tailieunhanh - Báo cáo y học: " Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection."

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection. | Dimitrakopoulou et al. Journal of Clinical Bioinformatics 2011 1 27 http content 1 1 27 JOURNAL OF CLINICAL BIOINFORMATICS METHODOLOGY Open Access Dynamic gene network reconstruction from gene expression data in mice after influenza A H1N1 infection Konstantina Dimitrakopoulou1 Charalampos Tsimpouris2 George Papadopoulos2 Claudia Pommerenke3 Cr h w A ỉ 11 3 LA rỉ S r l I 0 z i r Sr 2 I I I I f- O hi Iz t L s r f 3 4 M A r 1 r i r I r 1 Esther Wilk Kyriakos N Sgarbas Klaus SChughart and Anastasios Bezerianos 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 Background It is now well established that the study of .