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LEE, GYUN MIN,SAVINELL, JOANNE,PALSSON, BERNHARD O. 한국미생물 · 생명공학회 1992 Journal of microbiology and biotechnology Vol.2 No.2
Physiological changes of the murine hybridoma cell line S3H5/γ2bA2 during adaptation to RPMI 1640 medium with 1%(v/v) fetal bovine serum were characterized in terms of cell growth, antibody production, morphology, and metabolic quotients. Cells adapted to 1% serum medium in T-flasks became sensitive to shear induced by mechanical agitation and required at least 5% serum in the medium or spent medium for cell growth in spinner flasks, while cells adapted to 10% serum medium in T-flasks could grow in 1% serum medium in spinner flasks. Consequently, long-term adaptation to low serum media may not give the expected growth enhancement. After adaptation to 1% serum medium, changes in cell morphology were observed. The cells in 10% serum medium were uniform and circular, while cells in 1% medium were irregularly shaped. The DNA contents, which were measured by flow cytometry, were almost constant among the cells in the range of 1% to 10%. Further, no significant changes in energy metabolism and specific monoclonal antibody production rate were observed among these cells.
Prediction of Transcription Factors Using a Deep Learning-based Tool, DeepTFactor
Gi Bae KIM,Ye GAO,Bernhard O. PALSSON,Gaeun PARK,Sang Yup LEE 한국생물공학회 2021 한국생물공학회 학술대회 Vol.2021 No.10
Transcription factor (TF), a sequence-specific DNA-binding protein that regulates the transcription, was hard to predict if it shows no sequence homology with identified TFs. Here we developed DeepTFactor, a deep learning-based tool for the prediction of TFs, applying a convolutional neural network that has three subnetworks in parallel. It successfully predicted TFs of both eukaryotic and prokaryotic origins. Analysis of gradients of prediction score for input showed DeepTFactor detects DNA-binding domains and other latent features. DeepTFactor predicted 332 candidate TFs in Escherichia coli K-12 ㎎1655, and three of them were validated by identifying genome-wide binding sites with ChIP-exo experiments. We provide DeepTFactor as a stand-alone program and made the list of 4,674,808 TFs from 73,873,012 protein sequences in 48,346 genomes. DeepTFactor will be useful for identifying the regulatory system.
Systematic Analysis on Transcription Unit Architecture of Streptomyces lividans TK24
이용재,이남일,정유진,황순규,김우리,조수형,( Bernhard O. Palsson ),조병관 한국공업화학회 2019 한국공업화학회 연구논문 초록집 Vol.2019 No.1
Streptomyces lividans is an attractive host for heterologous production of proteins and secondary metabolites. In this study, the transcription unit (TU) architecture of S. lividans was elucidated by integrating four high-throughput data types, including dRNA-Seq, Term-Seq, RNA-Seq and Ribo-Seq. Total 1,300 TUs and the corresponding regulatory elements were elucidated from 1,978 transcription start sites and 1,640 transcript 3'-end positions. The TU information and regulatory elements identified will serve as invaluable resources for understanding the regulatory mechanisms of S. lividans and to elevate its industrial potential. <sup>**</sup> This work was supported by the Novo Nordisk Foundation (NNF10CC1016517). This work was also supported by the Intelligent Synthetic Biology Center of Global Frontier Project (2011-0031957) and the Bio & Medical Technology Development Program (2018M3A9F3079664) through the National Research Foundation of Korea funded by the Ministry of Science and ICT.