Skip to main content
SpringerLink
Log in

Modeling p53 Signaling Pathways by Using Multiset Processing

  • Chapter
Applications of Membrane Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

We present an approach to modeling and simulating the protein p53 signaling pathways by means of a particular class of P systems, called ARMS (Abstract Rewriting Systems on Multisets). The results of the computer simulations are presented; they agree with the biological observations.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. Aris: Prolegomena to the Rational Analysis of Systems of Chemical Reactions. Arch. Rational Mech. Anal., 19,1 (1965), 81–99.

    Article  MathSciNet  Google Scholar 

  2. R.V. Baror, R. Maya, L.A. Segel, U. Alon, A.J. Levine, M. Oren: Generation of Oscillations by the p53-MDM2 Feedback Loop: A Theoretical and Experimental Study. PNAS, 97,21 (2000), 11250–11255.

    Article  Google Scholar 

  3. B.A. Barshop, R.F. Wrenn, C. Frieden: Analysis of Numerical Methods for Computer Simulation of Kinetic Processes: Development of KINSIM — A Flexible, Portable System. Anal. Biochem., 130 (1983), 134–145.

    Article  Google Scholar 

  4. P. Dittrich, J. Ziegler, W. Banzhaf: Artificial Chemistries — A Review. Artificial Life, 7,3 (2001), 225–275.

    Article  Google Scholar 

  5. M. Ehlde, G. Zacchi: MIST: A User-Friendly Metabolic Simulator. Comput. Applic. Biosci., 11 (1995), 201–207.

    Google Scholar 

  6. C.W. Gardiner: Handbook of Stochastic Methods, 2nd ed., Springer, Berlin, 1985.

    Google Scholar 

  7. M.S. Herbert, B. Ingalls: Conservation Analysis in Biochemical Networks: Computational Issues for Software Writers. Keck Graduate Institute, 2003.

    Google Scholar 

  8. M. Hollstein, K. Rice, M.S. Greenblatt, T. Soussi, R. Fuchs, T. Sorlie, E. Hovig, B. Smith-Sorensen, R. Montesano, C.C. Harris: Database of p53 Gene Somatic Mutations in Human Tumors and Cell Lines. Nucleic Acids Research, 17 (1994), 3551–3555.

    Google Scholar 

  9. T.R. Hupp: Regulation of p53 Protein Function Through Alterations in Protein-Folding Pathways. Cell Mollecular Life Science, 55,1 (1999), 88–95.

    Article  Google Scholar 

  10. N.G. van Kampen: Stochastic Processes in Physics and Chemistry. North-Holland, 1981.

    Google Scholar 

  11. D. Lane: Awakening Angels. Nature, 394 (1998), 616–617.

    Article  Google Scholar 

  12. A.J. Levine: p53, the Cellular Gatekeeper for Growth and Division. Cell, 88,3 (1997), 323–331.

    Article  Google Scholar 

  13. L.D. Mayo, D.B. Donner: The PTEN, MDM2, p53 Tumor Suppressor-Oncoprotein Network. Trends in Biochemical Science, 27,9 (2002), 462–467.

    Article  Google Scholar 

  14. P. Mendes: Biochemistry by Numbers: Simulation of Biochemical Pathways with Gepasi 3. Trends in Biochemical Science, 22,9 (1997), 361–363.

    Article  Google Scholar 

  15. Gh. Păun: Computing with Membranes. Journal of Computer and System Sciences, 61,1 (2000), 108–143, and Turku Center for Computer Science, TUCS Report No 208, 1998 (www.tucs.fi).

    Article  MATH  MathSciNet  Google Scholar 

  16. C. Prives, P.A. Hall: The p53 Pathway. Journal of Patholgy, 187,1 (1999), 112–126.

    Article  Google Scholar 

  17. J.G. Reich, E.F. Selkov: Energy Metabolism the Cell. Academic Press, 1981.

    Google Scholar 

  18. Y. Suzuki, P. Davis, H. Tanaka: Emergence of Auto-Catalytic Structure in Stochastic Self-Reinforcing Reaction Networks. J. of Artificial Life and Robotics, 7 (2004).

    Google Scholar 

  19. Y. Suzuki, Y. Fujiwara, H. Tanaka, J. Takabayashi: Artificial Life Applications of a Class of P Systems: Abstract Rewriting Systems on Multisets. In Multiset Processing. Mathematical, Computer Science, and Molecular Computing Points of View (C.S. Calude, Gh. Păun, G. Rozenberg, A. Salomaa, eds.), LNCS 2235, Springer, Berlin, 2001, 299–346.

    Google Scholar 

  20. Y. Suzuki, J. Takabayashi, H. Tanaka: Investigation of Tritrophic System in Ecological Systems by Using an Artificial Chemistry. J. of Artificial Life and Robotics, 6 (2002), 129–132.

    Article  Google Scholar 

  21. Y. Suzuki, H. Tanaka: A Symbolic Chemical System Based on an Abstract Rewriting System and Its Behavior Pattern. J. of Artificial Life and Robotics, 1 (1997), 211–219.

    Article  Google Scholar 

  22. Y. Suzuki, H. Tanaka: Chemical Evolution Among Artificial Proto-Cells. Artificial Life VII, MIT Press, 2000, 54–64.

    Google Scholar 

  23. Y. Suzuki, S. Tsumoto, H. Tanaka: Analysis of Cycles in Symbolic Chemical Systems Based on Abstract Rewriting Systems on Multisets. Artificial Life V, MIT Press, 1996, 522–528.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bioinformatics, Medical Research Institute, Tokyo Medical and Dental University, Yushima 1-5-45, Bunkyo, Tokyo, 113, Japan

    Yasuhiro Suzuki & Hiroshi Tanaka

Authors
  1. Yasuhiro Suzuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroshi Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Computer Science of the Romanian Academy, Blvd. Copou 8, 700506, IaÅŸi, Romania

    Gabriel Ciobanu

  2. Research Group on Natural Computing Department of Computer Science and Artificial Intelligence, University of Seville, Avda. Reina Mercedes s/n, 41012, Seville, Spain

    Gheorghe Păun  & Mario J. Pérez-Jiménez  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Suzuki, Y., Tanaka, H. (2006). Modeling p53 Signaling Pathways by Using Multiset Processing. In: Ciobanu, G., Păun, G., Pérez-Jiménez, M.J. (eds) Applications of Membrane Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-29937-8_7

Download citation

  • DOI: https://doi.org/10.1007/3-540-29937-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25017-3

  • Online ISBN: 978-3-540-29937-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation