki Kitano. A model of axis determination for the Drosophila wing disc. In Dario Floreano, Jean-Daniel Nicoud, and Francesco Mondada, editors, Advances in Artificial Life, Lecture Notes in Artificial Intelligence, pages 473–476, Berlin Heidelberg, 1999. Springer-Verlag.Google Scholar
  11. [11]
    C.G. Langton, C. Taylor, J.D. Farmer, and S. Rasmussen, editors. Artificial Life II, Redwood City, CA, 1992. Addison-Wesley.Google Scholar
  12. [12]
    H. Meinhardt. Biological pattern formation: New observations provide support for theoretical predictions. BioEssays, 16:627–632, 1994.CrossRefGoogle Scholar
  13. [13]
    Luis Mendoza and Elena R. Alvarez-Buylla. Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. J.theor.Biol., 193:307–319, 1998.CrossRefGoogle Scholar
  14. [14]
    Elliot M. Meyerowitz. The genetics of flower development. Scientific American, 271(5):56–65, 1994.CrossRefGoogle Scholar
  15. [15]
    Karl J. Niklas. Adaptive walks through fitness landscapes for early vascular land plants. American Journal of Botany, 84:16–25, 1997.CrossRefGoogle Scholar
  16. [16]
    S.W. Omholt, E. Plathe, L. Oyehaug, and K.F. Xiang. Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics, 155:969–980, 2000.Google Scholar
  17. [17]
    P. Prusinkiewicz and A. Lindenmayer. The algorithmic beauty of plants. Springer-Verlag, New York, 1990.zbMATHGoogle Scholar
  18. [18]
    Günter Theißen and Heinz Saedler. MADS-box genes in plant ontogeny and phylogeny: Haeckel’s’ biogenetic law’ revisited. Current Opinion in Genetics and Development, 5:628–639, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Jan T. Kim
    • 1
  1. 1.Institut für Neuro- und BioinformatikLübeckGermany

Personalised recommendations