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

Rickert D, Fricker N, Lavrik IN, Theis FJ (2013)

Publication Type: Authored book

Publication year: 2013

Publisher: Springer New York

ISBN: 9781461440093

DOI: 10.1007/978-1-4614-4009-3_3

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.

Involved external institutions

Otto-von-Guericke-Universität Magdeburg DE Germany (DE) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Rickert, D., Fricker, N., Lavrik, I.N., & Theis, F.J. (2013). Systematic complexity reduction of signaling models and application to a CD95 signaling model for apoptosis. Springer New York.

MLA:

Rickert, Dennis, et al. Systematic complexity reduction of signaling models and application to a CD95 signaling model for apoptosis. Springer New York, 2013.

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