Computational Modeling and Verification of Signaling Pathways in Cancer

  • Haijun Gong
  • Paolo Zuliani
  • Anvesh Komuravelli
  • James R. Faeder
  • Edmund M. Clarke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6479)

Abstract

We propose and analyze a rule-based model of the HMGB1 signaling pathway. The protein HMGB1 can activate a number of regulatory networks – the p53, NFκB, Ras and Rb pathways – that control many physiological processes of the cell. HMGB1 has been recently shown to be implicated in cancer, inflammation and other diseases. In this paper, we focus on the NFκB pathway and construct a crosstalk model of the HMGB1-p53-NFκB-Ras-Rb network to investigate how these couplings influence proliferation and apoptosis (programmed cell death) of cancer cells. We first built a single-cell model of the HMGB1 network using the rule-based BioNetGen language. Then, we analyzed and verified qualitative properties of the model by means of simulation and statistical model checking. For model simulation, we used both ordinary differential equations and Gillespie’s stochastic simulation algorithm. Statistical model checking enabled us to verify our model with respect to behavioral properties expressed in temporal logic. Our analysis showed that HMGB1-activated receptors can generate sustained oscillations of irregular amplitude for the NFκB, IκB, A20 and p53 proteins. Also, knockout of A20 can destroy the IκB-NFκB negative feedback loop, leading to the development of severe inflammation or cancer. Our model also predicted that the knockout or overexpression of the IκB kinase can influence the cancer cell’s fate – apoptosis or survival – through the crosstalk of different pathways. Finally, our work shows that computational modeling and statistical model checking can be effectively combined in the study of biological signaling pathways.

Keywords

Model Checking cancer HMGB1 verification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
    Bardeesy, N., DePinho, R.A.: Pancreatic cancer biology and genetics. Nature Reviews Cancer 2(12), 897–909 (2002)CrossRefGoogle Scholar
  4. 4.
    Brezniceanu, M.L., Volp, K., Bosser, S., Solbach, C., Lichter, P., et al.: HMGB1 inhibits cell death in yeast and mammalian cells and is abundantly expressed in human breast carcinoma. FASEB Journal 17, 1295–1297 (2003)Google Scholar
  5. 5.
    Cascinu, S., Scartozzi, M., et al.: COX-2 and NF-kB overexpression is common in pancreatic cancer but does not predict for COX-2 inhibitors activity in combination with gemcitabine and oxaliplatin. American Journal of Clinical Oncology 30(5), 526–530 (2007)CrossRefGoogle Scholar
  6. 6.
    Ciliberto, A., Novak, B., Tyson, J.: Steady states and oscillations in the p53/Mdm2 network. Cell Cycle 4(3), 488–493 (2005)CrossRefGoogle Scholar
  7. 7.
    Clarke, E.M., Emerson, E.A., Sifakis, J.: Model checking: algorithmic verification and debugging. Commun. ACM 52(11), 74–84 (2009)CrossRefGoogle Scholar
  8. 8.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press (1999)Google Scholar
  9. 9.
    Downward, J.: Targeting RAS signalling pathways in cancer therapy. Nature Reviews Cancer 3, 11–22 (2003)CrossRefGoogle Scholar
  10. 10.
    Dumitriu, I.E., Baruah, P., Valentinis, B., et al.: Release of high mobility group box 1 by dendritic cells controls T cell activation via the receptor for advanced glycation end products. The Journal of Immunology 174, 7506–7515 (2005)CrossRefGoogle Scholar
  11. 11.
    Eddy, S.F., Guo, S., et al.: Inducible IkB kinase/IkB kinase expression is induced by CK2 and promotes aberrant Nuclear Factor-kB activation in breast cancer cells. Cancer Research 65, 11375–11383 (2005)CrossRefGoogle Scholar
  12. 12.
    Ellerman, J.E., Brown, C.K., de Vera, M., Zeh, H.J., Billiar, T., et al.: Masquerader: high mobility group box-1 and cancer. Clinical Cancer Research 13, 2836–2848 (2007)CrossRefGoogle Scholar
  13. 13.
    Faeder, J.R., Blinov, M.L., Hlavacek, W.S.: Rule-based modeling of biochemical systems with BioNetGen. Methods in Molecular Biology 500, 113–167 (2009)CrossRefGoogle Scholar
  14. 14.
    Geva-Zatorsky, N., Rosenfeld, N., Itzkovitz, S., Milo, R., Sigal, A., Dekel, E., Yarnitzky, T., Liron, Y., Polak, P., Lahav, G., Alon, U.: Oscillations and variability in the p53 system. Molecular Systems Biology, 2:2006.0033 (2006)Google Scholar
  15. 15.
    Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics 22(4), 403–434 (1976)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Gong, H., Guo, Y., Linstedt, A., Schwartz, R.: Discrete, continuous, and stochastic models of protein sorting in the Golgi apparatus. Physical Review E 81(1), 011914 (2010)CrossRefGoogle Scholar
  17. 17.
    Gong, H., Sengupta, H., Linstedt, A., Schwartz, R.: Simulated de novo assembly of Golgi compartments by selective cargo capture during vesicle budding and targeted vesicle fusion. Biophysical Journal 95, 1674–1688 (2008)CrossRefGoogle Scholar
  18. 18.
    Gong, H., Zuliani, P., Komuravelli, A., Faeder, J.R., Clarke, E.M.: Analysis and verification of the HMGB1 signaling pathway. BMC Bioinformatics (2010) (to appear)Google Scholar
  19. 19.
    Hanahan, D., Weinberg, R.A.: The hallmarks of cancer. Cell 100(1), 57–70 (2000)CrossRefGoogle Scholar
  20. 20.
    Hinz, M., Krappmann, D., Eichten, A., Heder, A., Scheidereit, C., Strauss, M.: NF-κB function in growth control: regulation of cyclin D1 expression and G0/G1-to-S-phase transition. Mol. Cell Biol. 19, 2690–2698 (1999)CrossRefGoogle Scholar
  21. 21.
    Hlavacek, W.S., Faeder, J.R., Blinov, M.L., Posner, R.G., Hucka, M., Fontana, W.: Rules for modeling signal-transduction system. Science STKE 2006 re6 (2006)Google Scholar
  22. 22.
    Hoffmann, A., Levchenko, A., Scott, M.L., Baltimore, D.: The IκB-NFκB signaling module: Temporal control and selective gene activation. Science 298, 1241–1245 (2002)CrossRefGoogle Scholar
  23. 23.
    Huang, Z.: Bcl-2 family proteins as targets for anticancer drug design. Oncogene 19, 6627–6631 (2000)CrossRefGoogle Scholar
  24. 24.
    Idel, S., Dansky, H.M., Breslow, J.L.: A20, a regulator of NFκB, maps to an atherosclerosis locus and differs between parental sensitive C57BL/6J and resistant FVB/N strains. Proceedings of the National Academy of Sciences 100, 14235–14240 (2003)CrossRefGoogle Scholar
  25. 25.
    Jha, S.K., Clarke, E.M., Langmead, C.J., Legay, A., Platzer, A., Zuliani, P.: A Bayesian Approach to Model Checking Biological Systems. In: Degano, P., Gorrieri, R. (eds.) CMSB 2009. LNCS, vol. 5688, pp. 218–234. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    Kang, R., Tang, D., Schapiro, N.E., Livesey, K.M., Farkas, A., Loughran, P., Bierhaus, A., Lotze, M.T., Zeh, H.J.: The receptor for advanced glycation end products (RAGE) sustains autophagy and limits apoptosis, promoting pancreatic tumor cell survival. Cell Death and Differentiation 17(4), 666–676 (2009)CrossRefGoogle Scholar
  27. 27.
    Krishna, S., Jensen, M.H., Sneppen, K.: Minimal model of spiky oscillations in NF-kB signaling. Proceedings of the National Academy of Sciences 103, 10840–10845 (2006)CrossRefGoogle Scholar
  28. 28.
    Langmead, C.J.: Generalized queries and bayesian statistical model checking in dynamic bayesian networks: Application to personalized medicine. In: CSB, pp. 201–212 (2009)Google Scholar
  29. 29.
    Larris, S., Levine, A.J.: The p53 pathway: positive and negative feedback loops. Oncogene 24, 2899–2908 (2005)CrossRefGoogle Scholar
  30. 30.
    Lee, D.F., Huang, M.C.: Advances in targeting IKK and IKK-related kinases for cancer therapy. Clinical Cancer Research 14, 5656 (2008)CrossRefGoogle Scholar
  31. 31.
    Lipniacki, T., Hat, T., Faeder, J.R., Hlavacek, W.S.: Stochastic effects and bistability in T cell receptor signaling. Journal of Theoretical Biology 254, 110–122 (2008)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Lipniacki, T., Paszek, P., Brasier, A., Luxon, B., Kimmel, M.: Crosstalk between p53 and nuclear factor-kB systems: pro-and anti-apoptotic functions of NF-kB. Journal of Theoretical Biology 228, 195–215 (2004)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Lotze, M.T., Tracey, K.: High-mobility group box 1 protein (HMGB1): nuclear weapon in the immune arsenal. Nature Reviews Immunology 5, 331–342 (2005)CrossRefGoogle Scholar
  34. 34.
    McInnes, C.: Progress in the evaluation of CDK inhibitors as anti-tumor agents. Drug Discovery Today 13(19-20), 875–881 (2008)CrossRefGoogle Scholar
  35. 35.
    Mengel, B., Krishna, S., Jensen, M.H., Trusina, A.: Theoretical analyses predict A20 regulates period of NF-κB oscillation. arXiv: bio-ph 0911.0529 (2009)Google Scholar
  36. 36.
    Nelson, D.E., Ihekwaba, A.E.C., et al.: Oscillations in NF-κB signaling control the dynamics of gene expression. Science 306, 704–708 (2004)CrossRefGoogle Scholar
  37. 37.
    Nevins, J.R.: The Rb/E2F pathway and cancer. Human Molecular Genetics 10, 699–703 (2001)CrossRefGoogle Scholar
  38. 38.
    Puszynski, K., Hat, B., Lipniacki, T.: Oscillations and bistability in the stochastic model of p53 regulation. Journal of Theoretical Biology 254, 452–465 (2008)CrossRefGoogle Scholar
  39. 39.
    Rizk, A., Batt, G., Fages, F., Soliman, S.: On a Continuous Degree of Satisfaction of Temporal Logic Formulae with Applications to Systems Biology. In: Heiner, M., Uhrmacher, A.M. (eds.) CMSB 2008. LNCS (LNBI), vol. 5307, pp. 251–268. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  40. 40.
    Rotblat, B., Ehrlich, M., Haklai, R., Kloog, Y.: The Ras inhibitor farnesylthiosalicylic acid (salirasib) disrupts the spatiotemporal localization of active Ras: a potential treatment for cancer. Methods in Enzymology 439, 467–489 (2008)CrossRefGoogle Scholar
  41. 41.
    Semino, C., Angelini, G., Poggi, A., Rubartelli, A.: NK/iDC interaction results in IL-18 secretion by DCs at the synaptic cleft followed by NK cell activation and release of the DC maturation factor HMGB1. Blood 106, 609–616 (2005)CrossRefGoogle Scholar
  42. 42.
    Sherr, C.J., McCormick, F.: The Rb and p53 pathways in cancer. Cancer Cell 2, 103–112 (2002)CrossRefGoogle Scholar
  43. 43.
    Tang, X., Liu, D., Shishodia, S., Ozburn, N., Behrens, C., Lee, J.J., Hong, W.K., Aggarwal, B.B., Wistuba, I.I.: Nuclear factor-κB (NF-κB) is frequently expressed in lung cancer and preneoplastic lesions. Cancer 107, 2637–2646 (2006)CrossRefGoogle Scholar
  44. 44.
    Vakkila, J., Lotze, M.T.: Inflammation and necrosis promote tumour growth. Nature Reviews Immunology 4, 641–648 (2004)CrossRefGoogle Scholar
  45. 45.
    van Beijnum, J.R., Buurman, W.A., Griffioen, A.W.: Convergence and amplification of toll-like receptor (TLR) and receptor for advanced glycation end products (RAGE) signaling pathways via high mobility group B1. Angiogenesis 11, 91–99 (2008)CrossRefGoogle Scholar
  46. 46.
    Vogelstein, B., Lane, D., Levine, A.J.: Surfing the p53 network. Nature 408, 307–310 (2000)CrossRefGoogle Scholar
  47. 47.
    Wee, K.B., Aguda, B.D.: Akt versus p53 in a network of oncogenes and tumor suppressor genes regulating cell survival and death. Biophysical Journal 91, 857–865 (2006)CrossRefGoogle Scholar
  48. 48.
    Wu, H., Lozano, G.: NF-κB activation of p53. a potential mechanism for suppressing cell growth in response to stress. J. Biol. Chem. 269, 20067–20074 (1994)Google Scholar
  49. 49.
    Yao, G., Lee, T.J., Mori, S., Nevins, J., You, L.: A bistable Rb-E2F switch underlies the restriction point. Nature Cell Biology 10, 476–482 (2008)CrossRefGoogle Scholar
  50. 50.
    Younes, H.L.S., Simmons, R.G.: Statistical probabilistic model checking with a focus on time-bounded properties. Information and Computation 204(9), 1368–1409 (2006)MathSciNetCrossRefMATHGoogle Scholar
  51. 51.
    Zuliani, P., Platzer, A., Clarke, E.M.: Bayesian statistical model checking with application to simulink/stateflow verification. In: HSCC, pp. 243–252 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Haijun Gong
    • 1
  • Paolo Zuliani
    • 1
  • Anvesh Komuravelli
    • 1
  • James R. Faeder
    • 2
  • Edmund M. Clarke
    • 1
  1. 1.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Computational BiologyUniversity of PittsburghPittsburghUSA

Personalised recommendations