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Rickert, D. ; Fricker, N. ; Lavrik, I.N.* ; Theis, F.J.

Systematic complexity reduction of signaling models and application to a CD95 signaling model for apoptosis.

In: Systems Biology of Apoptosis. New York: Springer, 2013. 57-84
DOI
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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Publication type Article: Edited volume or book chapter
Editors Lavrik, I.N.*
Corresponding Author
ISBN 978-1-4614-4008-6
Book Volume Title Systems Biology of Apoptosis
Quellenangaben Volume: , Issue: , Pages: 57-84 Article Number: , Supplement: ,
Publisher Springer
Publishing Place New York
Non-patent literature Publications
Institute(s) Institute of Bioinformatics and Systems Biology (IBIS)