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Bioinformatics Advance Access published online on December 21, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti226
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received September 3, 2004
Revised November 9, 2004
Accepted December 13, 2004

Article

Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection

Reinhard Guthke 1*, Ulrich Möller 1, Martin Hoffmann 1, Frank Thies 1, and Susanne Töpfer 2

1 Hans Knoell Institute for Natural Products Research, D-07745 Jena, Beutenbergstr. 11a, Germany
2 BioControl Jena GmbH, D-07745 Jena, Wildenbruchstr. 15, Germany

* To whom correspondence should be addressed.
Reinhard Guthke, E-mail: Reinhard.Guthke{at}hki-jena.de


  Abstract

Motivation: The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both, microarray data and available biological knowledge.

Results: The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures Singular Value Decomposition (SVD) based methods and a here newly introduced heuristic Network Generation Method were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data.


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