PLoS Comput Biol 13(12):Įditor: Alexander Hoffmann, University of California Los Angeles, UNITED STATES (2017) System identification of signaling dependent gene expression with different time-scale data. Here we developed a signal recovery technique in the field of compressed sensing originally developed for image analysis to biological sparse data of different time scales of signaling and gene expression.Ĭitation: Tsuchiya T, Fujii M, Matsuda N, Kunida K, Uda S, Kubota H, et al. However, no system identification method with such different time scales exists. 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. Here we identified a selective NGF- and PACAP-signaling decoding system by LP gene expression for neurite extension by developing a system identification method. Thus, the decoding mechanism of signaling activities by LP gene expression is a key issue for understanding the mechanism of cell differentiation. Importantly, the expression levels of the LP genes, but not the phosphorylation level of ERK, correlate with neurite length. We previously demonstrated the encoding mechanism of cell fate decision information by transient and sustained ERK activation in PC12 cells, and also identified the decoding genes essential for cell differentiation in PC12 cells, including Metrnl, Dclk1, and Serpinb1a, denoted as LP (latent process) genes, which are the decoders of neurite length information. The key points of this study are two-fold: The first point is the decoding mechanism for cell differentiation. Thus, we provide a versatile method for system identification using data with different time scales. We predicted and validated the identified system using pharmacological perturbation. 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.

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.

Kubota input output model