An Introduction to BioModel Engineering, Illustrated for Signal Transduction Pathways

  • David Gilbert
  • Rainer Breitling
  • Monika Heiner
  • Robin Donaldson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5391)


BioModel Engineering is the science of designing, constructing and analyzing computational models of biological systems. It is inspired by concepts from software engineering and computing science.

This paper illustrates a major theme in BioModel Engineering, namely that identifying a quantitative model of a dynamic system means building the structure, finding an initial state, and parameter fitting. In our approach, the structure is obtained by piecewise construction of models from modular parts, the initial state is obtained by analysis of the structure and parameter fitting comprises determining the rate parameters of the kinetic equations. We illustrate this with an example in the area of intracellular signalling pathways.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aziz, A., Sanwal, K., Singhal, V., Brayton, R.K.: Verifying Continuous-Time Markov Chains. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102, pp. 269–276. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  2. 2.
    Baier, C.: On Algorithmic Verification Methods for Probabilistic Systems. Habilitation thesis, University of Mannheim (1998)Google Scholar
  3. 3.
    Brightman, F.A., Fell, D.A.: Differential feedback regulation of the mapk cascade underlies the quantitative differences in egf and ngf signalling in pc12 cells. FEBS Lett. 482, 169–174 (2000)CrossRefGoogle Scholar
  4. 4.
    Breitling, R., Gilbert, D., Heiner, M., Orton, R.J.: A structured approach for the engineering of biochemical network models, illustrated for signalling pathways. Briefings in Bioinformatics 9(5), 404–421 (2008)CrossRefGoogle Scholar
  5. 5.
    Chabrier, N., Fages, F.: Symbolic model checking of biochemical networks. In: Priami, C. (ed.) CMSB 2003. LNCS, vol. 2602, pp. 149–162. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model checking. MIT Press, Cambridge (1999) (third printing, 2001)Google Scholar
  7. 7.
    Cho, K.-H., Shin, S.-Y., Kim, H.-W., Wolkenhauer, O., McFerran, B., Kolch, W.: Mathematical modeling of the influence of RKIP on the ERK signaling pathway. In: Priami, C. (ed.) CMSB 2003. LNCS, vol. 2602, pp. 127–141. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Donaldson, R., Gilbert, D.: A model checking approach to the parameter estimation of biochemical pathways. In: Heiner, M., Uhrmacher, A.M. (eds.) CMSB 2008. LNCS (LNBI), vol. 5307, pp. 269–287. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Fages, F., Rizk, A.: On the analysis of numerical data time series in temporal logic. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 48–63. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Gilbert, D., Heiner, M.: From petri nets to differential equations - an integrative approach for biochemical network analysis. In: Donatelli, S., Thiagarajan, P.S. (eds.) ICATPN 2006. LNCS, vol. 4024, pp. 181–200. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Gilbert, D., Heiner, M., Lehrack, S.: A unifying framework for modelling and analysing biochemical pathways using petri nets. In: Calder, M., Gilmore, S. (eds.) CMSB 2007. LNCS (LNBI), vol. 4695, pp. 200–216. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Heiner, M., Gilbert, D., Donaldson, R.: Petri nets for systems and synthetic biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 215–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Hansson, H., Jonsson, B.: A Logic for Reasoning about Time and Reliability. Formal Aspects of Computing 6(5), 512–535 (1994)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kolch, W., Calder, M., Gilbert, D.: When kinases meet mathematics: the systems biology of MAPK signalling. FEBS Lett. 579, 1891–1895 (2005)CrossRefGoogle Scholar
  15. 15.
    Levchenko, A., Bruck, J., Sternberg, P.W.: Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties. Proc. Natl. Acad. Sci. USA 97(11), 5818–5823 (2000)CrossRefGoogle Scholar
  16. 16.
    Merz, S.: Model checking: A tutorial overview. In: Cassez, F., Jard, C., Rozoy, B., Dermot, M. (eds.) MOVEP 2000. LNCS, vol. 2067, pp. 3–38. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Matsuno, H., Fujita, S., Doi, A., Nagasaki, M., Miyano, S.: Towards biopathway modeling and simulation. In: van der Aalst, W.M.P., Best, E. (eds.) ICATPN 2003. LNCS, vol. 2679, pp. 3–22. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Murata, T.: Petri nets: Properties, analysis and applications. Proc.of the IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  19. 19.
    Orton, R., Sturm, O.E., Gormand, A., Kolch, W., Gilbert, D.: Computational modelling reveals feedback redundancy within the epidermal growth factor receptor/extracellular-signal regulated kinase signalling pathway. Systems Biology 2, 173–183 (2008)Google Scholar
  20. 20.
    Pnueli, A.: The Temporal Semantics of Concurrent Programs. Theor. Comput. Sci. 13, 45–60 (1981)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Popova-Zeugmann, L., Heiner, M., Koch, I.: Time Petri Nets for Modelling and Analysis of Biochemical Networks. Fundamenta Informaticae 67, 149–162 (2005)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Schoeberl, B., Eichler-Jonsson, C., Gilles, E.D., Muller, G.: Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnology 20, 370–375 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • David Gilbert
    • 1
  • Rainer Breitling
    • 2
  • Monika Heiner
    • 3
  • Robin Donaldson
    • 4
  1. 1.School of Information Science, Computing and MathematicsBrunel UniversityUK
  2. 2.Groningen Bioinformatics CentreUniversity of GroningenHarenThe Netherlands
  3. 3.Department of Computer ScienceBrandenburg University of TechnologyCottbusGermany
  4. 4.Bioinformatics Research CentreUniversity of GlasgowGlasgowUK

Personalised recommendations