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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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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241209T160000
DTEND;TZID=Asia/Seoul:20241209T170000
DTSTAMP:20260522T011749
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/
LOCATION:Daejeon
CATEGORIES:Biomedical Mathematics Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241211T160000
DTEND;TZID=Asia/Seoul:20241211T170000
DTSTAMP:20260522T011749
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:20241213T140000
DTEND;TZID=Asia/Seoul:20241213T160000
DTSTAMP:20260522T011749
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:20241216T150000
DTEND;TZID=Asia/Seoul:20241216T170000
DTSTAMP:20260522T011749
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:20241220T140000
DTEND;TZID=Asia/Seoul:20241220T160000
DTSTAMP:20260522T011749
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:20241227T100000
DTEND;TZID=Asia/Seoul:20241227T120000
DTSTAMP:20260522T011749
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:20241230T110000
DTEND;TZID=Asia/Seoul:20241230T120000
DTSTAMP:20260522T011749
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
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