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System identification of signaling dependent gene expression with different time-scale data

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dc.contributor.author Tsuchiya, Takaho
dc.contributor.author Fujii, Masashi
dc.contributor.author Kunida, Katsuyuki
dc.contributor.author Uda, Shinsuke
dc.contributor.author Kubota, Hiroyuki
dc.contributor.author Konishi, Katsumi
dc.contributor.author Kuroda, Shinya
dc.date.accessioned 2019-02-12T06:09:33Z
dc.date.available 2019-02-12T06:09:33Z
dc.date.issued 2017-12-27
dc.identifier.uri http://hdl.handle.net/10061/13116
dc.description.abstract Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input–output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales. ja_JP
dc.language.iso en ja_JP
dc.publisher Public Library of Science ja_JP
dc.rights © 2017 Tsuchiya et al. ja_JP
dc.title System identification of signaling dependent gene expression with different time-scale data ja_JP
dc.type.nii Journal Article ja_JP
dc.contributor.transcription クニダ, カツユキ
dc.contributor.alternative 国田, 勝行
dc.identifier.fulltexturl https://doi.org/10.1371/journal.pcbi.1005913 ja_JP
dc.textversion publisher ja_JP
dc.identifier.abstracturl https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005913#abstract0 ja_JP
dc.identifier.jtitle PLOS Computational Biology ja_JP
dc.identifier.volume 13 ja_JP
dc.identifier.issue 12 ja_JP
dc.relation.doi info:doi/10.1371/journal.pcbi.1005913 ja_JP
dc.identifier.artnum e1005913 ja_JP
dc.identifier.NAIST-ID 74654278 ja_JP


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