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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230407T140000
DTEND;TZID=Asia/Seoul:20230407T160000
DTSTAMP:20260425T213339
CREATED:20230331T040259Z
LAST-MODIFIED:20230331T040312Z
UID:7562-1680876000-1680883200@www.ibs.re.kr
SUMMARY:Yun Min Song\, The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms
DESCRIPTION:We will discuss about “The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms”\,Rombouts\, Jan\, Sarah Verplaetse\, and Lendert Gelens.\, bioRxiv (2023) \nAbstract \n\n\n\nMany biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here\, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models\, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay\, of double regulation (acting on production and degradation)\, and of enzymatic degradation.
URL:https://www.ibs.re.kr/bimag/event/2023-04-07-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230414T140000
DTEND;TZID=Asia/Seoul:20230414T160000
DTSTAMP:20260425T213339
CREATED:20230331T040622Z
LAST-MODIFIED:20230413T085616Z
UID:7564-1681480800-1681488000@www.ibs.re.kr
SUMMARY:Hyun Kim\, Comparison of transformations for single-cell RNA-seq data
DESCRIPTION:We will discuss about “Comparison of transformations for single-cell RNA-seq data”\,Ahlmann-Eltze\, Constantin\, and Wolfgang Huber\, Nature Methods (2023): 1-8. \nAbstract \n\n\n\nThe count table\, a numeric matrix of genes × cells\, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here\, we describe four transformation approaches based on the delta method\, model residuals\, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however\, in benchmarks using simulated and real-world data\, it turns out that a rather simple approach\, namely\, the logarithm with a pseudo-count followed by principal-component analysis\, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.
URL:https://www.ibs.re.kr/bimag/event/2023-04-14-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230421T140000
DTEND;TZID=Asia/Seoul:20230421T160000
DTSTAMP:20260425T213339
CREATED:20230331T040917Z
LAST-MODIFIED:20230419T071820Z
UID:7566-1682085600-1682092800@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Improving gene regulatory network inference and assessment: The importance of using network structure
DESCRIPTION:We will discuss about “Improving gene regulatory network inference and assessment: The importance of using network structure”\, Escorcia-Rodríguez\, Juan M.\, et al.\, bioRxiv (2023): 2023-01. \nAbstract \n\n\n\n\nGene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here\, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard\, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods\, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data\, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-04-21-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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