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A Steering Server for Collaborative Simulation of Quantitative Petri Nets

  • Mostafa Herajy
  • Monika Heiner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8489)

Abstract

In this paper we present a Petri net simulation tool called Snoopy Steering and Simulation Server, S 4 for short, which works as a stand-alone extension of Snoopy. The server permits users to share and interactively steer quantitative Petri net models during a running simulation. Moreover, users can collaborate by controlling the execution of a model remotely from different machines (clients). S 4 is shipped with an Application Programming Interface (API) which enables user-defined extensions of the core functionalities. Stochastic, continuous and hybrid Petri nets are supported, both as low-level and coloured ones. S 4 is platform-independent and distributed free of charge for academic use.

Keywords

stochastic continuous hybrid Petri nets computational steering collaborative simulation 

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References

  1. 1.
    Boost website, http://www.boost.org/ (accessed: January 14, 2014)
  2. 2.
    CPN tools website, http://cpntools.org/ (accessed: January 10, 2014)
  3. 3.
    Virtual cell website, http://vcell.org/ (accessed: January 10, 2014)
  4. 4.
    wxWidgets website, http://www.wxwidgets.org/ (accessed: January 15, 2014)
  5. 5.
    Barkai, N., Leibler, S.: Biological rhythms: circadian clocks limited by noise. Nature 403, 267–268 (2000)Google Scholar
  6. 6.
    Gao, Q., Gilbert, D., Heiner, M., Liu, F., Maccagnola, D., Tree, D.: Multiscale Modelling and Analysis of Planar Cell Polarity in the Drosophila Wing. IEEE/ACM Transactions on Computational Biology and Bioinformatics 10(2), 337–351 (2013)CrossRefGoogle Scholar
  7. 7.
    Gilbert, D., Heiner, M.: From Petri nets to differential equations - an integrative approach for biochemical network analysis. In: Donatelli, S., Thiagarajan, P. (eds.) ICATPN 2006. LNCS, vol. 4024, pp. 181–200. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  9. 9.
    Heiner, M., Herajy, M., Liu, F., Rohr, C., Schwarick, M.: Snoopy – A unifying Petri net tool. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 398–407. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Herajy, M.: Computational Steering of Multi-Scale Biochemical Networks. Ph.D. thesis, BTU Cottbus, Dep. of CS (January 2013)Google Scholar
  11. 11.
    Herajy, M., Heiner, M.: Hybrid representation and simulation of stiff biochemical networks. J. Nonlinear Analysis: Hybrid Systems 6(4), 942–959 (2012)MathSciNetMATHGoogle Scholar
  12. 12.
    Herajy, M., Heiner, M.: Snoopy Computational Steering Framework – User Manual Version 1.0. Tech. Rep. 02-13, BTU Cottbus, Dept. of CS (July 2013)Google Scholar
  13. 13.
    Herajy, M., Heiner, M.: Petri net-based collaborative simulation and steering of biochemical reaction networks. Fundamenta Informatica (129), 49–67 (2014)Google Scholar
  14. 14.
    Herajy, M., Schwarick, M., Heiner, M.: Hybrid Petri Nets for Modelling the Eukaryotic Cell Cycle. In: Koutny, M., van der Aalst, W.M.P., Yakovlev, A. (eds.) ToPNoC VIII. LNCS, vol. 8100, pp. 123–141. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Hindmarsh, A., Brown, P., Grant, K., Lee, S., Serban, R., Shumaker, D., Woodward, C.: Sundials: Suite of nonlinear and differential/algebraic equation solvers. ACM Trans. Math. Softw. 31, 363–396 (2005)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Korečko, Š., Marcinčin, J., Slodičák, V.: CPN Assistant II: A tool for management of networked simulations. In: Haddad, S., Pomello, L. (eds.) PETRI NETS 2012. LNCS, vol. 7347, pp. 408–417. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Marwan, W., Rohr, C., Heiner, M.: Petri nets in Snoopy: A unifying framework for the graphical display, computational modelling, and simulation of bacterial regulatory networks. In: Methods in Molec. Biol., vol. 804, ch. 1, pp. 409–437 (2012)Google Scholar
  18. 18.
    Matsuno, H., Nagasaki, M., Miyano, S.: Hybrid Petri net based modeling for biological pathway simulation. Natural Computing 10(3), 1099–1120 (2011)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Nagasaki, M., Saito, A., Jeong, E., Li, C., Kojima, K., Ikeda, E., Miyano, S.: Cell Illustrator 4.0: a Computational Platform for Systems Biology. Silico Biology 10 (2010)Google Scholar
  20. 20.
    Rohr, C., Marwan, W., Heiner, M.: Snoopy - a unifying Petri net framework to investigate biomolecular networks. Bioinformatics 26(7), 974–975 (2010) (Advanced Access published February 7, 2010) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mostafa Herajy
    • 1
  • Monika Heiner
    • 2
  1. 1.Department of Mathematics and Computer Science, Faculty of SciencePort Said UniversityPort SaidEgypt
  2. 2.Computer Science InstituteBrandenburg University of TechnologyCottbusGermany

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