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Analysis of Cellular Proliferation and Survival Signaling by Using Two Ligand/Receptor Systems Modeled by Pathway Logic

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Hybrid Systems Biology (HSB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9271))

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Abstract

Systems biology attempts to understand biological systems by their structure, dynamics, and control methods. Hepatocyte growth factor (HGF) and interleukin 6 (IL6) are two proteins involved in cellular signaling that bind specific cell surface receptors (HGFR and IL6R, respectively) in order to induce cellular proliferation in different cell types or cell contexts. In both cases, the signaling is initiated by binding the ligand (HGF or IL6) to the membrane-bound receptors (HGFR or IL6R) so as to trigger two cellular signaling paths that have several common elements. In this paper we discuss the processes by which an initial cell leads to cellular proliferation and/or survival signaling by using one of these two ligand/receptor systems analyzed by “rewriting logic” methodology. Rewriting logic procedures are suitable computational tools that handle these dynamic systems, and they can be applied to the study of specific biological pathways behavior. Pathway Logic (PL) constitutes a rewriting logic formalism that provides a knowledge base and development environment to carry out model checking, searches, and executions of signaling systems. Moreover, Pathway Logic Assistant (PLA) is a tool that helps us visualize, analyze and understand graphically cellular elements and their relations. We compare the models of HGF/HGFR and IL6/IL6R signaling pathways in order to investigate the relation between these processes and the way in which they induce cellular proliferation. In conclusion, our results illustrate the use of a logical system that explores complex and dynamic cellular signaling processes.

Pathway Logic development has been funded in part by NIH BISTI R21/R33 grant (GM068146-01), NIH/NCI P50 grant (CA112970-01), and NSF grant IIS-0513857. This work was partially supported by NSF grant IIS-0513857. Research was supported by Spanish projects Strongsoft TIN2012-39391-C04-04 and PI12/00624 (MINECO, Instituto de Salud Carlos III).

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References

  1. Abate, A., Bai, Y., Sznajder, N., Talcott, C.L., Tiwari, A.: Quantitative and probabilistic modeling in pathway logic. In: Zhu, M.M., Zhang, Y., Arabnia, H.R., Deng, Y. (eds.) Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2007, pp. 922–929. Harvard Medical School, Boston, MA, October 14–17, 2007. IEEE (2007)

    Google Scholar 

  2. Agha, G., Danvy, O., Meseguer, J. (eds.): Formal Modeling: Actors, Open Systems, Biological Systems - Essays Dedicated to Carolyn Talcott on the Occasion of her 70th Birthday. LNCS, vol. 7000. Springer, Heidelberg (2011)

    Google Scholar 

  3. Asthagiri, A.R., Lauffenburger, D.A.: A computational study of feedback effects on signal dynamics in a mitogen-activated protein kinase (MAPK) pathway model. Biotechnol. Progr. 17(2), 227–239 (2001)

    Article  Google Scholar 

  4. Blinov, M.L., Faeder, J.R., Goldstein, B., Hlavacek, W.S.: BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20(17), 3289–3291 (2004)

    Article  Google Scholar 

  5. Boccaccio, C., Ando, M., Tamagnone, L., Bardelli, A., Michieli, P., Battistini, C., Comoglio, P.M.: Induction of epithelial tubules by growth factor HGF depends on the STAT pathway. Nature 391(6664), 285–288 (1998)

    Article  Google Scholar 

  6. Bottaro, D.P., Rubin, J.S., Faletto, D.L., Chan, A.M., Kmiecik, T.E., Vande Woude, G.F., Aaronson, S.A.: Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science 251(4995), 802–804 (1991)

    Article  Google Scholar 

  7. Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C. (eds.): All About Maude - A High-Performance Logical Framework, how to Specify, Program and Verify Systems in Rewriting Logic. LNCS, vol. 4350. Springer, Heidelberg (2007). doi:10.1007/978-3-540-71999-1

  8. Danos, V., Laneve, C.: Formal molecular biology. Theor. Comput. Sci. 325(1), 69–110 (2004). doi:10.1016/j.tcs.2004.03.065

    Article  MathSciNet  MATH  Google Scholar 

  9. Donaldson, R., Talcott, C.L., Knapp, M., Calder, M.: Understanding signalling networks as collections of signal transduction pathways. In: Quaglia, P. (ed.) Computational Methods in Systems Biology, CMSB, pp. 86–95. ACM (2010)

    Google Scholar 

  10. Efroni, S., Harel, D., Cohen, I.R.: Toward rigorous comprehension of biological complexity: modeling, execution, and visualization of thymic T-cell maturation. Genome Res. 13(11), 2485–2497 (2003)

    Article  Google Scholar 

  11. Faeder, J.R., Blinov, M.L., Hlavacek, W.S.: Rule-based modeling of biochemical systems with BioNetGen. Methods Mol. Biol. 500, 113–167 (2009)

    Article  Google Scholar 

  12. Fiadeiro, J.L. (ed.): WADT 1998. LNCS, vol. 1589. Springer, Heidelberg (1999)

    Google Scholar 

  13. Fisher, J., Henzinger, T.A.: Executable cell biology. Nat. Biotech. 25(11), 1239–1249 (2007). doi:10.1038/nbt1356

    Article  Google Scholar 

  14. Graziani, A., Gramaglia, D., dalla Zonca, P., Comoglio, P.M.: Hepatocyte growth factor/scatter factor stimulates the Ras-guanine nucleotide exchanger. J. Biol. Chem. 268(13), 9165–9168 (1993)

    Google Scholar 

  15. Guschin, D., Rogers, N., Briscoe, J., Witthuhn, B., Watling, D., Horn, F., Pellegrini, S., Yasukawa, K., Heinrich, P., Stark, G.R.: A major role for the protein tyrosine kinase JAK1 in the JAK/STAT signal transduction pathway in response to interleukin-6. EMBO J. 14(7), 1421–1429 (1995)

    Google Scholar 

  16. Hardy, S., Robillard, P.N.: Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways. Bioinformatics 24(2), 209–217 (2008)

    Article  Google Scholar 

  17. Heiser, L.M., Wang, N.J., Talcott, C.L., Laderoute, K.R., Knapp, M., Guan, Y., Hu, Z., Ziyad, S., Weber, B.L., Laquerre, S., Jackson, J.R., Wooster, R.F., Kuo, W.L., Gray, J.W., Spellman, P.T.: Integrated analysis of breast cancer cell lines reveals unique signaling pathways. Genome Biol. 10(3), R31 (2009)

    Article  Google Scholar 

  18. Hwang, W., Hwang, Y., Lee, S., Lee, D.: Rule-based multi-scale simulation for drug effect pathway analysis. BMC Med. Inform. Decis. Mak. 13(Suppl 1), S4 (2013)

    Article  Google Scholar 

  19. Li, C., Ge, Q.W., Nakata, M., Matsuno, H., Miyano, S.: Modelling and simulation of signal transductions in an apoptosis pathway by using timed petri nets. J. Biosci. 32(1), 113–127 (2007)

    Article  Google Scholar 

  20. Liu, X., Betterton, M.D., Saadatpour, A., Albert, R.: Methods in Molecular Biology, vol. 880, pp. 255–272. Humana Press, Clifton (2012)

    Google Scholar 

  21. Martí-Oliet, N., Ölveczky, P.C., Talcott, C. (eds.): Logic, Rewriting, and Concurrency- Essays dedicated to José Meseguer on the occasion of his 65th birthday. LNCS, vol. 9200. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23165-5

  22. Meseguer, J.: Conditional rewriting logic as a unified model of concurrency. Theor. Comput. Sci. 96(1), 73–155 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  23. Meseguer, J.: Twenty years of rewriting logic. J. Log. Algebr. Program 81(7–8), 721–781 (2012). doi:10.1016/j.jlap.2012.06.003

    Article  MathSciNet  MATH  Google Scholar 

  24. Novotny-Diermayr, V., Lin, B., Gu, L., Cao, X.: Modulation of the interleukin-6 receptor subunit glycoprotein 130 complex and its signaling by LMO4 interaction. J. Biol. Chem. 280(13), 12747–12757 (2005)

    Article  Google Scholar 

  25. Novotny-Diermayr, V., Zhang, T., Gu, L., Cao, X.: Protein kinase C delta associates with the interleukin-6 receptor subunit glycoprotein (gp) 130 via Stat3 and enhances Stat3-gp130 interaction. J. Biol. Chem. 277(51), 49134–49142 (2002)

    Article  Google Scholar 

  26. Pais, R.S., Moreno-Barriuso, N., Hernandez-Porras, I., Lopez, I.P., De Las Rivas, J., Pichel, J.G.: Transcriptome analysis in prenatal IGF1-deficient mice identifies molecular pathways and target genes involved in distal lung differentiation. PLoS One 8(12), e83028 (2013)

    Article  Google Scholar 

  27. Panikkar, A., Knapp, M., Mi, H., Anderson, D., Kodukula, K., Galande, A.K., Talcott, C.L.: Applications of Pathway Logic modeling to target identification. In: Agha et al. (eds.): Formal Modeling: Actors, Open Systems, Biological Systems - Essays Dedicated to Carolyn Talcott on the Occasion of her 70th Birthday. LNCS, vol. 7000, pp. 434–445. Springer, Heidelberg (2011)

    Google Scholar 

  28. Podar, K., Mostoslavsky, G., Sattler, M., Tai, Y.T., Hayashi, T., Catley, L.P., Hideshima, T., Mulligan, R.C., Chauhan, D., Anderson, K.C.: Critical role for hematopoietic cell kinase (Hck)-mediated phosphorylation of Gab1 and Gab2 docking proteins in interleukin 6-induced proliferation and survival of multiple myeloma cells. J. Biol. Chem. 279(20), 21658–21665 (2004)

    Article  Google Scholar 

  29. Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: BioAmbients: an abstraction for biological compartments. Theor. Comput. Sci. 325(1), 141–167 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  30. Sadot, A., Fisher, J., Barak, D., Admanit, Y., Stern, M.J., Hubbard, E.J.A., Harel, D.: Toward verified biological models. IEEE/ACM Trans. Comput. Biol. Bioinform. 5(2), 223–234 (2008)

    Article  Google Scholar 

  31. Santos-García, G., De Las Rivas, J., Talcott, C.L.: In: Saez-Rodriguez, J., Rocha, M.P., Fdez-Riverola, F., De Paz Santana, J.F. (eds.) A Logic Computational Framework to Query Dynamics on Complex Biological Pathways. AISC, vol. 294, pp. 207–214. Springer, Heidelberg (2014)

    Google Scholar 

  32. Schlessinger, J.: Cell signaling by receptor tyrosine kinases. Cell 103(2), 211–225 (2000)

    Article  Google Scholar 

  33. Smolen, P., Baxter, D.A., Byrne, J.H.: Mathematical modeling of gene networks. Neuron 26(3), 567–580 (2000)

    Article  MATH  Google Scholar 

  34. Sodhi, A., Montaner, S., Gutkind, J.S.: Viral hijacking of G-protein-coupled-receptor signalling networks. Nat. Rev. Mol. Cell Biol. 5(12), 998–1012 (2004). doi:10.1038/nrm1529

    Article  Google Scholar 

  35. Taga, T., Hibi, M., Hirata, Y., Yamasaki, K., Yasukawa, K., Matsuda, T., Hirano, T., Kishimoto, T.: Interleukin-6 triggers the association of its receptor with a possible signal transducer, gp130. Cell 58(3), 573–581 (1995)

    Article  Google Scholar 

  36. Talcott, C.: Pathway Logic. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 21–53. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  37. Talcott, C.L., Dill, D.L.: The pathway logic assistant. In: Plotkin, G. (ed.) Proceedings of the Third International Workshop on Computational Methods in Systems Biology, pp. 228–239 (2005)

    Google Scholar 

  38. Talcott, C.L., Eker, S., Knapp, M., Lincoln, P., Laderoute, K.: Pathway logic modeling of protein functional domains in signal transduction. In: Markstein, P., Xu, Y. (eds.) Proceedings of the 2nd IEEE Computer Society Bioinformatics Conference, CSB 2003, pp. 618–619, Stanford, CA, 11–14 August 2003. IEEE Computer Society (2003). doi:10.1109/CSB.2003.1227425

  39. Vukmirovic, O.G., Tilghman, S.M.: Exploring genome space. Nature 405(6788), 820–822 (2000). doi:10.1038/35015690

    Article  Google Scholar 

  40. Weng, G., Bhalla, U.S., Iyengar, R.: Complexity in biological signaling systems. Science 284(5411), 92–96 (1999)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad de Salamanca, Salamanca, Spain

    Gustavo Santos-García

  2. Computer Science Laboratory, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA

    Carolyn Talcott

  3. Cancer Research Center (CSIC/USAL) and IBSAL, Salamanca, Spain

    Javier De Las Rivas

Authors
  1. Gustavo Santos-García
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  2. Carolyn Talcott
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  3. Javier De Las Rivas
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Correspondence to Gustavo Santos-García .

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Editors and Affiliations

  1. University of Oxford, Oxford, United Kingdom

    Alessandro Abate

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    David Šafránek

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Santos-García, G., Talcott, C., De Las Rivas, J. (2015). Analysis of Cellular Proliferation and Survival Signaling by Using Two Ligand/Receptor Systems Modeled by Pathway Logic. In: Abate, A., Šafránek, D. (eds) Hybrid Systems Biology. HSB 2015. Lecture Notes in Computer Science(), vol 9271. Springer, Cham. https://doi.org/10.1007/978-3-319-26916-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-26916-0_13

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