Skip to main content
SpringerLink
Log in

Algorithmische Systembiologie mit Petrinetzen – Von qualitativen zu quantitativen Systemmodellen

  • HAUPTBEITRAG
  • ALGORITHMISCHE SYSTEMBIOLOGIE
  • Published:
Informatik-Spektrum Aims and scope

Zusammenfassung

Die algorithmische Systembiologie ist ein aktuelles Teilgebiet der Bioinformatik. Petrinetze [28] werden seit Jahrzehnten für die Modellierung von Systemen und seit ca. 15 Jahren auch in der netzwerk-orientierten Bioinformatik [17, 27] verwendet. In der algorithmischen Systembiologie werden Petrinetze einerseits zur Repräsentation von biologischem Wissen in Form von qualitativen Netzwerkmodellen und andererseits für die Analyse ihrer dynamischen Eigenschaften und ihre quantitative Simulation eingesetzt. Die Auswahl relevanter Teilnetze aus großen qualitativen Netzen und ihre Umsetzung in quantitative Modelle erfordern neue methodische Ansätze.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Alon U (2006) An Introduction to Systems Biology. Chapman & Hall, New York

    MATH  Google Scholar 

  2. Birzele F, Csaba G, Zimmer R (2008) Alternative splicing and protein structure evolution. Nucleic Acids Res 36(2):550–558

    Article  Google Scholar 

  3. Coloured Petri Net Tools (CPNTools). http://wiki.daimi.au.dk/cpntools, Zugriff 15.03.2009

  4. Csaba G (2007) Syngrep (persönliche Mitteilung/Kontakt: gergely.csaba@bio.ifi.lmu.de)

  5. EC – Enzyme Classification, Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC-IUBMB), http://www.chem.qmul.ac.uk/iubmb/enzyme, Zugriff 25.05.2009

  6. Erhard F (2008) Petri net based system for spatio-temporal simulation of gene regulation. Diplomarbeit, Institut für Informatik, LMU München

  7. Erhard F, Friedel CC, Zimmer R (2008) FERN – a Java framework for stochastic simulation and evaluation of reaction networks. BMC Bioinformatics 9:356

    Article  Google Scholar 

  8. Friedel CC, Krumsiek J, Zimmer R (2008) Bootstrapping the Interactome: Unsupervised Identification of Protein Complexes in Yeast, RECOMB08. LNCS 4955:3–16. Springer, Singapore

    Google Scholar 

  9. Friedel CC, Zimmer R (2006) Toward the complete interactome. Nature Biotechnol 24(6):614–615

    Article  Google Scholar 

  10. Friedel CC, Zimmer R (2008) Identifying the topology of protein complexes from affinity purification assays. German Conf Bioinf 136GI:30–43

    Google Scholar 

  11. Fundel K, Güttler D, Zimmer R, Apostolakis J (2005) A simple approach for protein name identification: prospects and limits. BMC Bioinformatics 6(1):S15

    Article  Google Scholar 

  12. Fundel K, Küffner R, Zimmer R (2007) RelEx-relation extraction using dependency parse trees. Bioinformatics 23(3):365–371

    Article  Google Scholar 

  13. Fundel K, Zimmer R (2006) Gene and protein nomenclature in public databases. BMC Bioinformatics 7:372

    Article  Google Scholar 

  14. Grafahrend-Belau E, Schreiber F, Heiner M, Sackmann A, Junker BH, Grunwald S, Speer A, Winder K, Koch I (2008) Modularization of biochemical networks based on classification of Petri net t-invariants. BMC Bioinformatics 9:90

    Article  Google Scholar 

  15. Hanisch D, Sohler F, Zimmer R (2004) ToPNet – an application for interactive analysis of expression data and biological networks. Bioinformatics 20(9):1470–1471

    Article  Google Scholar 

  16. HGNC – Human Genome Nomenclature Committee der Human Genome Organisation (HUGO), http://www.genenames.org, Zugriff 25.05.2009

  17. Hofestadt R, Thelen S (1998) Quantitative modeling of biochemical networks. In Silico Biol 1(1):39–53

    Google Scholar 

  18. http://www.bio.ifi.lmu.de, Zugriff 15.03.2009

  19. Integrated Net Analyser (INA). http://www2.informatik.hu-berlin.de/starke/ina.html, Zugriff 15.03.2009

  20. Junker BH, Schreiber F (2008) Analysis of Biological Networks. Wiley, New York

    Book  Google Scholar 

  21. Klein D, Manning C (2003) Fast Exact Inference with a Factored Model for Natural Language Parsing. In: Advances in Neural Information Processing Systems 15 (NIPS 2002), MIT Press, Cambridge. http://nlp.stanford.edu/software/lex-parser.shtml, Zugriff 15.03.2009

  22. Küffner R, Fundel K, Zimmer R (2005) Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts. Bioinformatics 21(Suppl 2):ii259–ii267

    Google Scholar 

  23. Matsuno H, Li C, Miyano S (2006) Petri net based descriptions for systematic understanding of biological pathways. IEICE T Fund Electr E89-A(11):3166–3174

    Article  Google Scholar 

  24. Ng A, Bursteinas B, Gao Q, Mollison E, Zvelebil M (2006) Resources for integrative systems biology: from data through databases to networks and dynamic system models. Brief Bioinform 7(4):318–330

    Article  Google Scholar 

  25. Petrinetz Editor Snoopy. http://www-dssz.informatik.tu-cottbus.de, Zugriff 15.03.2009

  26. PubMed. http://www.ncbi.nlm.nih.gov/pubmed/, Zugriff 15.03.2009

  27. Reddy VN, Mavrovouniotis ML, Liebman MN (1993) Petri net representations in metabolic pathways. Proc Int Conf Intell Syst Mol Biol 1:328–36

    Google Scholar 

  28. Reisig W (1982) Petrinetze – Eine Einführung. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  29. Sackmann A, Heiner M, Koch I (2006) Application of Petri net based analysis techniques to signal transduction pathways. BMC Bioinformatics 7:482

    Article  Google Scholar 

  30. Sohler F, Hanisch D, Zimmer R (2004) New methods for joint analysis of biological networks and expression data. Bioinformatics 20(10):1517–1521

    Article  Google Scholar 

  31. Szallasi Z, Stelling J, Periwal V (2006) System Modeling in Cell Biology. MIT Press, Cambridge, MA

    Google Scholar 

  32. Windhager L, Zimmer R (2008) Intuitive modeling of dynamic systems with Petri nets and fuzzy logic. German Conf Bioinf 136GI:106–115

    Google Scholar 

  33. Zadeh L (1965) Fuzzy set theory. Inf Control 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lehrstuhl Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München, Amalienstr. 17, 80333, München, Deutschland

    Fabian Birzele, Gergely Csaba, Florian Erhard, Caroline Friedel, Robert Küffner, Tobias Petri, Lukas Windhager & Ralf Zimmer

Authors
  1. Fabian Birzele
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gergely Csaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Florian Erhard
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caroline Friedel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert Küffner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tobias Petri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lukas Windhager
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ralf Zimmer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ralf Zimmer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Birzele, F., Csaba, G., Erhard, F. et al. Algorithmische Systembiologie mit Petrinetzen – Von qualitativen zu quantitativen Systemmodellen. Informatik Spektrum 32, 310–319 (2009). https://doi.org/10.1007/s00287-009-0355-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00287-009-0355-4

Search

Navigation