Advertisement

A Tutorial on Mathematical Modeling of Biological Signaling Pathways

Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 880)

Abstract

Mathematical models have been widely used in the studies of biological signaling pathways. Among these studies, two systems biology approaches have been applied: top-down and bottom-up systems biology. The former approach focuses on X-omics researches involving the measurement of experimental data in a large scale, for example proteomics, metabolomics, or fluxomics and transcriptomics. In contrast, the bottom-up approach studies the interaction of the network components and employs mathematical models to gain some insights about the mechanisms and dynamics of biological systems. This chapter introduces how to use the bottom-up approach to establish mathematical models for cell signaling studies.

Key words

Mathematical modeling Signaling pathway Reaction kinetics Systems biology 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

This work was supported by the Excellence Initiative of the German Federal and State Governments (EXC 294).

References

  1. 1.
    Boogerd FC, Bruggeman FJ, Hofmeyr J-HS, Westerhoff HV (2007) Systems biology: philosophical foundations, 1st edn. Elsevier, AmsterdamGoogle Scholar
  2. 2.
    Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531PubMedCrossRefGoogle Scholar
  3. 3.
    Aldridge BB, Burke JM, Lauffenburger DA, Sorger PK (2006) Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8:1195–1203PubMedCrossRefGoogle Scholar
  4. 4.
    Guldberg CM, Waage P (1879) Concerning chemical affinity. Erdmann’s J Pract Chem 127:69–114CrossRefGoogle Scholar
  5. 5.
    Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H (2005) Systems biology in practice: concepts, implementation and application, 1st edn. Wiley, BerlinCrossRefGoogle Scholar
  6. 6.
    Hill AV (1910) The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J Physiol 40:4–7Google Scholar
  7. 7.
    Legewie S, Bluthgen N, Herzel H (2005) Quantitative analysis of ultrasensitive responses. FEBS J 272:4071–4079PubMedCrossRefGoogle Scholar
  8. 8.
    Szallasi Z, Stelling J, Periwal V (2006) System modeling in cellular biology: from concepts to nuts and bolts, 1st edn. The MIT, Boston, MAGoogle Scholar
  9. 9.
    Ferrell JE Jr (2008) Feedback regulation of opposing enzymes generates robust, all-or-none bistable responses. Curr Biol 18:R244–R245PubMedCrossRefGoogle Scholar
  10. 10.
    Ma W, Trusina A, El-Samad H, Lim WA, Tang C (2009) Defining network topologies that can achieve biochemical adaptation. Cell 138:760–773PubMedCrossRefGoogle Scholar
  11. 11.
    Zi Z, Klipp E (2007) Cellular signaling is potentially regulated by cell density in receptor trafficking networks. FEBS Lett 581:4589–4595PubMedCrossRefGoogle Scholar
  12. 12.
    Moles CG, Mendes P, Banga JR (2003) Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res 13:2467–2474PubMedCrossRefGoogle Scholar
  13. 13.
    Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N et al (2006) COPASI—a COmplex PAthway SImulator. Bioinformatics 22:3067–3074PubMedCrossRefGoogle Scholar
  14. 14.
    Zi Z (2011) SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models. Bioinformatics 27:1028–1029PubMedCrossRefGoogle Scholar
  15. 15.
    Zi Z, Klipp E (2006) SBML-PET: a Systems Biology Markup Language-based parameter estimation tool. Bioinformatics 22:2704–2705PubMedCrossRefGoogle Scholar
  16. 16.
    Maiwald T, Timmer J (2008) Dynamical modeling and multi-experiment fitting with PottersWheel. Bioinformatics 24:2037–2043PubMedCrossRefGoogle Scholar
  17. 17.
    Tanimura AFMMHKN (2003) CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. Biosilico 1:159–162CrossRefGoogle Scholar
  18. 18.
    Mendes P, Hoops S, Sahle S, Gauges R, Dada J, Kummer U (2009) Computational modeling of biochemical networks using COPASI. Methods Mol Biol 500:17–59PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.BIOSS Centre for Biological Signalling StudiesUniversity of FreiburgFreiburgGermany

Personalised recommendations

Cite protocol

Buy options