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An Analysis of Correlative and Static Causality in P Systems

  • Roberto Pagliarini
  • Oana Agrigoroaiei
  • Gabriel Ciobanu
  • Vincenzo Manca
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7762)

Abstract

In this paper we present two approaches, namely correlative and static causality, to study cause-effect relationships in reaction models and we propose a framework which integrates them in order to study causality by means of transition P systems. The proposed framework is based on the fact that statistical analysis can be used to building up a membrane model which can be used to analyze causality relationships in terms of multisets of objects and rules in presence of non-determinism and parallelism. We prove that the P system which is defined by means of correlation analysis provides a correspondence between the static and correlative notions of causality.

Keywords

Membrane System Static Causality Cascade Model Cdc2 Kinase Membrane Computing 
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.

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References

  1. 1.
    Agrigoroaiei, O., Ciobanu, G.: Rule-based and Object-based Event Structures for Membrane Systems. Journal of Logic and Algebraic Programming 79(6), 295–303 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Agrigoroaiei, O., Ciobanu, G.: Quantitative Causality in Membrane Systems. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (eds.) CMC 2011. LNCS, vol. 7184, pp. 62–72. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Busi, N.: Causality in Membrane Systems. In: Eleftherakis, G., Kefalas, P., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2007. LNCS, vol. 4860, pp. 160–171. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Castellini, A., Franco, G., Pagliarini, R.: Data analysis pipeline from laboratory to MP models. Natural Computing 10(1), 55–76 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ciobanu, G., Lucanu, D.: Events, Causality, and Concurrency in Membrane Systems. In: Eleftherakis, G., Kefalas, P., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2007. LNCS, vol. 4860, pp. 209–227. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Fisher, R.A.: On the Mathematical Foundations of Theoretical Statistics. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 222, 309–368 (1922)zbMATHCrossRefGoogle Scholar
  7. 7.
    Fuente, A., Bing, N., Hoeschele, I., Mendes, P.: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18), 3565–3574 (2004)CrossRefGoogle Scholar
  8. 8.
    Goldbeter, A.: A Minimal cascade model for the mitotic oscillator involving cyclin and cdc2 Kinase. PNAS 88(20), 9107–9111 (1991)CrossRefGoogle Scholar
  9. 9.
    Hynne, F., Danø, S., Sørensen, P.G.: Full-scale model of glycolysis in saccharomyces cerevisiae. Biophysical Chemistry 94(1-2), 121–163 (2001)CrossRefGoogle Scholar
  10. 10.
    Junker, B.H., Schreiber, F.: Analysis of Biological Networks (Wiley Series in Bioinformatics). Wiley-Interscience (2008)Google Scholar
  11. 11.
    Kleijn, J.H.C.M., Koutny, M., Rozenberg, G.: Towards a Petri Net Semantics for Membrane Systems. In: Freund, R., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2005. LNCS, vol. 3850, pp. 292–309. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  12. 12.
    Manca, V.: Fundamentals of Metabolic P Systems. In: Păun, G., Rozenberg, G., Salomaa, A. (eds.) Handbook of Membrane Computing. Oxford University Press (2009)Google Scholar
  13. 13.
    Muirhead, R.J.: Aspects of Multivariate Statistical Theory. Wiley-Interscience (2005)Google Scholar
  14. 14.
    Pagliarini, R.: Modelling and Reverse-Engineering of Biological Phenomena by means of Metabolic P Systems. PhD thesis, University of Verona, Italy (2011)Google Scholar
  15. 15.
    Păun, G.: Membrane Computing. An Introduction. Springer, Berlin (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Pyle, D.: Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann (1999)Google Scholar
  17. 17.
    Steuer, R., Kurths, J., Fiehn, O., Weckwerth, W.: Interpreting correlations in metabolomic networks. Biochem. Soc. Trans. 31(Pt. 6), 1476–1478 (2003)CrossRefGoogle Scholar
  18. 18.
    Steuer, R., Kurths, J., Fiehn, O., Weckwerth, W.: Observing and interpreting correlations in metabolomic networks. Bioinformatics 19(8), 1019–1026 (2003)CrossRefGoogle Scholar
  19. 19.
    Theobald, U., Mailinger, W., Baltes, M., Rizzi, M., Reuss, M.: In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnology and Bioengineering 55(2), 305–316 (1997)CrossRefGoogle Scholar
  20. 20.
    Vilela, M., Danuser, G.: What’s wrong with correlative experiments? Nature Cell Biology 13(9), 1011 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Roberto Pagliarini
    • 1
  • Oana Agrigoroaiei
    • 2
  • Gabriel Ciobanu
    • 2
  • Vincenzo Manca
    • 3
  1. 1.Telethon Institute of Genetics and MedicineNaplesItaly
  2. 2.Institute of Computer ScienceRomanian AcademyRomania
  3. 3.Computer Science Dept.Verona UniversityVeronaItaly

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