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Proceedings Paper

Gene regulatory networks simplified by nonlinear balanced truncation
Author(s): Anke Meyer-Bäse; Fabian Theis
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Paper Abstract

The complexity of gene regulatory networks described by coupled nonlinear differential equations is often an obstacle for analysis purposes. Therefore, the development of effective model reduction techniques is of paramount importance in the field of systems biology. In this paper, we apply the theory of nonlinear balanced truncation for model reduction for gene regulatory networks based only on standard matrix computations. The method is based on finding a controllability and observability function of the nonlinear system and thus obtain a balanced representation that produces singular value functions which are functions of the state. As a result, we obtain a ranked contribution of the states from an input - output perspective.

Paper Details

Date Published: 16 April 2008
PDF: 8 pages
Proc. SPIE 6979, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI, 69790C (16 April 2008); doi: 10.1117/12.777292
Show Author Affiliations
Anke Meyer-Bäse, Florida State Univ. (United States)
Fabian Theis, Helmholtz Ctr. Munich (Germany)


Published in SPIE Proceedings Vol. 6979:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks VI
Harold H. Szu; F. Jack Agee, Editor(s)

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