Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2005 Apr 15;21(8):1626-34. Epub 2004 Dec 21.

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

Author information

  • 1Hans Knoell Institute for Natural Products Research, D-07745 Jena, Beutenbergstrasse 11a, Germany. Reinhard.Guthke@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 newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data.

CONTACT:

Reinhard.Guthke@hki-jena.de.

PMID:
15613398
[PubMed - indexed for MEDLINE]
Free full text
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for HighWire
    Loading ...
    Write to the Help Desk