Email updates

Keep up to date with the latest news and content from Journal of Clinical Bioinformatics and BioMed Central.

Open Access Methodology

Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

Konstantina Dimitrakopoulou1, Charalampos Tsimpouris2, George Papadopoulos2, Claudia Pommerenke3, Esther Wilk3, Kyriakos N Sgarbas2, Klaus Schughart34 and Anastasios Bezerianos1*

Author Affiliations

1 School of Medicine, University of Patras, Patras 26500, Greece

2 Department of Electrical and Computer Engineering, University of Patras, Patras 26500, Greece

3 Department of Infection Genetics, Helmholtz Centre for Infection Research, Inhoffenstr. 7, D-38124 Braunschweig, Germany

4 University of Veterinary Medicine Hannover, Buenteweg 2, D-30559 Hannover, Germany

For all author emails, please log on.

Journal of Clinical Bioinformatics 2011, 1:27  doi:10.1186/2043-9113-1-27

Published: 21 October 2011

Abstract

Background

The immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.

Results

We applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.

Conclusions

Our results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.

Keywords:
Gene Regulatory Network; Time Varying Dynamic Bayesian Network; Immune System; Influenza A