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PLoS Comput Biol. 2017 Dec 27;13(12):e1005913. doi: 10.1371/journal.pcbi.1005913. eCollection 2017 Dec.

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

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Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, Japan.
Molecular Genetics Research Laboratory, Graduate School of Science, University of Tokyo, Tokyo, Japan.
Laboratory of Computational Biology, Graduate School of Biological Sciences, Nara Institute of Science and Technology, Ikoma, Japan.
Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.
Department of Computer Science, Faculty of Informatics, Kogakuin University, Tokyo, Japan.
CREST, Japan Science and Technology Corporation, Tokyo, Japan.


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.

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