Dynamic Modeling and Simulation of Leukocyte Integrin Activation through an Electronic Design Automation Framework

  • Nicola Bombieri
  • Rosario Distefano
  • Giovanni Scardoni
  • Franco Fummi
  • Carlo Laudanna
  • Rosalba Giugno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8859)


Model development and analysis of biological systems is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulations of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Several characteristics and issues of biological system modeling are common to the electronics system modeling, such as concurrency, reactivity, abstraction levels, as well as state space explosion during verification. This paper proposes a modeling and simulation framework for discrete event-based execution of biochemical systems based on SystemC. SystemC is the reference language in the electronic design automation (EDA) field for modeling and verifying complex systems at different abstraction levels. SystemC-based verification is the de-facto an alternative to model checking when such a formal verification technique cannot deal with the state space complexity of the model. The paper presents how the framework has been applied to model the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues, by handling the solution space complexity through different levels of simulation accuracy.


Biochemical networks Dynamic modeling and simulation SystemC 


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  1. 1.
    Cadence Palladium - System Design and Verification,
  2. 2.
    Mentor Graphics SystemVisio,
  3. 3.
  4. 4.
    SystemC - Accellera Systems Initiative,
  5. 5.
    IEEE 1666 Standard: SystemC Language Reference Manual (2011),
  6. 6.
    Bolomini-Vittori, M., Montresor, A., Giagulli, C., Staunton, D., Rossi, B., Martinello, M., Constantin, G., Laudanna, C.: Regulation of conformer-specific activation of the integrin lfa-1 by a chemokine-triggered rho signaling module. Nat. Immunol. 10, 185–194 (2009)CrossRefGoogle Scholar
  7. 7.
    Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. Wiley, Chichester (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Cai, L., Gajski, D.: Transaction level modeling: An overview. In: ACM/IEEE CODES+ISSS, pp. 19–24 (2003)Google Scholar
  9. 9.
    Cai, L., Gajski, D.: Transaction level modeling: An overview. In: Proceedings of the 1st IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 19–24. CODES+ISSS (2003)Google Scholar
  10. 10.
    Chaouiya, C.: Petri net modelling of biological networks. Briefings in Bioinformatics 8(4), 210–219 (2007)CrossRefGoogle Scholar
  11. 11.
    Constantin, G., Majeed, M., Giagulli, C., Piccio, L., Kim, J., Butcher, E., Laudanna, C.: Chemokines trigger immediate beta2 integrin affinity and mobility changes: differential regulation and roles in lymphocyte arrest under flow. Immunity 13, 759–769 (2000)CrossRefGoogle Scholar
  12. 12.
    Ezudheen, P., Chandran, P., Chandra, J., Simon, B.P., Ravi, D.: Parallelizing SystemC kernel for fast hardware simulation on SMP machines. In: Proc. of ACM/IEEE PADS, pp. 80–87 (2009)Google Scholar
  13. 13.
    Fisher, J., Harel, D., Henzinger, T.A.: Biology as reactivity. Commun. ACM 54(10), 72–82 (2011)CrossRefGoogle Scholar
  14. 14.
    Fisher, J., Henzinger, T.A.: Executable cell biology. Nature Biotechnology 25, 1239–1249 (2007)CrossRefGoogle Scholar
  15. 15.
    Giagulli, C., Ottoboni, L., Caveggion, E., Rossi, B., Lowell, C., Constantin, G., Laudanna, C., Berton, G.: The src family kinases hck and fgr are dispensable for inside-out, chemoattractant-induced signaling regulating beta 2 integrin affinity and valency in neutrophils, but are required for beta 2 integrin-mediated outside-in signaling involved in sustained adhesion. J. Immunol. 177, 604–611 (2006)CrossRefGoogle Scholar
  16. 16.
    Gilbert, D., Fuss, H., Gu, X., Orton, R., Robinson, S., Vyshemirsky, V., Kurth, M.J., Downes, C.S., Dubitzky, W.: Computational methodologies for modelling, analysis and simulation of signalling networks. Briefings in Bioinformatics 7(4), 339–353 (2006)CrossRefGoogle Scholar
  17. 17.
    Kim, M., Carman, C., Yang, W., Salas, A., Springer, T.: The primacy of affinity over clustering in regulation of adhesiveness of the integrin αlβ2. J. Cell. Biol. 167, 1241–1253 (2004)CrossRefGoogle Scholar
  18. 18.
    Ley, K., Laudanna, C., Cybulsky, M., Nourshargh, S.: Getting to the site of inflammation: the leukocyte adhesion cascade updated. Nat. Rev. Immunol. 7, 678–689 (2007)CrossRefGoogle Scholar
  19. 19.
    Melham, T.: Modelling, abstraction, and computation in systems biology: A view from computer science. Progress in Biophysics and Molecular Biology 111, 129–136 (2013)CrossRefGoogle Scholar
  20. 20.
    Montresor, A., Bolomini-Vittori, M., Toffali, L., Rossi, B., Constantin, G., Laudanna, C.: Jak tyrosine kinases promote hierarchical activation of rho and rap modules of integrin activation. J. Cell. Biol. 203(6), 1003–1019 (2013)CrossRefGoogle Scholar
  21. 21.
    Montresor, A., Toffali, L., Constantin, G., Laudanna, C.: Chemokines and the signaling modules regulating integrin affinity. Front Immunol. 3, 127 (2012)CrossRefGoogle Scholar
  22. 22.
    Nanjundappa, M., Patel, H.D., Jose, B.A., Shukla, S.K.: Scgpsim: a fast systemc simulator on gpus. In: Proceedings of the 2010 Asia and South Pacific Design Automation Conference, ASPDAC 2010, pp. 149–154 (2010)Google Scholar
  23. 23.
    Priami, C.: Stochastic pi-calculus. The Computer Journal 38(7), 578–589 (1995)CrossRefGoogle Scholar
  24. 24.
    Sadot, A., Fisher, J., Barak, D., Admanit, Y., Stern, M.J., Hubbard, E.J., Harel, D.: Toward verified biological models. IEEE/ACM Transactions on Computational Biology and Bioinformatics 5(2), 223–234 (2008)CrossRefGoogle Scholar
  25. 25.
    Srihari, S., Raman, V., Leong, H.W., Ragan, M.A.: Evolution and controllability of cancer networks: A boolean perspective. IEEE/ACM Transactions on Computational Biology and Bioinformatics 11(1), 83–94 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nicola Bombieri
    • 1
  • Rosario Distefano
    • 1
  • Giovanni Scardoni
    • 2
  • Franco Fummi
    • 1
  • Carlo Laudanna
    • 2
  • Rosalba Giugno
    • 3
  1. 1.Dept. Computer ScienceUniversity of VeronaItaly
  2. 2.Dept. Patology and DiagnosticsUniversity of VeronaItaly
  3. 3.Dept. Clinical and Molecular BiomedicineUniversity of CataniaItaly

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