ArticleJournal of clinical bioinformatics2011
Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection.
Article in Journal of clinical bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data.BMC bioinformatics · 2020Article
- Meeting report of the European mouse complex genetics network SYSGENET.Mammalian genome : official journal of the International Mammalian Genome Society · 2013Article
- Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses.PloS one · 2012Article
- Reverse engineering: a key component of systems biology to unravel global abiotic stress cross-talk.Frontiers in plant science · 2012Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
resultsWe 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.
conclusionsOur 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.
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Registered trials
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