Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment

  • W. J. Tang
  • Q. H. Wu
  • J. R. Saunders
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4447)


“Quorum sensing” has been described as “the most consequential molecular microbiology story of the last decade” [1][2]. The purpose of this paper is to study the mechanism of quorum sensing, in order to obtain a deeper understanding of how and when this mechanism works. Our study focuses on the use of an Individual-based Modeling (IbM) method to simulate this phenomenon of “cell-to-cell communication” incorporated in bacterial foraging behavior, in both intracellular and population scales. The simulation results show that this IbM approach can reflect the bacterial behaviors and population evolution in time-varying environments, and provide plausible answers to the emerging question regarding to the significance of this phenomenon of bacterial foraging behaviors.


Quorum Sensing Gene Expression Program Population Evolution Gravity Center Core Point 
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.
    Busby, S., de Lorenzo, V.: Cell regulation putting together pieces of the big puzzle. Current Opinion in Microbiology 5, 117–118 (2001)CrossRefGoogle Scholar
  2. 2.
    Winzer, K., Hardie, K.R., Williams, P.: Bacterial cell-to-cell communication: sorry, can’t talk now - gone to lunch! Current Opinion in Microbiology 5, 216–222 (2002)CrossRefGoogle Scholar
  3. 3.
    Vlachos, C., Paton, R.C., Saunders, J.R., Wu, Q.H.: A rule-based approach to the modelling of bacterial ecosystems. BioSystems 84, 49–72 (2005)CrossRefGoogle Scholar
  4. 4.
    Ben-Jacob, E., Shochet, O., Tenenbaum, A., Cohen, I.: Generic modeling of cooperative growth patterns in bacterial colonies. Nature 368, 46–49 (1994)CrossRefGoogle Scholar
  5. 5.
    Davies, D.G., Parsek, M.R., Pearson, J.P. et al.: The involvement of cell-to-cell signals in the development of a bacterial biofilm. Nature 280, 295–298 (1998)Google Scholar
  6. 6.
    Ward, J.P., King, J.R., Koerber, A.J.: Mathematical modelling of quorum sensing in bacteria. Journal of Mathematics Applied in Medicine and Biology 18, 263–292 (2001)zbMATHCrossRefGoogle Scholar
  7. 7.
    Dockery, J.D., Keener, J.P.: A mathematical model for quorum sensing in pseudomonas aeruginosa. Mathematical Biology, 1–22 (2000)Google Scholar
  8. 8.
    Painter, K.J., Hillen, T.: Volume-filling and quorum sensing in models for chemosensitive movement. Canadian Applied Mathematics Quarterly 10, 51–543 (2002)MathSciNetGoogle Scholar
  9. 9.
    Goryachev, A.B., Toh, D.J., Wee, K.B., et al.: Transition to quorum sensing in an agrobacterium population: A stochastic model. PLoS Computational Biology, 1–51 (2005)Google Scholar
  10. 10.
    Muller, J., Kuttler, C., Hense, B.A.: Cell-cell communication by quorum sensing and dimension-reduction. Technical Report, Technical University Munich, pp. 1–28 (2005)Google Scholar
  11. 11.
    You, L., Cox III, R.C., Weiss, R., Arnold, F. H.: Programmed population control by cell-cell communication and regulated killing. Nature (2004)Google Scholar
  12. 12.
    Garcia-Ojalvo, J., Elowitz, M.B., Strogatz, S.H.: Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing. Proceedings of the National Academy of Sciences 101, 10955–10960 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Tang, W.J., Wu, Q.H., Saunders, J.R.: A novel model for bacterial foraging in varying environments. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 556–565. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Miller, M.B., Bassler, B.L.: Quorum sensing in bacteria. Annual Review of Microbiology 55, 165–199 (2001)CrossRefGoogle Scholar
  15. 15.
    Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatical databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, KDD-96 (1996)Google Scholar
  16. 16.
    Cao, Y.J., Wu, Q.H.: Study of initial population in evolutionary programming. In: Proceedings of the European Control Conference, vol. 368, pp. 1–4 (1997)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • W. J. Tang
    • 1
  • Q. H. Wu
    • 1
  • J. R. Saunders
    • 2
  1. 1.Department of Electrical Engineering and Electronics 
  2. 2.School of Biological Sciences The University of Liverpool, Liverpool L69 3GJU.K.

Personalised recommendations