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CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250328T110000
DTEND;TZID=Asia/Seoul:20250328T120000
DTSTAMP:20260422T203036
CREATED:20250217T075911Z
LAST-MODIFIED:20250217T081432Z
UID:10766-1743159600-1743163200@www.ibs.re.kr
SUMMARY:Dynamics and Decision Making in Single Cells - Galit Lahav
DESCRIPTION:Abstract \nIndividual human cancer cells often show different responses to the same treatment. In this talk I will share the quantitative experimental approaches my lab has developed for studying the fate and behavior of human cells at the single-cell level. I will focus on the tumor suppressor protein p53\, a transcription factor controlling genomic integrity and cell survival. In the last several years we have established the dynamics of p53 (changes in its levels over time) as an important mechanism controlling gene expression and guiding cellular outcomes. I will present recent studies from the lab demonstrating how studying p53 dynamics in response to radiation and chemotherapy in single cells can guide the design and schedule of combinatorial therapy\, and how the p53 oscillator can be used to study the principles and function of entertainment in Biology. I will also present new findings suggesting that p53’s post-translational modification state is altered between its first and second pulses of expression\, and the effects these have on gene expression programs over time.
URL:https://www.ibs.re.kr/bimag/event/dynamics-and-decision-making-in-single-cells-galit-lahav/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/02/Galit-Lahav-e1739779209180.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250321T143000
DTEND;TZID=Asia/Seoul:20250321T163000
DTSTAMP:20260422T203036
CREATED:20250226T070501Z
LAST-MODIFIED:20250314T140235Z
UID:10811-1742567400-1742574600@www.ibs.re.kr
SUMMARY:Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Designing microplastic-binding peptides with a variational quantum circuit–based hybrid quantum-classical approach” by R.C. Vendrell et.al.\, Sci. Adv. 2024 at the Journal Club. \nAbstract \nDe novo peptide design exhibits great potential in materials engineering\, particularly for the use of plastic-binding peptides to help remediate microplastic pollution. There are no known peptide binders for many plastics—a gap that can be filled with de novo design. Current computational methods for peptide design exhibit limitations in sampling and scaling that could be addressed with quantum computing. Hybrid quantum-classical methods can leverage complementary strengths of near-term quantum algorithms and classical techniques for complex tasks like peptide design. This work introduces a hybrid quantum-classical generative framework for designing plastic-binding peptides combining variational quantum circuits with a variational autoencoder network. We demonstrate the framework’s effectiveness in generating peptide candidates\, evaluate its efficiency for property-oriented design\, and validate the candidates with molecular dynamics simulations. This quantum computing–based approach could accelerate the development of biomolecular tools for environmental and biomedical applications while advancing the study of biomolecular systems through quantum technologies. \n 
URL:https://www.ibs.re.kr/bimag/event/phantom-oscillations-in-principal-component-analysis-gyuyoung-hwang/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250321T110000
DTEND;TZID=Asia/Seoul:20250321T120000
DTSTAMP:20260422T203036
CREATED:20250217T075507Z
LAST-MODIFIED:20250217T075934Z
UID:10756-1742554800-1742558400@www.ibs.re.kr
SUMMARY:Disrupting Heathcare Using Deep Data and Remote Monitoring - Michael Snyder
DESCRIPTION:Abstract \nOur present healthcare system focuses on treating people when they are ill rather than keeping them healthy. We have been using big data and remote monitoring approaches to monitor people while they are healthy to keep them that way and detect disease at its earliest moment presymptomatically. We use advanced multiomics technologies (genomics\, immunomics\, transcriptomics\, proteomics\, metabolomics\, microbiomics) as well as wearables and microsampling for actively monitoring health. Following a group of 109 individuals for over 13 years revealed numerous major health discoveries covering cardiovascular disease\, oncology\, metabolic health and infectious disease. We have also found that individuals have distinct aging patterns that can be measured in an actionable period of time. Finally\, we have used wearable devices for early detection of infectious disease\, including COVID-19 as well as microsampling for monitoring and improving lifestyle. We believe that advanced technologies have the potential to transform healthcare and keep people healthy.
URL:https://www.ibs.re.kr/bimag/event/disrupting-heathcare-using-deep-data-and-remote-monitoring-michael-snyder/
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/2025/02/mike-snyder-e1739778881131.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250314T140000
DTEND;TZID=Asia/Seoul:20250314T160000
DTSTAMP:20260422T203036
CREATED:20250226T070011Z
LAST-MODIFIED:20250226T070011Z
UID:10806-1741960800-1741968000@www.ibs.re.kr
SUMMARY:A biological model of nonlinear dimensionality reduction - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “A biological model of nonlinear dimensionality reduction” by K. Yoshida and T. Toyoizumi\, Science Advances\, 2025\, at the Journal Club. \nAbstract \nObtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) have been developed\, their implementation in simple biological circuits remains unclear. Here\, we develop a biologically plausible dimensionality reduction algorithm compatible with t-SNE\, which uses a simple three-layer feedforward network mimicking the Drosophila olfactory circuit. The proposed learning rule\, described as three-factor Hebbian plasticity\, is effective for datasets such as entangled rings and MNIST\, comparable to t-SNE. We further show that the algorithm could be working in olfactory circuits in Drosophila by analyzing the multiple experimental data in previous studies. We lastly suggest that the algorithm is also beneficial for association learning between inputs and rewards\, allowing the generalization of these associations to other inputs not yet associated with rewards.
URL:https://www.ibs.re.kr/bimag/event/a-biological-model-of-nonlinear-dimensionality-reduction-shingo-gibo/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250314T110000
DTEND;TZID=Asia/Seoul:20250314T120000
DTSTAMP:20260422T203036
CREATED:20250217T075146Z
LAST-MODIFIED:20250217T075146Z
UID:10749-1741950000-1741953600@www.ibs.re.kr
SUMMARY:COVID-19 and Challenges to the Classical Theory of Epidemics - Simon Levin
DESCRIPTION:Abstract \nThe standard theory of infectious diseases\, tracing back to the work of Kermack and McKendrick nearly a century ago\, has been a triumph of mathematical biology\, a rare marriage of theory and application. Yet the limitations of its most simple representations\, which has always been known\, have been laid bare in dealing with COVID-19\, sparking a spate of extensions of the basic theory to deal more effectively with aspects of viral evolution\, asymptotic stages\, heterogeneity of various kinds\, the ambiguities of notions of herd immunity\, the role of social behaviors and other features. This lecture will address some progress in addressing these\, and open challenges in expanding the mathematical theory.
URL:https://www.ibs.re.kr/bimag/event/covid-19-and-challenges-to-the-classical-theory-of-epidemics-simon-levin/
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/2025/02/simon-levin-e1739778689468.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250307T140000
DTEND;TZID=Asia/Seoul:20250307T160000
DTSTAMP:20260422T203036
CREATED:20250226T065718Z
LAST-MODIFIED:20250305T000149Z
UID:10804-1741356000-1741363200@www.ibs.re.kr
SUMMARY:The Large Language Models on Biomedical Data Analysis: A Survey - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “The Large Language Models on Biomedical Data Analysis: A Survey” by Wei Lan et.al\, IEEE J. Biomedical and Health Informatics\, 2025\, at the Journal Club. \nAbstract  \nWith the rapid development of Large Language Model (LLM) technology\, it has become an indispensable force in biomedical data analysis research. However\, biomedical researchers currently have limited knowledge about LLM. Therefore\, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein\, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review\, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis\, followed by a detailed analysis of LLM applications in genomics\, proteomics\, transcriptomics\, radiomics\, single-cell analysis\, medical texts and drug discovery. Finally\, the challenges of LLM in biomedical data analysis are discussed. In summary\, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research.
URL:https://www.ibs.re.kr/bimag/event/machine-learning-model-for-menstrual-cycle-phase-classification-and-ovulation-day-detection-based-on-sleeping-heart-rate-under-free-living-conditions-myna-lim/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250306T130000
DTEND;TZID=Asia/Seoul:20250306T153000
DTSTAMP:20260422T203036
CREATED:20250226T071803Z
LAST-MODIFIED:20250226T072211Z
UID:10813-1741266000-1741275000@www.ibs.re.kr
SUMMARY:심리학이 알려주는 연구의 기술: 논리적 사고부터 논문 작성까지 - 서수연
DESCRIPTION:본 세미나에서는 성신여자대학교 서수연 교수님께서 “심리학이 알려주는 연구의 기술: 논리적 사고부터 논문 작성까지”라는 내용으로 강연을 해주실 예정입니다. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/psychology-and-research/
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250228T140000
DTEND;TZID=Asia/Seoul:20250228T160000
DTSTAMP:20260422T203036
CREATED:20250220T082847Z
LAST-MODIFIED:20250225T080719Z
UID:10787-1740751200-1740758400@www.ibs.re.kr
SUMMARY:Quantifying information accumulation encoded in the dynamics of biochemical signaling - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Quantifying information accumulation encoded in the dynamics of biochemical signaling” by Y. Tang\, et.al\, Nature Communications\, 2021. \nAbstract \nCellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However\, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is\, in part\, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here\, we develop a quantitative framework\, based on inferred trajectory probabilities\, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats\, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements\, and enables understanding how temporal regulatory codes transmit information over time.
URL:https://www.ibs.re.kr/bimag/event/quantum-computing-enhanced-algorithm-unveils-potential-kras-inhibitors-kang-min-lee/
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:20250221T140000
DTEND;TZID=Asia/Seoul:20250221T160000
DTSTAMP:20260422T203036
CREATED:20250128T024716Z
LAST-MODIFIED:20250203T004930Z
UID:10712-1740146400-1740153600@www.ibs.re.kr
SUMMARY:Constraining nonlinear time series modeling with the metabolic theory of ecology - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Constraining nonlinear time series modeling with the metabolic theory of ecology” by S.B. Munch et.al.\, PNAS\, 2023. \nAbstract \nForecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature\, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM)\, an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a “metabolic time step\,” our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average)\, with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate\, rather than the form\, of population dynamics\, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable\, at least approximately\, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.
URL:https://www.ibs.re.kr/bimag/event/constraining-nonlinear-time-series-modeling-with-the-metabolic-theory-of-ecology-olive-cawiding/
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:20250214T140000
DTEND;TZID=Asia/Seoul:20250214T160000
DTSTAMP:20260422T203036
CREATED:20250128T024512Z
LAST-MODIFIED:20250203T004838Z
UID:10710-1739541600-1739548800@www.ibs.re.kr
SUMMARY:Method for cycle detection in sparse\, irregularly sampled\, long-term neuro-behavioral timeseries - Brenda Gavina
DESCRIPTION:In this talk\, we discuss the paper “Method for cycle detection in sparse\, irregularly sampled\, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term\, inter-ictal epileptiform activity” by Irena Balzekas et.al.\, Plos Com.\, 2024. \nAbstract \nNumerous physiological processes are cyclical\, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep\, wakefulness\, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans\, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases\, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals.
URL:https://www.ibs.re.kr/bimag/event/method-for-cycle-detection-in-sparse-irregularly-sampled-long-term-neuro-behavioral-timeseries-brenda-gavina/
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:20250207T140000
DTEND;TZID=Asia/Seoul:20250207T160000
DTSTAMP:20260422T203036
CREATED:20250128T024238Z
LAST-MODIFIED:20250206T103822Z
UID:10708-1738936800-1738944000@www.ibs.re.kr
SUMMARY:A cell atlas foundation model for scalable search of similar human cells - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “A cell atlas foundation model for scalable search of similar human cells” by Graham Heimberg et.al.\, Nature\, 2024 at the Journal Club. \nAbstract \n\n\nSingle-cell RNA sequencing has profiled hundreds of millions of human cells across organs\, diseases\, development and perturbations to date. Mining these growing atlases could reveal cell–disease associations\, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity\, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2\, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body\, providing a powerful tool for generating biological insights from the Human Cell Atlas.
URL:https://www.ibs.re.kr/bimag/event/scdiffusion-conditional-generation-of-high-quality-single-cell-data-using-diffusion-model-kevin-spinicci/
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:20250131T140000
DTEND;TZID=Asia/Seoul:20250131T160000
DTSTAMP:20260422T203036
CREATED:20250126T021153Z
LAST-MODIFIED:20250203T004702Z
UID:10696-1738332000-1738339200@www.ibs.re.kr
SUMMARY:Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Self-supervised learning of accelerometer data provides new insights for sleep and\nits association with mortality” by H. Yuan et.al\, npj digital medicine\, 2024\, at the Journal Club. \nAbstract  \nSleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus\, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion\, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration\, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100\,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66\,262 UK Biobank participants\, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h\, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.
URL:https://www.ibs.re.kr/bimag/event/self-supervised-learning-of-accelerometer-data-provides-new-insights-for-sleep-and-its-association-with-mortality-yun-min-song/
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:20250124T140000
DTEND;TZID=Asia/Seoul:20250124T160000
DTSTAMP:20260422T203036
CREATED:20250104T005711Z
LAST-MODIFIED:20250104T005711Z
UID:10531-1737727200-1737734400@www.ibs.re.kr
SUMMARY:Plausible\, robust biological oscillations through allelic buffering - Eui Min Jeong
DESCRIPTION:In this talk\, we discuss the paper “Plausible\, robust biological oscillations through allelic buffering” by F-S. Hsieh et.al\, Cell Systems\, 2024. at the Journal Club.  \nAbstract \nBiological oscillators can specify time- and dose-dependent functions via dedicated control of their oscillatory dynamics. However\, how biological oscillators\, which recurrently activate noisy biochemical processes\, achieve robust oscillations remains unclear. Here\, we characterize the long-term oscillations of p53 and its negative feedback regulator Mdm2 in single cells after DNA damage. Whereas p53 oscillates regularly\, Mdm2 from a single MDM2 allele exhibits random unresponsiveness to ∼9% of p53 pulses. Using allelic-specific imaging of MDM2 activity\, we show that MDM2 alleles buffer each other to maintain p53 pulse amplitude. Removal of MDM2 allelic buffering cripples the robustness of p53 amplitude\, thereby elevating p21 levels and cell-cycle arrest. In silico simulations support that allelic buffering enhances the robustness of biological oscillators and broadens their plausible biochemical space. Our findings show how allelic buffering ensures robust p53 oscillations\, highlighting the potential importance of allelic buffering for the emergence of robust biological oscillators during evolution. A record of this paper’s transparent peer review process is included in the supplemental information. 
URL:https://www.ibs.re.kr/bimag/event/plausible-robust-biological-oscillations-through-allelic-buffering-eui-min-jeong/
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:20250114T110000
DTEND;TZID=Asia/Seoul:20250114T120000
DTSTAMP:20260422T203036
CREATED:20241231T015243Z
LAST-MODIFIED:20250109T125003Z
UID:10499-1736852400-1736856000@www.ibs.re.kr
SUMMARY:Biomolecular Condensates: Principles and Models\, Jeong-Mo Choi
DESCRIPTION:Over the past decade\, the phase behavior of biomolecules has garnered significant attention\, particularly due to its biological implications\, such as the reversible formation and dissociation of biomolecular condensates. These condensates perform diverse and essential functions within cells\, including the acceleration of chemical reactions. Recent advances aim to uncover the fundamental principles of these systems and harness them as tools for engineering cellular processes in synthetic biology. In this talk\, I will discuss the key principles that govern the behaviors of biomolecular condensates and introduce several (semi-)analytical models that provide both qualitative insights and quantitative predictions. These models serve as a foundation for understanding and leveraging condensate-driven phenomena in biological systems.
URL:https://www.ibs.re.kr/bimag/event/tbd-jeong-mo-choi/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250110T140000
DTEND;TZID=Asia/Seoul:20250110T160000
DTSTAMP:20260422T203036
CREATED:20250104T003730Z
LAST-MODIFIED:20250107T122054Z
UID:10529-1736517600-1736524800@www.ibs.re.kr
SUMMARY:CARE as a wearable derived feature linking circadian amplitude to human cognitive functions - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “CARE as a wearable derived feature linking circadian amplitude to human cognitive functions” by Shuya Cui et.al.\, npj Digital Medicine\, 2023. \nAbstract \nCircadian rhythms are crucial for regulating physiological and behavioral processes. Pineal hormone melatonin is often used to measure circadian amplitude but its collection is costly and time-consuming. Wearable activity data are promising alternative\, but the most commonly used measure\, relative amplitude\, is subject to behavioral masking. In this study\, we firstly derive a feature named circadian activity rhythm energy (CARE) to better characterize circadian amplitude and validate CARE by correlating it with melatonin amplitude (Pearson’s r = 0.46\, P = 0.007) among 33 healthy participants. Then we investigate its association with cognitive functions in an adolescent dataset (Chinese SCHEDULE-A\, n = 1703) and an adult dataset (UK Biobank\, n = 92\,202)\, and find that CARE is significantly associated with Global Executive Composite (β = 30.86\, P = 0.016) in adolescents\, and reasoning ability\, short-term memory\, and prospective memory (OR = 0.01\, 3.42\, and 11.47 respectively\, all P < 0.001) in adults. Finally\, we identify one genetic locus with 126 CARE-associated SNPs using the genome-wide association study\, of which 109 variants are used as instrumental variables in the Mendelian Randomization analysis\, and the results show a significant causal effect of CARE on reasoning ability\, short-term memory\, and prospective memory (β = -59.91\, 7.94\, and 16.85 respectively\, all P < 0.0001). The present study suggests that CARE is an effective wearable-based metric of circadian amplitude with a strong genetic basis and clinical significance\, and its adoption can facilitate future circadian studies and potential intervention strategies to improve circadian rhythms and cognitive functions.
URL:https://www.ibs.re.kr/bimag/event/mapping-the-physiological-changes-in-sleep-regulation-across-infancy-and-young-childhood-dongju-lim/
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:20250103T140000
DTEND;TZID=Asia/Seoul:20250103T160000
DTSTAMP:20260422T203036
CREATED:20250101T061847Z
LAST-MODIFIED:20250101T061847Z
UID:10503-1735912800-1735920000@www.ibs.re.kr
SUMMARY:Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model - Seokhwan Moon
DESCRIPTION:In this talk\, we discuss the paper “Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model” by F. W. Townes et.al.\, Genome Biology\, 2019. \nAbstract  \nSingle-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls\, we show UMI counts follow multinomial sampling with no zero inflation. Current normalization procedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We propose simple multinomial methods\, including generalized principal component analysis (GLM-PCA) for non-normal distributions\, and feature selection using deviance. These methods outperform the current practice in a downstream clustering assessment using ground truth datasets.
URL:https://www.ibs.re.kr/bimag/event/feature-selection-and-dimension-reduction-for-single-cell-rna-seq-based-on-a-multinomial-model-seokhwan-moon/
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:20241230T110000
DTEND;TZID=Asia/Seoul:20241230T120000
DTSTAMP:20260422T203036
CREATED:20241222T065319Z
LAST-MODIFIED:20241222T065404Z
UID:10424-1735556400-1735560000@www.ibs.re.kr
SUMMARY:Enhanced Gaussian Process Surrogates for Optimization and Sampling by Pure Exploration - Hwanwoo Kim
DESCRIPTION:Abstract: \nIn this talk\, we propose novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm\, but the additional random exploration step accelerates their convergence\, nearly achieving the optimal convergence rate. Furthermore\, to facilitate Bayesian inference with an intractable likelihood\, we propose to utilize the optimization iterates as design points to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. The effectiveness of our algorithms is demonstrated in benchmark non-convex test functions for optimization\, and in a black-box engineering design problem. We also showcase the effectiveness of our posterior approximation approach in Bayesian inference for parameters of dynamical systems.
URL:https://www.ibs.re.kr/bimag/event/enhanced-gaussian-process-surrogates-for-optimization-and-sampling-by-pure-exploration-hwanwoo-kim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241227T100000
DTEND;TZID=Asia/Seoul:20241227T120000
DTSTAMP:20260422T203036
CREATED:20241022T001840Z
LAST-MODIFIED:20241226T235355Z
UID:10197-1735293600-1735300800@www.ibs.re.kr
SUMMARY:Diffusion Posterior Sampling for Linear Inverse Problem Solving- A Filtering Perspective - U Jin Choi
DESCRIPTION:In this talk\, we discuss the paper : “Diffusion Posterior Sampling for Linear Inverse Problem Solving- A Filtering Perspective” by Z. Dou& Y. Song \n\n\nDiffusion models have achieved tremendous success in generating high-dimensional data like images\, videos and audio. These models provide powerful data priors that can solve linear inverse problems in zero shot through Bayesian posterior sampling. However\, exact posterior sampling for diffusion models is intractable. Current solutions often hinge on approximations that are either computationally expensive or lack strong theoretical guarantees. In this work\, we introduce an efficient diffusion sampling algorithm for linear inverse problems that is guaranteed to be asymptotically accurate. We reveal a link between Bayesian posterior sampling and Bayesian filtering in diffusion models\, proving the former as a specific instance of the latter. Our method\, termed filtering posterior sampling\, leverages sequential Monte Carlo methods to solve the corresponding filtering problem. It seamlessly integrates with all Markovian diffusion samplers\, requires no model re-training\, and guarantees accurate samples from the Bayesian posterior as particle counts rise. Empirical tests demonstrate that our method generates better or comparable results than leading zero-shot diffusion posterior samplers on tasks like image inpainting\, super-resolution\, and deblurring.
URL:https://www.ibs.re.kr/bimag/event/diffusion-posterior-sampling-for-linear-inverse-problem-solving-a-filtering-perspective-u-jin-choi/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241220T140000
DTEND;TZID=Asia/Seoul:20241220T160000
DTSTAMP:20260422T203036
CREATED:20241209T001156Z
LAST-MODIFIED:20241219T012147Z
UID:10339-1734703200-1734710400@www.ibs.re.kr
SUMMARY:cellFlow: a generative flow-based model for single-cell count data - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “cellFlow: a generative flow-based model for single-cell count data” by A. Palma et.al\, ICLR\, 2024. \nAbstract  \nGenerative modeling for single-cell RNA-seq has proven transformative in crucial fields such as learning single-cell representations and perturbation responses. However\, despite their appeal in relevant applications involving data augmentation and unseen cell state prediction\, use cases like generating artificial biological samples are still in their pioneering phase. While common approaches producing single-cell samples from noise operate in continuous space by assuming normalized gene expression\, we argue for the necessity of sample generation in a raw transcription count space to favor processing-agnostic data generation and flexible downstream applications. To this end\, we propose cellFlow\, a Flow-Matching-based model that generates single-cell count data. In our empirical study\, cellFlow performs on par with existing methods operating on normalized data when evaluated on three biological datasets. By carefully considering raw single-cell distributional properties\, cellFlow is a promising avenue for future developments in single-cell generative models.
URL:https://www.ibs.re.kr/bimag/event/qclus-a-droplet-filtering-algorithm-for-enhanced-snrna-seq-data-quality-in-challenging-samples-hyun-kim/
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:20241216T150000
DTEND;TZID=Asia/Seoul:20241216T170000
DTSTAMP:20260422T203036
CREATED:20241022T001632Z
LAST-MODIFIED:20241208T082830Z
UID:10195-1734361200-1734368400@www.ibs.re.kr
SUMMARY:Solving Inverse Problems in Medical Imaging with Score-Based Generative Models - U Jin Choi
DESCRIPTION:In this talk\, we discuss the paper : “Solving Inverse Problems in Medical Imaging with Score-Based Generative Models” by Y Song et al. \nReconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly map measurements to medical images\, leveraging a training dataset of paired images and measurements. These measurements are typically synthesized from images using a fixed physical model of the measurement process\, which hinders the generalization capability of models to unknown measurement processes. To address this issue\, we propose a fully unsupervised technique for inverse problem solving\, leveraging the recently introduced score-based generative models. Specifically\, we first train a score-based generative model on medical images to capture their prior distribution. Given measurements and a physical model of the measurement process at test time\, we introduce a sampling method to reconstruct an image consistent with both the prior and the observed measurements. Our method does not assume a fixed measurement process during training\, and can thus be flexibly adapted to different measurement processes at test time. Empirically\, we observe comparable or better performance to supervised learning techniques in several medical imaging tasks in CT and MRI\, while demonstrating significantly better generalization to unknown measurement processes.
URL:https://www.ibs.re.kr/bimag/event/solving-inverse-problems-in-medical-imaging-with-score-based-generative-models-u-jin-choi/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241213T140000
DTEND;TZID=Asia/Seoul:20241213T160000
DTSTAMP:20260422T203036
CREATED:20241209T000818Z
LAST-MODIFIED:20241209T000818Z
UID:10337-1734098400-1734105600@www.ibs.re.kr
SUMMARY:Laplacian renormalization group for heterogeneous networks - Gyuyoung Hwang
DESCRIPTION:In this talk\, we study and discuss the paper “Laplacian renormalization group for heterogeneous networks” by Pablo Villegas et.al\, Nature Physics\, 2023. \nAbstract  \nThe renormalization group is the cornerstone of the modern theory of universality and phase transitions and it is a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However\, its application to complex networks has proven particularly challenging\, owing to correlations between intertwined scales. To date\, existing approaches have been based on hidden geometries hypotheses\, which rely on the embedding of complex networks into underlying hidden metric spaces. Here we propose a Laplacian renormalization group diffusion-based picture for complex networks\, which is able to identify proper spatiotemporal scales in heterogeneous networks. In analogy with real-space renormalization group procedures\, we first introduce the concept of Kadanoff supernodes as block nodes across multiple scales\, which helps to overcome detrimental small-world effects that are responsible for cross-scale correlations. We then rigorously define the momentum space procedure to progressively integrate out fast diffusion modes and generate coarse-grained graphs. We validate the method through application to several real-world networks\, demonstrating its ability to perform network reduction keeping crucial properties of the systems intact.
URL:https://www.ibs.re.kr/bimag/event/laplacian-renormalization-group-for-heterogeneous-networks-gyuyoung-hwang/
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:20241211T160000
DTEND;TZID=Asia/Seoul:20241211T170000
DTSTAMP:20260422T203036
CREATED:20240829T004544Z
LAST-MODIFIED:20241204T022447Z
UID:10005-1733932800-1733936400@www.ibs.re.kr
SUMMARY:Circadian phase in cells and humans - Achim Kramer
DESCRIPTION:Abstract: \nCircadian clocks in cells and humans are heterogeneous in period and phase. This heterogeneity can be exploited not only to gain insight into the molecular basis of circadian rhythms\, but also to explore plasticity and robustness. In this talk\, I will report on two ongoing projects in the lab: (i) We are exploiting the heterogeneity of cells in both circadian period and a metabolic parameter – protein stability – to study their interdependence without the need for genetic manipulation. We have generated cells expressing key circadian proteins (CRYPTOCHROME1/2 (CRY1/2) and PERIOD1/2 (PER1/2)) as endogenous fusions with fluorescent proteins and are simultaneously monitoring circadian rhythm and degradation in thousands of single cells. (ii) We are developing molecular biomarkers of human circadian characteristics that will allow an objective description of the epidemiology of the human circadian clock and an assessment of its robustness and plasticity.
URL:https://www.ibs.re.kr/bimag/event/circadian-phase-in-cells-and-humans-achim-kramer/
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/2024/08/achim-kramer-e1724986773749.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241209T160000
DTEND;TZID=Asia/Seoul:20241209T170000
DTSTAMP:20260422T203036
CREATED:20241204T084453Z
LAST-MODIFIED:20241204T084453Z
UID:10334-1733760000-1733763600@www.ibs.re.kr
SUMMARY:Theoretical studies on biological oscillations by using waveform data and mathematical models - Shingo Gibo
DESCRIPTION:Title: Theoretical studies on biological oscillations by using waveform data and mathematical models \nAbstract: Temporal waveforms of biological oscillations are of various shapes. In our research\, we have explored the functional implications of these waveform shapes. In particular\, we theoretically showed that the period of circadian clocks is proportional to the waveform distortion from sinusoidal wave. It suggests that the circadian period can be stable against temperature changes only if the waveform becomes more distorted at higher temperatures. In this talk\, I will explain my past research and discuss my future plans. \n\nReference:\n[1] Shingo Gibo\, Gen Kurosawa\, Non-sinusoidal Waveform in Temperature Compensated Circadian Oscillations\, Biophysical Journal 116 (4) 741-751 (2019). doi: 10.1016/j.bpj.2018.12.022\n[2] Shingo Gibo\, Gen Kurosawa\, Theoretical study on the regulation of circadian rhythms by RNA methylation\, Journal of Theoretical Biology 490\, 110140 (2020). doi; 10.1016/j.jtbi.2019.110140\n[3] Shingo Gibo\, Teiji Kunihiro\, Tetsuo Hatsuda\, Gen Kurosawa\, Waveform distortion for temperature compensation and synchronization in circadian rhythms: An approach based on the renormalization group method\, arXiv (2024). arXiv:2409.02526
URL:https://www.ibs.re.kr/bimag/event/theoretical-studies-on-biological-oscillations-by-using-waveform-data-and-mathematical-models-shingo-gibo/
CATEGORIES:Biomedical Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241129T110000
DTEND;TZID=Asia/Seoul:20241129T120000
DTSTAMP:20260422T203036
CREATED:20240829T004146Z
LAST-MODIFIED:20241114T001353Z
UID:10001-1732878000-1732881600@www.ibs.re.kr
SUMMARY:Mathematical Modelling of Microtube Driven Invasion of Glioma - Thomas Hillen
DESCRIPTION:Abstract: Malignant gliomas are highly invasive brain tumors. Recent attention has focused on their capacity for network-driven invasion\, whereby mitotic events can be followed by the migration of nuclei along long thin cellular protrusions\, termed tumour microtubes (TM). Here I develop a mathematical model that describes this microtube-driven invasion of gliomas. I show that scaling limits lead to well known glioma models as special cases such as go-or-grow models\, the PI model of Swanson\, and the anisotropic model of Swan. I compute the invasion speed and I use the model to fit experiments of cancer resection and regrowth in the mouse brain.\n(Joint work with N. Loy\, K.J. Painter\, R. Thiessen\, A. Shyntar).
URL:https://www.ibs.re.kr/bimag/event/mathematical-modelling-of-microtube-driven-invasion-of-glioma-thomas-hillen/
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/2024/08/thillen.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241122T100000
DTEND;TZID=Asia/Seoul:20241122T113000
DTSTAMP:20260422T203036
CREATED:20241119T001534Z
LAST-MODIFIED:20241119T001534Z
UID:10259-1732269600-1732275000@www.ibs.re.kr
SUMMARY:SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection” by Yueyue Yao\, et.al.\, Neural Networks\, 2024.  \nAbstract  \n\n\n\nAnomaly detection in multivariate time series is of critical importance in many real-world applications\, such as system maintenance and Internet monitoring. In this article\, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both SVD and autoencoder to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed\, where two encoders are used to capture features in time and variable dimensions and a shared decoder is used to generate reconstructions based on latent representations from both dimensions. A new regularization based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical supports delivered. A specific loss component is further proposed to align Fourier coefficients of inputs and reconstructions. It can preserve details of original inputs\, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms.
URL:https://www.ibs.re.kr/bimag/event/svd-ae-an-asymmetric-autoencoder-with-svd-regularization-for-multivariate-time-series-anomaly-detection-myna-lim/
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:20241115T090000
DTEND;TZID=Asia/Seoul:20241115T110000
DTSTAMP:20260422T203036
CREATED:20241112T000249Z
LAST-MODIFIED:20241112T041049Z
UID:10232-1731661200-1731668400@www.ibs.re.kr
SUMMARY:Next generation reservoir computing - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Next generation reservoir computing”\, by Gauthier\, et.al\, Nat. Comm.\, 2021. \nAbstract : Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly\, it requires very small training data sets\, uses linear optimization\, and thus requires minimal computing resources. However\, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression\, which requires no random matrices\, fewer metaparameters\, and provides interpretable results. Here\, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time\, heralding the next generation of reservoir computing.
URL:https://www.ibs.re.kr/bimag/event/next-generation-reservoir-computing-kang-min-lee/
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:20241113T160000
DTEND;TZID=Asia/Seoul:20241113T170000
DTSTAMP:20260422T203036
CREATED:20240829T003616Z
LAST-MODIFIED:20240829T005258Z
UID:9996-1731513600-1731517200@www.ibs.re.kr
SUMMARY:Mathematical models for malaria - Jennifer Flegg
DESCRIPTION:Abstract:  The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400\,000 deaths\, with children under the age of five the most vulnerable. Ambitious malaria elimination targets have been set by the World Health Organization for 2030. These involve the elimination of the disease in at least 35 countries. However\, these malaria elimination targets rest precariously on being able to treat the disease appropriately; a difficult feat with the emergence and spread of antimalarial drug resistance\, along with many other challenges. In this talk\, I will introduce several statistical and mathematical models that can be used to monitor malaria transmission and to support malaria elimination. For example\, I’ll present mechanistic models of disease transmission\, statistical models that allow the emergence and spread of antimalarial drug resistance to be monitored\, mechanistic models that capture the role of bioclimatic factors on the risk of malaria and optimal geospatial sampling schemes for future malaria surveillance. I will discuss how the results of these models have been used to inform public health policy and support ongoing malaria elimination efforts.
URL:https://www.ibs.re.kr/bimag/event/mathematical-models-for-malaria/
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/2024/08/Jennifer-Flegg-e1724892764918.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241108T140000
DTEND;TZID=Asia/Seoul:20241108T160000
DTSTAMP:20260422T203036
CREATED:20241104T150449Z
LAST-MODIFIED:20241104T150551Z
UID:10220-1731074400-1731081600@www.ibs.re.kr
SUMMARY:Cluster-based network modeling—From snapshots to complex dynamical systems - Olive R. Cawiding
DESCRIPTION:Abstract: We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning\, network science\, and statistical physics. CNM describes short- and long-term behavior and is fully automatable\, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor\, ECG heartbeat signals\, Kolmogorov flow\, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand\, estimate\, predict\, and control complex systems in all scientific fields.
URL:https://www.ibs.re.kr/bimag/event/cluster-based-network-modeling-from-snapshots-to-complex-dynamical-systems/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241101T140000
DTEND;TZID=Asia/Seoul:20241101T150000
DTSTAMP:20260422T203036
CREATED:20241024T085401Z
LAST-MODIFIED:20241029T034102Z
UID:10201-1730469600-1730473200@www.ibs.re.kr
SUMMARY:Derivation and simulation of a computational model of active cell populations: How overlap avoidance\, deformability\, cell-cell junctions and cytoskeletal forces affect alignment - Kevin SPINICCI
DESCRIPTION:In this talk\, we discuss the paper : “Derivation and simulation of a computational model of active cell populations: How overlap avoidance\, deformability\, cell-cell junctions and cytoskeletal forces affect alignment” by Leech et al\, nature biotechnology\, https://doi.org/10.1371/journal.pcbi.1011879. \nZoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nCollective alignment of cell populations is a commonly observed phenomena in biology. An important example are aligning fibroblasts in healthy or scar tissue. In this work we derive and simulate a mechanistic agent-based model of the collective behaviour of actively moving and interacting cells\, with a focus on understanding collective alignment. The derivation strategy is based on energy minimisation. The model ingredients are motivated by data on the behaviour of different populations of aligning fibroblasts and include: Self-propulsion\, overlap avoidance\, deformability\, cell-cell junctions and cytoskeletal forces. We find that there is an optimal ratio of self-propulsion speed and overlap avoidance that maximises collective alignment. Further we find that deformability aids alignment\, and that cell-cell junctions by themselves hinder alignment. However\, if cytoskeletal forces are transmitted via cell-cell junctions we observe strong collective alignment over large spatial scales.
URL:https://www.ibs.re.kr/bimag/event/batch-effects-in-single-cell-rna-sequencing-data-are-corrected-by-matching-mutual-nearest-neighbors-kevin-spinicci/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241030T150000
DTEND;TZID=Asia/Seoul:20241030T160000
DTSTAMP:20260422T203036
CREATED:20240829T003420Z
LAST-MODIFIED:20241023T052507Z
UID:9992-1730300400-1730304000@www.ibs.re.kr
SUMMARY:Latent space dynamics identification - Youngsoo Choi
DESCRIPTION:Abstravt: Latent space dynamics identification (LaSDI) is an interpretable data-driven framework that follows three distinct steps\, i.e.\, compression\, dynamics identification\, and prediction. Compression allows high-dimensional data to be reduced so that they can be easily fit into an interpretable model. Dynamics identification lets you derive the interpretable model\, usually some form of parameterized differential equations that fit the reduced latent space data. Then\, in the prediction phase\, the identified differential equations are solved in the reduced space for a new parameter point and its solution is projected back to the full space. The efficiency of the LaSDI framework comes from the fact that the solution process in the prediction phase does not involve any full order model size. For the identification\, various approaches are possible\, e.g.\, a fixed form as in dynamic mode decomposition and thermodynamics-based LaSDI\, a regression form as in sparse identification of nonlinear dynamics (SINDy) and weak SINDy\, and a physics-driven form as projection-based reduced order model. Various physics prob- lems were accurately accelerated by the family of LaSDIs\, achieving a speed-up of 1000x\, e.g.\, kinetic plasma simulations\, pore collapse\, and computational fluid problems.
URL:https://www.ibs.re.kr/bimag/event/latent-space-dynmaics-identification-youngsoo-choi/
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/2024/08/choi15_1-e1724991182393.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR