Quantitative Biology最新文献

{"title":"Quantifying E2F1 protein dynamics in single cells.","authors":"Bernard Mathey-Prevot,&nbsp;Bao-Tran Parker,&nbsp;Carolyn Im,&nbsp;Cierra Hong,&nbsp;Peng Dong,&nbsp;Guang Yao,&nbsp;Lingchong You","doi":"10.1007/s40484-019-0193-6","DOIUrl":"https://doi.org/10.1007/s40484-019-0193-6","url":null,"abstract":"<p><strong>Background: </strong>E2F1 protein, a major effector of the Rb/E2F pathway plays a central role in regulating cell-fate decisions involved in proliferation, apoptosis, and differentiation. Its expression is highly dynamic and tightly modulated through a combination of transcriptional, translational and posttranslational controls. However, the mechanisms by which its expression and activity can promote different cellular outcomes remain to be fully elucidated. To better document E2F1 expression in live cells, we have engineered a series of fluorescent E2F1 protein reporters that quantitatively capture E2F1 protein dynamics.</p><p><strong>Methods: </strong>Reporter constructs, under the control of the mouse or human E2F1 proximal promoter, were designed to express an E2F1-Venus fusion protein incapable of binding DNA. In addition, constructs either included or excluded the 3' untranslated region (3'UTR) of the <i>E2F1</i> gene. These constructs were introduced into fibroblasts and epithelial cells, and expression of the fusion reporter protein was validated and quantified in single cells using live imaging.</p><p><strong>Results: </strong>In all cases, expression of the reporter protein effectively recapitulated the behavior of E2F1 under various conditions, including cell cycle progression and genotoxic stress. No or little fluorescent signal of the reporter was detected in G₀, but as the cycle progressed, expression of the reporter protein steadily increased in the nucleus, peaking a few hours before cell division, but declining to baseline 2-3 h prior to the onset of mitosis. The absence of the E2F1 3'UTR in the constructs led to considerably higher steady-state levels of the fusion protein, which although normally regulated, exhibited a slightly less complex dynamic profile during the cell cycle or genotoxic stress. Lastly, the presence or absence of Rb failed to impact the overall detection and levels of the reporter proteins.</p><p><strong>Conclusions: </strong>Our validated E2F1 protein reporters complement nicely other reporters of the Rb/E2F pathway and provide a unique tool to follow the complex dynamics of E2F1 expression in real time in single cells.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 1","pages":"20-30"},"PeriodicalIF":3.1,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-019-0193-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38047393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chao Tang: a self-motivated scientist pursues interdisciplinary excellence","authors":"Ya-Yuan Huang, Yimeng Ye, Jiang Zhang","doi":"10.1007/s40484-020-0198-1","DOIUrl":"https://doi.org/10.1007/s40484-020-0198-1","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 1","pages":"1-3"},"PeriodicalIF":3.1,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0198-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46843924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of a CRISPR-based paired-sgRNA library for chromosomal deletion of long non-coding RNAs","authors":"Minzhen Tao, Qiaochu Mu, Yurui Zhang, Zhen Xie","doi":"10.1007/s40484-020-0194-5","DOIUrl":"https://doi.org/10.1007/s40484-020-0194-5","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 1","pages":"31-42"},"PeriodicalIF":3.1,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0194-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41852931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination of versatile platforms for the development of synthetic biology","authors":"Baizhu Chen, Zhuojun Dai","doi":"10.1007/s40484-020-0197-2","DOIUrl":"https://doi.org/10.1007/s40484-020-0197-2","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 1","pages":"4-10"},"PeriodicalIF":3.1,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0197-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45721415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZokorDB: tissue specific regulatory network annotation for non-coding elements of plateau zokor","authors":"Jingxue Xin, Junjun Hao, Lang Chen, Tao Zhang, Lei Li, Luonan Chen, Wenming Zhao, Xuemei Lu, Peng Shi, Yong Wang","doi":"10.1007/s40484-020-0195-4","DOIUrl":"https://doi.org/10.1007/s40484-020-0195-4","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 1","pages":"43-50"},"PeriodicalIF":3.1,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0195-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48873282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-chain Fudan-CCDC model for COVID-19-a revisit to Singapore's case.","authors":"Hanshuang Pan,&nbsp;Nian Shao,&nbsp;Yue Yan,&nbsp;Xinyue Luo,&nbsp;Shufen Wang,&nbsp;Ling Ye,&nbsp;Jin Cheng,&nbsp;Wenbin Chen","doi":"10.1007/s40484-020-0224-3","DOIUrl":"https://doi.org/10.1007/s40484-020-0224-3","url":null,"abstract":"<p><strong>Background: </strong>COVID-19 has been impacting on the whole world critically and constantly since late December 2019. Rapidly increasing infections has raised intense worldwide attention. How to model the evolution of COVID-19 effectively and efficiently is of great significance for prevention and control.</p><p><strong>Methods: </strong>We propose the multi-chain Fudan-CCDC model based on the original single-chain model in [Shao et al. 2020] to describe the evolution of COVID-19 in Singapore. Multi-chains can be considered as the superposition of several single chains with different characteristics. We identify the parameters of models by minimizing the penalty function.</p><p><strong>Results: </strong>The numerical simulation results exhibit the multi-chain model performs well on data fitting. Though unsteady the increments are, they could still fall within the range of _30% fluctuation from simulation results.</p><p><strong>Conclusion: </strong>The multi-chain Fudan-CCDC model provides an effective way to early detect the appearance of imported infectors and super spreaders and forecast a second outbreak. It can also explain the data from those countries where the single-chain model shows deviation from the data.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 4","pages":"325-335"},"PeriodicalIF":3.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-020-0224-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38312867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential methylation analysis for bisulfite sequencing using DSS.","authors":"Hao Feng,&nbsp;Hao Wu","doi":"10.1007/s40484-019-0183-8","DOIUrl":"https://doi.org/10.1007/s40484-019-0183-8","url":null,"abstract":"<p><p>Bisulfite sequencing (BS-seq) technology measures DNA methylation at single nucleotide resolution. A key task in BS-seq data analysis is to identify differentially methylation (DM) under different conditions. Here we provide a tutorial for BS-seq DM analysis using Bioconductor package DSS. DSS uses a beta-binomial model to characterize the sequence counts from BS-seq, and implements rigorous statistical method for hypothesis testing. It provides flexible functionalities for a variety of DM analyses.</p>","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"7 4","pages":"327-334"},"PeriodicalIF":3.1,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-019-0183-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38979346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International Workshop on Applications of Probability and Statistics to Biology, July 11–13, 2019","authors":"Minghua Deng, Jianfeng Feng, Hong Qian, Lin Wan, Fengzhu Sun","doi":"10.1007/s40484-019-0182-9","DOIUrl":"https://doi.org/10.1007/s40484-019-0182-9","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"8 1","pages":"177-186"},"PeriodicalIF":3.1,"publicationDate":"2019-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-019-0182-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42598989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging deep learning methods for single-cell RNA-seq data analysis","authors":"Jie Zheng, Ke Wang","doi":"10.1007/s40484-019-0189-2","DOIUrl":"https://doi.org/10.1007/s40484-019-0189-2","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"7 1","pages":"247 - 254"},"PeriodicalIF":3.1,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-019-0189-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44179762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of some tensor analysis techniques for biological systems","authors":"Farzane Yahyanejad, R. Albert, B. Dasgupta","doi":"10.1007/s40484-019-0186-5","DOIUrl":"https://doi.org/10.1007/s40484-019-0186-5","url":null,"abstract":"","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":"7 1","pages":"266 - 277"},"PeriodicalIF":3.1,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40484-019-0186-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46061971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}