A Logic Computational Framework to Query Dynamics on Complex Biological Pathways

  • Gustavo Santos-García
  • Javier De Las Rivas
  • Carolyn Talcott
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 294)

Abstract

Biological pathways define complex interaction networks where multiple molecular elements work in a series of reactions to produce a response to different biomolecular signals. These biological systems are dynamic and we need mathematical methods that can analyze symbolic elements and complex interactions between them to produce adequate readouts of such systems. Rewriting logic procedures are adequate tools to handle dynamic systems which are applied to the study of specific biological pathways behaviour. Pathway Logic is a rewriting logic development applied to symbolic systems biology. Rewriting logic language Maude allows us to define transition rules and to set up queries about the flow in the biological system. In this paper we describe the use of Pathway Logic to model and analyze the dynamics in a well-known signaling transduction pathway: epidermal growth factor (EGF) pathway. We also use Pathway Logic Assistant (PLA) tool to browse and query this system.

Keywords

signal transduction symbolic systems biology epidermal growth factor signaling Pathway Logic rewriting logic Maude Petri net executable model 

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References

  1. 1.
    Meseguer, J.: Conditional rewriting logic as a unified model of concurrency. Theor. Comput. Sci. 96, 73–155 (1992)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Meseguer, J.: Twenty years of rewriting logic. J. Log. Algebr. Program. 81(7-8), 721–781 (2012)CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.: All About Maude - A High-Performance Logical Framework. LNCS, vol. 4350. Springer, Heidelberg (2007)MATHGoogle Scholar
  4. 4.
    Vukmirovic, O.G., Tilghman, S.M.: Exploring genome space. Nature 405, 820–822 (2000)CrossRefGoogle Scholar
  5. 5.
    Weng, G., Bhalla, U.S., Iyengar, R.: Complexity in biological signaling systems. Science 284, 92–96 (1999)CrossRefGoogle Scholar
  6. 6.
    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. Prog. 17, 227–239 (2001)CrossRefGoogle Scholar
  7. 7.
    Smolen, P., Baxter, D.A., Byrne, J.H.: Mathematical modeling of gene networks. Neuron 26, 567–580 (2000)CrossRefGoogle Scholar
  8. 8.
    Saadatpour, A., Albert, R.: Discrete dynamic modeling of signal transduction networks. In: Liu, X., Betterton, M.D. (eds.) Computational Modeling of Signaling Networks, pp. 255–272 (2012)Google Scholar
  9. 9.
    Fisher, J., Henzinger, T.A.: Executable cell biology. Nat. Biotechnol. 25(11), 1239–1249 (2007)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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, 113–127 (2006)Google Scholar
  12. 12.
    Regev, A., Panina, E.M., Silverman, W., et al.: Bioambients: An abstraction for biological compartments. Theor. Comput. Sci. 325(1), 141–167 (2004)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Efroni, S., Harel, D., Cohen, I.: Towards rigorous comprehension of biological complexity: modeling, execution and visualization of thymic T-cell maturation. Genome Res. 13(11), 2485–2497 (2003)CrossRefGoogle Scholar
  14. 14.
    Faeder, J.R., Blinov, M.L., Hlavacek, W.S.: Rule-Based Modeling of Biochemical Systems with BioNetGen. Systems Biology. Methods Mol. Biol. 500, 113–167 (2009)CrossRefGoogle Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    Eduati, F., De Las Rivas, J., Di Camillo, B., Toffolo, G., Saez-Rodríguez, J.: Integrating literature-constrained and data-driven inference of signalling networks. Bioinformatics 28(18), 2311–2317 (2012)CrossRefGoogle Scholar
  17. 17.
    Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.: Maude Manual, version 2.6 (2011), http://maude.cs.uiuc.edu
  18. 18.
    Talcott, C., Eker, S., Knapp, M., Lincoln, P., Laderoute, K.: Pathway logic modeling of protein functional domains in signal transduction. In: Proceedings of the Pacific Symposium on Biocomputing (2004)Google Scholar
  19. 19.
    Talcott, C., Dill, D.L.: The pathway logic assistant. In: Plotkin, G. (ed.) Proc. of the Third Intl Conf. CMSB 2005, pp. 228–239 (2005)Google Scholar
  20. 20.
    Talcott, C.L.: Pathway logic. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 21–53. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Cenni, V., Döppler, H., Sonnenburg, E.D., et al.: Regulation of novel protein kinase C epsilon by phosphorylation. Biochem. J. 363(Pt.3), 537–545 (2002)CrossRefGoogle Scholar
  22. 22.
    Schlessinger, J.: Cell signaling by receptor tyrosine kinases. Cell 103, 211–225 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gustavo Santos-García
    • 1
  • Javier De Las Rivas
    • 2
  • Carolyn Talcott
    • 3
  1. 1.Computing CenterUniversidad de SalamancaSalamancaSpain
  2. 2.Bioinformatics and Functional Genomics Research GroupCancer Research Center (CiC-IBMCC, CSIC/USAL)SalamancaSpain
  3. 3.Computer Science LaboratorySRI InternationalMenlo ParkUSA

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