Systematic Complexity Reduction of Signaling Models and Application to a CD95 Signaling Model for Apoptosis

  • Dennis Rickert
  • Nicolai Fricker
  • Inna N. Lavrik
  • Fabian J. TheisEmail author


A major problem when designing mathematical models of biochemical processes to analyze and explain experimental data is choosing the correct degree of model complexity. A common approach to solve this problem is top-down: Initially, complete models including all possible reactions are generated; they are then iteratively reduced to a more manageable size. The reactions to be simplified at each step are often chosen manually since exploration of the full search space seems unfeasible. While such a strategy is sufficient to identify a single, clearly structured reduction of the model, it discards additional information such as whether some model features are essential. In this chapter, we introduce alternate set-based strategies to model reduction that can be employed to exhaustively analyze the complete reduction space of a biochemical model instead of only identifying a single valid reduction.


  1. Agrawal R, and Srikant R Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487–499, Santiago, Chile, September 1994.Google Scholar
  2. Fricker N, Beaudouin J, Richter P, Eils R, Krammer PH et al (2010) Model-based dision of CD95 signaling dynamics reveals both a pro- and antiapoptotic role of c-FLIPL. J Cell Biol 190(3):377–389, Intro to CD95 ModelingPubMedCrossRefGoogle Scholar
  3. Kischkel FC, Hellbardt S, Behrmann I, Germer M, Pawlita M et al (1995) Cytotoxicity-dependent APO-1 (Fas/CD95)-associated proteins form a death-inducing signaling complex (DISC) with the receptor. EMBO J 14(22):5579–5588, Forming of DISCPubMedGoogle Scholar
  4. Lavrik IN, Golks A, Riess D, Bentele M, Eils R et al (2007) Analysis of CD95 threshold signaling: triggering of CD95 (FAS/APO-1) at low concentrations primarily results in survival signaling. J Biol Chem 282(18):13664–13671, Intro to thresholdPubMedCrossRefGoogle Scholar
  5. Neumann L, Pforr C, Beaudouin J, Pappa A, Fricker N et al (2010) Dynamics within the CD95 death-inducing signaling complex decide life and death of cells. Mol Syst Biol 6:352, Role of cFLIPlPubMedCrossRefGoogle Scholar
  6. Suda T, Takahashi T, Golstein P, Nagata S (1993) Molecular cloning and expression of the Fas ligand, a novel member of the tumor necrosis factor family. Cell 75:1169–1178, CD95R/L BindingPubMedCrossRefGoogle Scholar
  7. Systems Biology Toolbox for MATLAB: A computational platform for research in Systems Biology, Bioinformatics, 22(4):514–515, 2006.Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Dennis Rickert
    • 1
  • Nicolai Fricker
    • 1
    • 2
    • 3
  • Inna N. Lavrik
    • 4
  • Fabian J. Theis
    • 1
    Email author
  1. 1.Institute of Bioinformatics and Systems BiologyHelmholtz Zentrum MünchenNeuherbergGermany
  2. 2.Division of ImmunogeneticsGerman Cancer Research Center (DKFZ)HeidelbergGermany
  3. 3.BioquantHeidelbergGermany
  4. 4.Department of Translational Inflammation Research, Institute of Experimental Internal MedicineOtto von Guericke UniversityMagdeburgGermany

Personalised recommendations