In Silico Modeling and Simulation Approach for Apoptosis Caspase Pathways

  • Pedro Pablo González-PérezEmail author
  • Maura Cárdenas-García
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 803)


We revisit and improve in silico modeling and simulation approach of the apoptosis caspases pathways, initially developed for exploring and discovering the complex interaction patterns of apoptotic caspases and the mitochondrial role. Symbolic abstractions and algorithms of the in silico model were improved to allow dealing with crucial aspects of the cellular signal transduction such as cellular processes. Also, the particular model of extrinsic and intrinsic apoptotic signaling pathways was improved, increasing the number of reactions and using all kinetic parameters accurately calculated. Using the computational simulation tool BTSSOC-Cellulat, we were able to determine experimentally how the modulation of concentrations of proteins XIAP, cFLIPs and TRAIL/FASL, can cause the death of cancerous cells. Our results show how crucial were the improvements made in the in silico modeling approach, which in turn were reflected in the accuracy of the simulation and, therefore, in the significant value of the in silico experiments carried out.


Caspase signaling pathway Apoptosis In silico modeling and simulation approach In silico experiments 


  1. 1.
    Kerr, J.F.T., Wyllie, A.H., Currie, A.R.: Apoptosis: a basic biological phenomenon with wide-ranging implication in tissue kinetics. Br. J. Cancer 26(4), 239–257 (1972)CrossRefGoogle Scholar
  2. 2.
    Wyllie, A.H.: Apoptosis: an overview. Br. Med. Bull. 53(3), 451–465 (1997)CrossRefGoogle Scholar
  3. 3.
    Poreba, M., Szalek, A., Kasperkiewicz, P., Rut, W., Salvesen, G.S., Drag, M.: Small molecule active site directed tools for studying human caspases. Chem. Rev. 115(22), 12546–12629 (2015). Scholar
  4. 4.
    Danial, N.N., Korsmeyer, S.J.: Cell death: critical control points. Cell 116(2), 205–219 (2004). Scholar
  5. 5.
    Poreba, M., Strózyk, A., Salvesen, G.S., Drag, M.: Caspase substrates and inhibitors. Cold Spring Harb. Perspect. Biol. 5(8), a008680 (2013). Scholar
  6. 6.
    Songane, M., Khair, M., Saleh, M.: An update view on the function of caspases in inflammation and immunity. Semin. Cell Dev. Biol. (2018).
  7. 7.
    Alves, R., Antunes, F., Salvador, A.: Tools for kinetic modeling of biochemical networks. Nat. Biotechnol. 24(6), 667–672 (2006). Scholar
  8. 8.
    Ciocchetta, F., Duguid, A., Guerriero, M.L.: A compartmental model of the cAMP/PKA/MAPK pathway in bio-PEPA. In: Third Workshop on Membrane Computing and Biologically Inspired Process Calculi (MeCBIC) (2009).
  9. 9.
    Kerr, R.A., Bartol, T.M., Kaminsky, B., Dittrich, M., Chang, J.C., Baden, S.B., Sejnowski, T.J., Stiles, J.R.: Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. SIAM J. Sci. Comput. 30(36), 3126–3149 (2008). Scholar
  10. 10.
    Hoops, S., et al.: COPASI: a complex pathway simulator. Bio-informatics 22(24), 3067–3074 (2006). Scholar
  11. 11.
    Cowan, A.E., Moraru, I.I., Schaff, J.C., Slepchenko, B.M., Loew, L.M.: Spatial modeling of cell signaling networks. Methods Cell Biol. 110, 195–221 (2012). Scholar
  12. 12.
    Swat, M., Thomas, G.L., Belmonte, J.M., Shirinifard, A., Hmeljak, D., Glazier, J.A.: Multi-scale modeling of tissues using CompuCell 3D. Methods Cell Biol. 110, 325–366 (2012). Scholar
  13. 13.
    Martinou, J.C., Youle, R.J.: Mitochondria in apoptosis; Bcl-2 family members and mitochondrial dynamics. Dev. Cell 21(1), 92–101 (2011). Scholar
  14. 14.
    González-Pérez, P.P., Omicini, A., Sbaraglia, M.: A biochemically inspired coordination-based model for simulating intracellular signalling pathway. J. Simul. 27(3), 216–226 (2013). Scholar
  15. 15.
    Cárdenas-García, M., González-Pérez, P.P., Montagna, S., Cortés Sánchez, O., Caballero, E.H.: Modeling intercellular communication as a survival strategy of cancer cells: an in silico approach on a flexible bioinformatics framework. Bioinf. Biol. Insights 10, 5–18 (2016). Scholar
  16. 16.
    Gelernter, D.: Generative communication in Linda. ACM Trans. Program. Lang. Syst. 7(1), 80–112 (1985). Scholar
  17. 17.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977). Scholar
  18. 18.
    Cárdenas-García, M., González-Pérez, P.P., Montagna, S.: bioinformatics. EMBnet.journal 18(S18.B), 94–96 (2012). Special Issue NETTAB 2012 Workshop on “Integrated Bio-Search”.
  19. 19.
    Cárdenas-García, M., González-Pérez, P.P.: Applying the tuple space-based approach to the simulation of the caspases, an essential signalling pathway. J. Integr. Bioinf. 10(1), 225. ISSN 1613-4516
  20. 20.
    Kang, W., et al.: Structural and biochemical basis for the inhibition of cell death by APIP, a methionine salvage enzyme. Proc. Nat. Acad. Sci. 111(1), E54–E61 (2014). Scholar
  21. 21.
    Karki, P., Lee, J., Shin, S.Y., Cho, B., Park, I.S.: Kinetic comparison of procaspase-3 and caspase-3. Arch. Biochem. Biophys. 442(1), 125–132 (2005). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pedro Pablo González-Pérez
    • 1
    Email author
  • Maura Cárdenas-García
    • 2
  1. 1.Universidad Autónoma MetropolitanaCiudad de MéxicoMexico
  2. 2.Benemérita Universidad Autónoma de PueblaPueblaMexico

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