Skip to main content
SpringerLink
Log in
Book cover

Systems Biology pp 361–393Cite as

Chemical Organisation Theory

  • Chapter

Part of the book series: Cell Engineering ((CEEN,volume 5))

Abstract

Complex dynamical reaction networks consisting of many molecular species are difficult to understand, especially, when new species may appear and present species may vanish completely. This chapter outlines a technique to deal with such systems. The first part introduces the concept of a chemical organisation as a closed and self-maintaining set of molecular species. This concept allows to map a complex (reaction) network to its set of organisations, providing a new view on the system’s structure. The second part connects dynamics with the set of organisations, which allows to map a movement of the system in state space to a movement in the set of organisations. The relevancy of this approach is underlined by a theorem that says that given a differential equation describing the chemical dynamics of the network, then every stationary state is an instance of an organisation. Finally, the relation between pathways and chemical organisations is sketched

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. L. M. Adleman. 1994 Molecular computation of solutions to combinatorial problems. Science, 266(5187):1021–1024.

    Article  PubMed  CAS  Google Scholar 

  2. J.-P. Banâtre and D. L. Métayer. 1990 The GAMMA model and its discipline of programming. Sci. Comput. Program., 15(1):55–77.

    Article  Google Scholar 

  3. W. Banzhaf. 1993 Self-replicating sequences of binary numbers – foundations I and II: General and strings of length n = 4. Biol. Cybern., 69:269–281.

    Article  Google Scholar 

  4. W. Banzhaf, P. Dittrich, and H. Rauhe. 1996 Emergent computation by catalytic reactions. Nanotechnology, 7(1):307–314.

    Article  CAS  Google Scholar 

  5. G. Benkö, C. Flamm, and P. F. Stadler. 2002 A graph-based toy model of chemistry. J. Chem. Inf. Comput. Sci., 43(4):1085–1093.

    Article  CAS  Google Scholar 

  6. N. Borisov, N. Markevich, H. J.B., and K. B.N. 2005 Signaling through receptors and scaffolds: independent interactions reduce combinatorial complexity. Biophys. J, 89(2):951–966.

    Article  PubMed  CAS  Google Scholar 

  7. F. Centler, P. S. di Fenizio, N. Matsumaru, and P. Dittrich. Chemical organizations in the central sugar metabolism of escherichia coli. In Modeling and Simulation in Science Engineering and Technology, Post-proceedings of ECMTB 2005, pages 1–8. Birkhäuser, 2006. (in print).

    Google Scholar 

  8. F. Centler and P. Dittrich. Chemical organizations in atmospheric photochemistries: a new method to analyze chemical reaction networks. Submitted.

    Google Scholar 

  9. M. E. Csete and J. C. Doyle. 2002 Reverse engineering of biological complexity. Science, 295:1664–1669.

    Article  PubMed  CAS  Google Scholar 

  10. P. Dittrich and P. S. di Fenizio. Chemical organization theory. Bull. Math. Biol., 2006. In print.

    Google Scholar 

  11. P. Dittrich, T. Kron, and W. Banzhaf. 2003 On the formation of social order – modeling the problem of double and multi contingency following luhmann. Journal of Artifical Societies and Social Simulation, 6(1).

    Google Scholar 

  12. P. Dittrich, J. Ziegler, and W. Banzhaf. 2001 Artificial chemistries – a review. Artificial Life, 7(3):225–275.

    Article  PubMed  CAS  Google Scholar 

  13. O. Ebenhöh, T. Handorf, and R. Heinrich. 2004 Structural analysis of expanding metabolic network. Genome Informatics, 15(1):35–45.

    PubMed  Google Scholar 

  14. M. Eigen. 1971 Selforganization of matter and the evolution of biological macromolecules. Naturwissenschaften, 58(10):465–523.

    Article  PubMed  CAS  Google Scholar 

  15. M. Eigen and P. Schuster. 1977 The hypercycle: a principle of natural self-organisation, part A. Naturwissenschaften, 64(11):541–565.

    Article  PubMed  CAS  Google Scholar 

  16. P. Érdi and J. Tóth. 1989 Mathematical Models of Chemical Reactions: Theory and Applications of Deterministic and Stochastic Models. Pinceton University Press, Princeton, NJ.

    Google Scholar 

  17. C. Espinosa-Soto, P. Padilla-Longoria, and E. R. Alvarez-Buylla. 2004 A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell, 16(11):2923–2939.

    Article  PubMed  CAS  Google Scholar 

  18. M. Feinberg and F. J. M. Horn. 1973 Dynamics of open chemical systems and the algebraic structure of the underlying reaction network. Chem. Eng. Sci., 29(3):775–787.

    Article  Google Scholar 

  19. W. Fontana. Algorithmic chemistry. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, pages 159–210, Redwood City, CA, 1992. Addison-Wesley.

    Google Scholar 

  20. W. Fontana and L. W. Buss 1994 ’The arrival of the fittest’: Toward a theory of biological organization. Bull. Math. Biol., 56(1):1–64.

    Google Scholar 

  21. K. V. Gothelf and R. S. Brown. Feb 2005 A modular approach to DNA-programmed self-assembly of macromolecular nanostructures. Chemistry, 11(4):1062–1069.

    Article  CAS  Google Scholar 

  22. C. Heij, A. e. C. M. Ran, and F. v. Schagen. 2006 Introduction to Mathematical Systems Theory Linear Systems, Identification and Control. Birkhäuser.

    Google Scholar 

  23. R. Heinrich and S. Schuster. 1996 The Regulation of Cellular Systems. Chapman and Hall, New York, NY.

    Google Scholar 

  24. S. Jain and S. Krishna. 1998 Autocatalytic sets and the growth of complexity in an evolutionary model. Phys. Rev. Lett., 81(25):5684–5687.

    Article  CAS  Google Scholar 

  25. S. Jain and S. Krishna. 2001 A model for the emergence of cooperation, interdependence, and structure in evolving networks. Proc. Natl. Acad. Sci. U. S. A., 98(2):543–547.

    Article  PubMed  CAS  Google Scholar 

  26. C. Kaleta, F. Centler, and P. Dittrich. Analyzing molecular reaction:networks: from pathways to chemical organizations. Molecular Biotechnology, 2006. In print.

    Google Scholar 

  27. S. A. Kauffman. 1971 Cellular homeostasis, epigenesis and replication in randomly aggregated macromolecular systems. J. Cybernetics, 1:71–96.

    Google Scholar 

  28. N. Matsumaru, F. Centler, and P. D. Klaus-Peter Zauner. Self-adaptive scouting – autonomous experimentation for systems biology. In G. R. Raidl, S. Cagnoni, J. Branke, D. Corne, R. Drechsler, Y. Jin, C. G. Johnson, P. Machado, E. Marchiori, F. Rothlauf, G. D. Smith, and G. Squillero, editors, 2004 Applications of Evolutionary Computing, EvoWorkshops 2004, volume 3005 of LNAI, pages 52–62. Springer, Berlin.

    Google Scholar 

  29. N. Matsumaru, F. Centler, P. Speroni di Fenizio, and P. Dittrich. 2006 Chemical organization theory applied to virus dynamics. – Information Technology, 48(3). In print.

    Google Scholar 

  30. J. A. Papin, J. Stelling, N. D. Price, S. Klamt, S. Schuster, and B. O. Palsson. Aug 2004 Comparison of network-based pathway analysis methods. Trends Biotechnol, 22(8):400–405.

    Article  CAS  Google Scholar 

  31. C. A. Petri. 1962 Kommunikation mit Automaten. PhD thesis, University of Bonn, Bonn.

    Google Scholar 

  32. R. Pinkas-Kramarski, I. Alroy, and Y. Yarden. 1997 Erbb receptors and egf-like ligands: cell lineage determination and oncogenesis through combinatorial signaling. J. Mammary. Gland. Biol. Neoplasia, 2(2):97–107.

    Article  PubMed  CAS  Google Scholar 

  33. J. Puchalka and A. Kierzek. 2004 Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks. Biophys. J., 86(3):1357–1372.

    Article  PubMed  CAS  Google Scholar 

  34. O. E. Rössler. 1971 A system theoretic model for biogenesis (in German). Z. Naturforsch. B, 26(8):741–746.

    PubMed  Google Scholar 

  35. P. Schuster and K. Sigmund. 1983 Replicator dynamics. J. Theor. Biol., 100:533–8.

    Article  Google Scholar 

  36. D. Segré, D. Lancet, O. Kedem, and Y. Pilpel 1998 Graded autocatalysis replication domain (GARD): Kinetic analysis of self-replication in mutually catalytic sets. Orig. Life Evol. Biosph., 28(4–6):501–514.

    Article  Google Scholar 

  37. P. Speroni di Fenizio. A less abstract artficial chemistry. In M. A. Bedau, J. S. McCaskill, N. H. Packard, and S. Rasmussen, editors, Artificial Life VII, pages 49–53, Cambridge, MA, 2000. MIT Press.

    Google Scholar 

  38. P. Speroni Di Fenizio and P. Dittrich. 2002 Artificial chemistry’s global dynamics. movement in the lattice of organisation. The Journal of Three Dimensional Images, 16(4):160–163.

    Google Scholar 

  39. P. Speroni di Fenizio, P. Dittrich, J. Ziegler, and W. Banzhaf. Towards a theory of organizations. In German Workshop on Artificial Life, Bayreuth, 5.-7. April, 2000. In print, available online: di.ttri.ch/p/SDZB2001gwal.html.

    Google Scholar 

  40. P. F. Stadler, W. Fontana, and J. H. Miller. 1993 Random catalytic reaction networks. Physica D, 63:378–392.

    Article  Google Scholar 

  41. D. Wodarz and M. A. Nowak. 1999 Specific therapy regimes could lead to long-term immunological control of hiv. Proc. Nat. Acad. Sci. USA, 96(25):14464–9.

    Article  PubMed  CAS  Google Scholar 

  42. Y. L. Yung and W. B. DeMore. 1999 Photochemistry of Planetary Athmospheres. Oxford University Press, New York.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bio Systems Analysis Group, Jena Centre for Bioinformatics and Department of Mathematics and Computer Science, Friedrich Schiller University Jena, D-07743 Jena, Germany

    Peter Dittrich & Pietro Speroni Di Fenizio

Authors
  1. Peter Dittrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pietro Speroni Di Fenizio
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University College Dublin, Ireland

    Mohamed Al-Rubeai

  2. Swiss Federal Institute of Technology, ETH Hoenggeerberg HCI Institute of Chemical and Bioengineering ICB, Zurich, Switzerland

    Martin Fussenegger

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer

About this chapter

Cite this chapter

Dittrich, P., Speroni Di Fenizio, P. (2007). Chemical Organisation Theory. In: Al-Rubeai, M., Fussenegger, M. (eds) Systems Biology. Cell Engineering, vol 5. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5252-9_11

Download citation

Publish with us

Policies and ethics

Search

Navigation