Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection.
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.
School of Medicine, University of Patras, Patras 26500, Greece. bezer@upatras.gr.
Konstantina
K
Dimitrakopoulou
Charalampos
C
Tsimpouris
George
G
Papadopoulos
Claudia
C
Pommerenke
Esther
E
Wilk
Kyriakos N
KN
Sgarbas
Klaus
K
Schughart
Anastasios
A
Bezerianos