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Adaptive Modularization of the MAPK Signaling Pathway Using the Multiagent Paradigm

  • Abbas Sarraf Shirazi
  • Sebastian von Mammen
  • Christian Jacob
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)

Abstract

We utilize an agent-based approach to model the MAPK signaling pathway, in which we capture both individual and group behaviour of the biological entities inside the system. In an effort to adaptively reduce complexity of interactions among the simulated agents, we propose a bottom-up approach to find and group similar agents into a single module which will result in a reduction in the complexity of the system. Our proposed adaptive method of grouping and ungrouping captures the dynamics of the system by identifying and breaking modules adaptively as the simulation proceeds. Experimental results on our simulated MAPK signaling pathway show that our proposed method can be used to identify modules in both stable and periodic systems.

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References

  1. 1.
    Amigoni, F., Schiaffonati, V.: Multiagent-based simulation in biology a critical analysis. Model-Based Reasoning in Science, Technology, and Medicine 64, 179–191 (2007)CrossRefGoogle Scholar
  2. 2.
    Desmeulles, G., Querrec, G., Redou, P., Kerdelo, S., Misery, L., Rodin, V., Tisseau, J.: The virtual reality applied to biology understanding: The in virtuo experimentation. Expert Syst. Appl. 30(1), 82–92 (2006)CrossRefGoogle Scholar
  3. 3.
    Hoar, R., Penner, J., Jacob, C.: Transcription and evolution of a virtual bacteria culture. In: IEEE Congress on Evolutionary Computation, Canberra, Australia. IEEE Press, Los Alamitos (2003)Google Scholar
  4. 4.
    Jacob, C., Burleigh, I.: Genetic programming inside a cell. In: Yu, T., Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practice III, pp. 191–206. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Jacob, C., Barbasiewicz, A., Tsui, G.: Swarms and genes: Exploring λ-switch gene regulation through swarm intelligence. In: CEC 2006, IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada (2006)Google Scholar
  6. 6.
    Jacob, C., Burleigh, I.: Biomolecular swarms: An agent-based model of the lactose operon. Natural Computing 3(4), 361–376 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gonzalez, P.P., Cardenas, M., Camacho, D., Franyuti, A., Rosas, O., Lagunez-Otero, J.: Cellulat: an agent-based intracellular signalling model. Biosystems 68(2-3), 171–185 (2003)CrossRefGoogle Scholar
  8. 8.
    Khan, S., Makkena, R., McGeary, F., Decker, K., Gillis, W., Schmidt, C.: A multi-agent system for the quantitative simulation of biological networks, pp. 385–392 (2003)Google Scholar
  9. 9.
    Nayak, L., De, R.K.: An algorithm for modularization of mapk and calcium signaling pathways: comparative analysis among different species. J. Biomed. Inform. 40(6), 726–749 (2007)CrossRefGoogle Scholar
  10. 10.
    Papin, J.A., Reed, J.L., Palsson, B.O.: Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem. Sci. 29(12), 641–647 (2004)CrossRefGoogle Scholar
  11. 11.
    Schuster, S., Dandekar, T., Fell, D.A.: Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol. 17(2), 53–60 (1999)CrossRefGoogle Scholar
  12. 12.
    Schilling, C.H., Letscher, D., Palsson, B.O.: Theory for the systemic definition of metabolic pathways and their use in interpreting metabolic function from a pathway-oriented perspective. J. Theor. Biol. 203(3), 229–248 (2000)CrossRefGoogle Scholar
  13. 13.
    Burgard, A.P., Nikolaev, E.V., Schilling, C.H., Maranas, C.D.: Flux coupling analysis of genome-scale metabolic network reconstructions. Genome. Res. 14(2), 301–312 (2004)CrossRefGoogle Scholar
  14. 14.
    Price, N.D., Schellenberger, J., Palsson, B.O.: Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys. J. 87(4), 2172–2186 (2004)CrossRefGoogle Scholar
  15. 15.
    Martins, M., Ferreira Jr., S.C., Vilela, M.: Multiscale models for biological systems. Current Opinion in Colloid & Interface Science 15(1-2), 18–23 (2010)CrossRefGoogle Scholar
  16. 16.
    Erson, E.Z., Cavuşoğlu, M.C.: A software framework for multiscale and multilevel physiological model integration and simulation. In: Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2008, pp. 5449–5453 (2008)Google Scholar
  17. 17.
    Merks, R.M., Glazier, J.A.: A cell-centered approach to developmental biology. Physica A: Statistical Mechanics and its Applications 352(1), 113–130 (2005)CrossRefGoogle Scholar
  18. 18.
    Haken, H.: Synergetics: an introduction: monequilibrium phase transitions and self-organization in physics, chemistry and biology / Hermann Haken. Springer, Berlin (1977)Google Scholar
  19. 19.
    Walker, D.C., Southgate, J.: The virtual cell–a candidate co-ordinator for ’middle-out’ modelling of biological systems. Briefings in Bioinformatics 10(4), 450–461 (2009)CrossRefGoogle Scholar
  20. 20.
    Bassingthwaighte, J., Chizeck, H., Atlas, L.: Strategies and tactics in multiscale modeling of cell-to-organ systems. Proceedings of the IEEE 94(4), 819–831 (2006)CrossRefGoogle Scholar
  21. 21.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan, New York (1994)zbMATHGoogle Scholar
  22. 22.
    Huang, C.Y., Ferrell, J.E.: Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc. Natl. Acad. Sci. USA 93(19), 10078–10083 (1996)CrossRefGoogle Scholar
  23. 23.
    Kholodenko, B.N.: Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur. J. Biochem. 267(6), 1583–1588 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Abbas Sarraf Shirazi
    • 1
  • Sebastian von Mammen
    • 1
  • Christian Jacob
    • 1
    • 2
  1. 1.Dept. of Computer Science, Faculty of Science
  2. 2.Dept. of Biochemistry & Molecular Biology, Faculty of MedicineUniversity of CalgaryCanada

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