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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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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240405T110000
DTEND;TZID=Asia/Seoul:20240405T120000
DTSTAMP:20260522T121420
CREATED:20240219T043532Z
LAST-MODIFIED:20240728T142635Z
UID:9236-1712314800-1712318400@www.ibs.re.kr
SUMMARY:Brian P. Delisle\, Circadian Regulation of Cardiac Electrophysiology
DESCRIPTION:Abstract: Circadian rhythms in physiology and behavior are regulated by circadian clocks\, ubiquitous molecular transcriptional-translational feedback loops that cycle with a periodicity of ~24 hours. Circadian clocks serve as cellular timekeepers regulating important cell-type specific functions. The phase of circadian rhythms and circadian clocks throughout the body are entrained to the light cycle by signals originating in the suprachiasmatic nucleus of the hypothalamus. The functional importance of circadian clocks in cardiomyocytes is underscored by the observation that genetic disruption of the circadian clock mechanism in mouse hearts alters the electrocardiogram (ECG)\, cardiac action potential\, and size of individual ionic currents. This presentation discusses recent basic science studies showing how daily environmental\, behavioral\, and circadian rhythms impact cardiac electrophysiology and cardiac arrhythmogenesis at the systems\, tissue\, and molecular levels. These studies provide new insights into how daily environmental\, behavioral\, and circadian rhythms affect the timing of cardiovascular events\, and they are starting to identify chronotherapeutic strategies that may mitigate the risk for cardiac arrhythmias.
URL:https://www.ibs.re.kr/bimag/event/brian-p-delisle-circadian-regulation-of-cardiac-electrophysiology/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2024/02/Brian-Delisle-e1722176786315.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240412T110000
DTEND;TZID=Asia/Seoul:20240412T120000
DTSTAMP:20260522T121420
CREATED:20240219T043247Z
LAST-MODIFIED:20240728T142452Z
UID:9233-1712919600-1712923200@www.ibs.re.kr
SUMMARY:Michael Chee\, How Data from Sleep Trackers Can Transform Our Understanding of Sleep
DESCRIPTION:Abstract: Wearable health trackers have shifted from gadgets for sports enthusiasts to valuable health sentinels over the last few years and that transformation is gathering pace. What do these devices really measure about sleep? What types of devices are there\, and which can we trust? Which of the many sleep measures reported\, contribute to a better understanding of sleep\, sleep habits and sleep health? How can sleep data improve personal and public health? What new uses of sensor data can we look forward to in coming years? I seek to shed light on these issues in a presentation that will focus on distinguishing scientific and health-oriented perspectives from consumer-facing ones.
URL:https://www.ibs.re.kr/bimag/event/michael-chee-how-data-from-sleep-trackers-can-transform-our-understanding-of-sleep-2/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2024/02/Michael-Chee-e1722176681984.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240419T100000
DTEND;TZID=Asia/Seoul:20240419T120000
DTSTAMP:20260522T121420
CREATED:20240326T142035Z
LAST-MODIFIED:20240415T082050Z
UID:9421-1713520800-1713528000@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, Phenotypic switching in gene regulatory networks
DESCRIPTION:We will discuss about “Phenotypic switching in gene regulatory networks”\, PNAS (2014). \n  \nAbstract \nNoise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype\, the quantification of which is important for understanding cellular decision-making. Here\, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation\, we rigorously show that\, in the limit of slow promoter dynamics\, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks\, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically\, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator\, and to hysteresis in phenotypic induction\, thus highlighting the ability of regulatory networks to retain memory.
URL:https://www.ibs.re.kr/bimag/event/2024-04-19-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 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:20240426T140000
DTEND;TZID=Asia/Seoul:20240426T160000
DTSTAMP:20260522T121420
CREATED:20240326T142526Z
LAST-MODIFIED:20240423T002345Z
UID:9423-1714140000-1714147200@www.ibs.re.kr
SUMMARY:Yun Min Song\, An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells
DESCRIPTION:We will discuss about “An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells”\, ArXiv (2023). \n  \nAbstract \nDetecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology\, rhythms in time series (for instance gene expression\, eclosion\, egg-laying and feeding) datasets tend to be low amplitude\, display large variations amongst replicates\, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method\, ODeGP (Oscillation Detection using Gaussian Processes)\, which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data\, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values\, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next\, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations\, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally\, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells\, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly\, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene\, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation\, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories\, not genome-wide datasets.
URL:https://www.ibs.re.kr/bimag/event/2024-04-26-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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