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dc.contributor.authorDoni, Jayavelu Naresh
dc.contributor.authorBar, Nadav
dc.date.accessioned2015-11-24T13:00:22Z
dc.date.accessioned2015-12-04T09:57:23Z
dc.date.available2015-11-24T13:00:22Z
dc.date.available2015-12-04T09:57:23Z
dc.date.issued2014
dc.identifier.citationPLoS ONE 2014, 9(1)nb_NO
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/11250/2366930
dc.description.abstractUnderstanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.nb_NO
dc.language.isoengnb_NO
dc.publisherPublic Library of Sciencenb_NO
dc.titleDynamics of Regulatory Networks in Gastrin-Treated Adenocarcinoma Cellsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer revieweden_GB
dc.date.updated2015-11-24T13:00:22Z
dc.source.volume9nb_NO
dc.source.journalPLoS ONEnb_NO
dc.source.issue1nb_NO
dc.identifier.doi10.1371/journal.pone.0078349
dc.identifier.cristin1128290
dc.description.localcode© 2014 Doni Jayavelu, Bar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.nb_NO


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