Incremental Signaling Pathway Modeling by Data Integration

  • Geoffrey Koh
  • David Hsu
  • P. S. Thiagarajan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


Constructing quantitative dynamic models of signaling pathways is an important task for computational systems biology. Pathway model construction is often an inherently incremental process, with new pathway players and interactions continuously being discovered and additional experimental data being generated. Here we focus on the problem of performing model parameter estimation incrementally by integrating new experimental data into an existing model. A probabilistic graphical model known as the factor graph is used to represent pathway parameter estimates. By exploiting the network structure of a pathway, a factor graph compactly encodes many parameter estimates of varying quality as a probability distribution. When new data arrives, the parameter estimates are refined efficiently by applying a probabilistic inference algorithm known as belief propagation to the factor graph. A key advantage of our approach is that the factor graph model contains enough information about the old data, and uses only new data to refine the parameter estimates without requiring explicit access to the old data. To test this approach, we applied it to the Akt-MAPK pathways, which regulate the apoptotic process and are among the most actively studied signaling pathways. The results show that our new approach can obtain parameter estimates that fit the data well and refine them incrementally when new data arrives.


Molecular Species Data Integration Belief Propagation Variable Node Factor Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bhalla, U.S., Iyengar, R.: Emergent properties of networks of biological signaling pathways. Science 283, 381–387 (1999)CrossRefGoogle Scholar
  2. 2.
    Aldridge, B.B., Burke, J.M., Lauffenburger, D.A., Sorger, P.K.: Physicochemical modelling of cell signalling pathways. Nature Cell Biology 8(11), 1195–1203 (2006)CrossRefGoogle Scholar
  3. 3.
    Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: A comparison of global optimization methods. Genome Research 13(11), 2467–2474 (2003)CrossRefGoogle Scholar
  4. 4.
    Kschischange, F., Frey, B., Loeliger, H.: Factor graphs and the sum-product algorithm. IEEE Trans. on Information Theory 42(2), 498–519 (2001)CrossRefGoogle Scholar
  5. 5.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Cambridge (2009)Google Scholar
  6. 6.
    Klipp, E., et al.: Systems Biology in Practice. Wiley-VCH, Chichester (2005)CrossRefGoogle Scholar
  7. 7.
    Matsuno, H., Tanaka, Y., Aoshima, H., Doi, A., Matsui, M., Miyano, S.: Biopathways representation and simulation on hybrid functional Petri net. Silico Biology 3(3), 389–404 (2003)Google Scholar
  8. 8.
    Yoshida, R., Nagasaki, M., Yamaguchi, R., Imoto, S., Miyano, S., Higuchi, T.: Bayesian learning of biological pathways on genomic data assimilation. Bioinformatics 24(22), 2592–2601 (2008)CrossRefGoogle Scholar
  9. 9.
    Purvis, J., Radhakrishnan, R., Diamond, S.: Steady-state kinetic modeling constrains cellular resting states and dynamic behavior. PLoS Computational Biology 5(3) (2009)Google Scholar
  10. 10.
    Gat-Viks, I., Tanay, A., Raijman, D., Shamir, R.: The factor graph network model for biological systems. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 31–47. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Delcher, A., Kasif, S., Goldberg, H., Hsu, W.: Protein secondary structure modelling with probabilistic networks. In: Proc. Int. Conf. on Intelligent Systems & Molecular Biology, pp. 109–117 (1993)Google Scholar
  12. 12.
    Kalos, M., Whitlock, P.: Monte Carlo Methods, vol. 1. John Wiley & Sons, New York (1986)zbMATHGoogle Scholar
  13. 13.
    Koh, G.: Pathway Models Decomposition and Composition Techniques for Parameter Estimation. PhD thesis, Graduate School of Integrative Sciences, National University of Singapore, Singapore (2008)Google Scholar
  14. 14.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  15. 15.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. Int. J. Computer Vision 70(1), 41–54 (2004)CrossRefGoogle Scholar
  16. 16.
    McEliece, R.J., MacKay, D.J., Cheng, J.F.: Turbo decoding as an instance of Pearl’s “Belief Propagation” algorithm. IEEE J. on Selected Areas in Communications 16(2), 140–152 (1998)CrossRefGoogle Scholar
  17. 17.
    Brazil, D.P., Yang, Z.Z., Hemmings, B.A.: Advances in protein kinase B signalling: AKTion on multiple fronts. Trends in Biochemical Sciences 29(5), 233–242 (2004)CrossRefGoogle Scholar
  18. 18.
    Koh, G., Teong, H.F.C., Clément, M.V., Hsu, D., Thiagarajan, P.: A decompositional approach to parameter estimation in pathway modeling: a case study of the Akt and MAPK pathways and their crosstalk. Bioinformatics 22(14), e271–e280 (2006)Google Scholar
  19. 19.
    Gill, P., Murray, W., Wright, M.: Practical Optimization. Academic Press, London (1982)Google Scholar
  20. 20.
    Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., Kummer, U.: COPASI—a COmplex PAthway SImulator. Bioinformatics 22(24), 3067–3074 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Geoffrey Koh
    • 1
  • David Hsu
    • 2
  • P. S. Thiagarajan
    • 2
  1. 1.Bioprocessing Technology InstituteSingaporeSingapore
  2. 2.National University of SingaporeSingaporeSingapore

Personalised recommendations