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

2.50
Hdl Handle:
http://hdl.handle.net/10033/620995
Title:
Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection
Authors:
Dimitrakopoulou, Konstantina; Tsimpouris, Charalampos; Papadopoulos, George; Pommerenke, Claudia; Wilk, Esther; Sgarbas, Kyriakos N; Schughart, Klaus; Bezerianos, Anastasios
Abstract:
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.
Citation:
Journal of Clinical Bioinformatics. 2011 Oct 21;1(1):27
Issue Date:
21-Oct-2011
URI:
http://dx.doi.org/10.1186/2043-9113-1-27; http://hdl.handle.net/10033/620995
Type:
Journal Article
Appears in Collections:
publications of the department infection genetics (INFG)

Full metadata record

DC FieldValue Language
dc.contributor.authorDimitrakopoulou, Konstantinaen
dc.contributor.authorTsimpouris, Charalamposen
dc.contributor.authorPapadopoulos, Georgeen
dc.contributor.authorPommerenke, Claudiaen
dc.contributor.authorWilk, Estheren
dc.contributor.authorSgarbas, Kyriakos Nen
dc.contributor.authorSchughart, Klausen
dc.contributor.authorBezerianos, Anastasiosen
dc.date.accessioned2017-07-05T13:45:15Z-
dc.date.available2017-07-05T13:45:15Z-
dc.date.issued2011-10-21en
dc.identifier.citationJournal of Clinical Bioinformatics. 2011 Oct 21;1(1):27en
dc.identifier.urihttp://dx.doi.org/10.1186/2043-9113-1-27en
dc.identifier.urihttp://hdl.handle.net/10033/620995-
dc.description.abstractAbstract 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.en
dc.titleDynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infectionen
dc.typeJournal Articleen
dc.language.rfc3066enen
dc.rights.holderDimitrakopoulou et al; licensee BioMed Central Ltd.en
dc.date.updated2015-09-04T08:23:21Zen
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