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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
Chapter

Abstract

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.

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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

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