Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection.
dc.contributor.author | Dimitrakopoulou, Konstantina | |
dc.contributor.author | Tsimpouris, Charalampos | |
dc.contributor.author | Papadopoulos, George | |
dc.contributor.author | Pommerenke, Claudia | |
dc.contributor.author | Wilk, Esther | |
dc.contributor.author | Sgarbas, Kyriakos N | |
dc.contributor.author | Schughart, Klaus | |
dc.contributor.author | Bezerianos, Anastasios | |
dc.date.accessioned | 2016-01-20T14:54:04Z | en |
dc.date.available | 2016-01-20T14:54:04Z | en |
dc.date.issued | 2011 | en |
dc.identifier.citation | Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection. 2011, 1:27 J Clin Bioinforma | en |
dc.identifier.issn | 2043-9113 | en |
dc.identifier.pmid | 22017961 | en |
dc.identifier.doi | 10.1186/2043-9113-1-27 | en |
dc.identifier.uri | http://hdl.handle.net/10033/594411 | en |
dc.description.abstract | 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. | |
dc.language.iso | en | en |
dc.title | Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection. | en |
dc.type | Article | en |
dc.contributor.department | Helmholtz Centre for infection research, Inhoffenstr. 7, D-38124 Braunschweig, Germany. | en |
dc.identifier.journal | Journal of clinical bioinformatics | en |
refterms.dateFOA | 2018-06-13T19:56:54Z | |
html.description.abstract | 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. |