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METHOD:PUBLISH
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:20250509T140000
DTEND;TZID=Asia/Seoul:20250509T160000
DTSTAMP:20260422T110642
CREATED:20250426T142850Z
LAST-MODIFIED:20250507T002814Z
UID:11058-1746799200-1746806400@www.ibs.re.kr
SUMMARY:Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics” by Amitava Banerjee\, Sarthak Chandra\, and Edward Ott\, PNAS\, 2023. \nAbstract \nNetwork link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications\, e.g.\, estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases\, the measured time series data may be subject to limitations\, including limited duration\, low sampling rate\, observational noise\, and partial nodal state measurement. However\, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here\, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality\, transfer entropy\, and\, a machine learning-based method. Furthermore\, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.
URL:https://www.ibs.re.kr/bimag/event/chaos-is-not-rare-in-natural-ecosystems-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:20250502T140000
DTEND;TZID=Asia/Seoul:20250502T160000
DTSTAMP:20260422T110642
CREATED:20250330T073307Z
LAST-MODIFIED:20250424T070416Z
UID:10929-1746194400-1746201600@www.ibs.re.kr
SUMMARY:Boolean modelling as a logic-based dynamic approach in systems medicine - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “Boolean modelling as a logic-based dynamic approach in systems medicine” by Ahmed Abdelmonem Hemedan et al.\, Computational and Structural biotechnology journal (2022). \nAbstract  \nMolecular mechanisms of health and disease are often represented as systems biology diagrams\, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models\, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive\, one for present or active. Because of this approximation\, Boolean modelling is applicable to large diagrams\, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally\, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
URL:https://www.ibs.re.kr/bimag/event/boolean-modelling-as-a-logic-based-dynamic-approach-in-systems-medicine-kevin-spinicci/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250418T140000
DTEND;TZID=Asia/Seoul:20250418T160000
DTSTAMP:20260422T110642
CREATED:20250327T010619Z
LAST-MODIFIED:20250327T010619Z
UID:10923-1744984800-1744992000@www.ibs.re.kr
SUMMARY:Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Identifying key drivers in a stochastic dynamical system through estimation of transfer entropy between univariate and multivariate time series” by Julian Lee\, Physical Review E\, 2025. \nAbstract  \nTransfer entropy (TE) is a widely used tool for quantifying causal relationships in stochastic dynamical systems. Traditionally\, TE and its conditional variants are applied pairwise between dynamic variables to infer these relationships. However\, identifying key drivers in such systems requires a measure of the causal influence exerted by each component on the entire system. I propose using outgoing transfer entropy (OutTE)\, the transfer entropy from a given variable to the collection of remaining variables\, to quantify the causal influence of the variable on the rest of the system. Conversely\, the incoming transfer entropy (InTE) is also defined to quantify the causal influence received by a component from the rest of the system. Since OutTE and InTE involve transfer entropy between univariate and multivariate time series\, naive estimation methods can result in significant errors\, especially when the number of variables is large relative to the number of samples. To address this\, I introduce a novel estimation scheme that computes outgoing and incoming TE only between significantly interacting partners. The feasibility and effectiveness of this approach are demonstrated using synthetic data and real oral microbiota data. The method successfully identifies the bacterial species known to be key players in the bacterial community\, highlighting its potential for uncovering causal drivers in complex systems.
URL:https://www.ibs.re.kr/bimag/event/identifying-key-drivers-in-a-stochastic-dynamical-system-through-estimation-of-transfer-entropy-between-univariate-and-multivariate-time-series-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:20250411T140000
DTEND;TZID=Asia/Seoul:20250411T160000
DTSTAMP:20260422T110642
CREATED:20250327T010416Z
LAST-MODIFIED:20250327T010416Z
UID:10921-1744380000-1744387200@www.ibs.re.kr
SUMMARY:Entrainment and multi-stability of the p53 oscillator in human cells - Eui Min Jeong
DESCRIPTION:In this talk\, we discuss the paper\, “Entrainment and multi-stability of the p53 oscillator in human cells” by Alba Jiménez et al.\, Cell Systems\, 2024. \nAbstract  \nThe tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by the theory of synchronization and entrainment\, we developed a mathematical model and experimental system to test the ability of the p53 oscillator to entrain to external drug pulses of various periods and strengths. We found that the p53 oscillator can be locked and entrained to a wide range of entrainment modes. External periods far from p53’s natural oscillations increased the heterogeneity between individual cells whereas stronger inputs reduced it. Single-cell measurements allowed deriving the phase response curves (PRCs) and multiple Arnold tongues of p53. In addition\, multi-stability and non-linear behaviors were mathematically predicted and experimentally detected\, including mode hopping\, period doubling\, and chaos. Our work revealed critical dynamical properties of the p53 oscillator and provided insights into understanding and controlling it. A record of this paper’s transparent peer review process is included in the supplemental information.
URL:https://www.ibs.re.kr/bimag/event/entrainment-and-multi-stability-of-the-p53-oscillator-in-human-cells-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:20250404T140000
DTEND;TZID=Asia/Seoul:20250404T160000
DTSTAMP:20260422T110642
CREATED:20250326T091007Z
LAST-MODIFIED:20250330T013324Z
UID:10919-1743775200-1743782400@www.ibs.re.kr
SUMMARY:Accurate predictions on small data with a tabular foundation model - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “Accurate predictions on small data with a tabular foundation model” by Noah Hollmann et al.\, Nature (2025). \nAbstract \nTabular data\, spreadsheets organized in rows and columns\, are ubiquitous across scientific fields\, from biomedicine to particle physics to economics and climate science1\,2. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models\, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories3\,4\,5\, gradient-boosted decision trees6\,7\,8\,9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN)\, a tabular foundation model that outperforms all previous methods on datasets with up to 10\,000 samples by a wide margin\, using substantially less training time. In 2.8 s\, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model\, this model also allows fine-tuning\, data generation\, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets\, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields\, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains.
URL:https://www.ibs.re.kr/bimag/event/a-differentiable-gillespie-algorithm-for-simulating-chemical-kinetics-parameter-estimation-and-designing-synthetic-biological-circuits-dongju-lim/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250328T140000
DTEND;TZID=Asia/Seoul:20250328T160000
DTSTAMP:20260422T110642
CREATED:20250302T133447Z
LAST-MODIFIED:20250327T010923Z
UID:10853-1743170400-1743177600@www.ibs.re.kr
SUMMARY:Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain” by Rubén Calvo et al.\, Physical Review Letters 2024\, at the Journal Club. \nAbstract \nRecent analyses\, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain\, compellingly support the hypothesis that neural dynamics operate near the edge of instability. However\, these and related analyses often fail to capture the intricate temporal structure of brain activity\, as they primarily rely on time-integrated measurements across neurons. Here\, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Furthermore\, we introduce a method for projecting brain activity onto a basis of spatiotemporal patterns\, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset\, we observe significant differences in criticality signatures\, effective dimensionality\, and spatiotemporal activity patterns between healthy subjects and individuals with Parkinson’s disease\, highlighting its potential impact.
URL:https://www.ibs.re.kr/bimag/event/journal-club-hyun-kim/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20260422T110642
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20260422T110642
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20260422T110642
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20260422T110642
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:20260422T110642
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:20260422T110642
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:20260422T110642
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:20260422T110642
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:20260422T110642
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:20250110T140000
DTEND;TZID=Asia/Seoul:20250110T160000
DTSTAMP:20260422T110642
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:20260422T110642
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:20241220T140000
DTEND;TZID=Asia/Seoul:20241220T160000
DTSTAMP:20260422T110642
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:20241213T140000
DTEND;TZID=Asia/Seoul:20241213T160000
DTSTAMP:20260422T110642
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:20241122T100000
DTEND;TZID=Asia/Seoul:20241122T113000
DTSTAMP:20260422T110642
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:20260422T110642
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:20241108T140000
DTEND;TZID=Asia/Seoul:20241108T160000
DTSTAMP:20260422T110642
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:20260422T110642
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241025T140000
DTEND;TZID=Asia/Seoul:20241025T150000
DTSTAMP:20260422T110642
CREATED:20241011T003836Z
LAST-MODIFIED:20241015T003252Z
UID:10162-1729864800-1729868400@www.ibs.re.kr
SUMMARY:Yun Min Song - Noise robustness and metabolic load determine the principles of central dogma regulation
DESCRIPTION:In this talk\, we discuss the paper : “Noise robustness and metabolic load determine the principles of central dogma regulation” by Teresa W. Lo et al\, Sci. Adv\, https://doi.org/10.1126/sciadv.ado3095. \nZoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nThe processes of gene expression are inherently stochastic\, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question\, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes\, and these principles have broad implications for cellular function. \n 
URL:https://www.ibs.re.kr/bimag/event/yun-min-song-noise-robustness-and-metabolic-load-determine-the-principles-of-central-dogma-regulation/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241011T140000
DTEND;TZID=Asia/Seoul:20241011T160000
DTSTAMP:20260422T110642
CREATED:20240923T012824Z
LAST-MODIFIED:20240923T012824Z
UID:10095-1728655200-1728662400@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators
DESCRIPTION:In this talk\, we discuss the paper\, “A frequency-amplitude coordinator and its optimal energy consumption for biological oscillators”\, by Bo-Wei Qin et. al.\, Nature Communications\, 2021. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract  \nBiorhythm including neuron firing and protein-mRNA interaction are fundamental activities with diffusive effect. Their well-balanced spatiotemporal dynamics are beneficial for healthy sustainability. Therefore\, calibrating both anomalous frequency and amplitude of biorhythm prevents physiological dysfunctions or diseases. However\, many works were devoted to modulate frequency exclusively whereas amplitude is usually ignored\, although both quantities are equally significant for coordinating biological functions and outputs. Especially\, a feasible method coordinating the two quantities concurrently and precisely is still lacking. Here\, for the first time\, we propose a universal approach to design a frequency-amplitude coordinator rigorously via dynamical systems tools. We consider both spatial and temporal information. With a single well-designed coordinator\, they can be calibrated to desired levels simultaneously and precisely. The practical usefulness and efficacy of our method are demonstrated in representative neuronal and gene regulatory models. We further reveal its fundamental mechanism and optimal energy consumption providing inspiration for biorhythm regulation in future.
URL:https://www.ibs.re.kr/bimag/event/eui-min-jeong-a-frequency-amplitude-coordinator-and-its-optimal-energy-consumption-for-biological-oscillators/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241004T140000
DTEND;TZID=Asia/Seoul:20241004T160000
DTSTAMP:20260422T110642
CREATED:20240827T002008Z
LAST-MODIFIED:20241002T001729Z
UID:9960-1728050400-1728057600@www.ibs.re.kr
SUMMARY:Dongju Lim\, Mathematical model for the distribution of DNA replication origins
DESCRIPTION:In this talk we discuss the paper “Mathematical model for the distribution of DNA replication origins” by Alessandro de Moura and Jens Karschau\, Physical Review E\, 2024. \nAbstract  \nDNAreplication in yeast and in many other organisms starts from well-defined locations on the DNA known as replication origins. The spatial distribution of these origins in the genome is particularly important in ensuring that replication is completed quickly. Cells are more vulnerable to DNA damage and other forms of stress while they are replicating their genome. This raises the possibility that the spatial distribution of origins is under selection pressure. In this paper we investigate the hypothesis that natural selection favors origin distributions leading to shorter replication times. Using a simple mathematical model\, we show that this hypothesis leads to two main predictions about the origin distributions: that neighboring origins that are inefficient (less likely to fire) are more likely to be close to each other than efficient origins; and that neighboring origins with larger differences in firing times are more likely to be close to each other than origins with similar firing times. We test these predictions using next-generation sequencing data\, and show that they are both supported by the data.
URL:https://www.ibs.re.kr/bimag/event/dongju-lim-analysis-of-a-detailed-multi-stage-model-of-stochastic-gene-expression-using-queueing-theory-and-model-reduction/
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:20240920T140000
DTEND;TZID=Asia/Seoul:20240920T160000
DTSTAMP:20260422T110642
CREATED:20240828T015222Z
LAST-MODIFIED:20240828T015222Z
UID:9966-1726840800-1726848000@www.ibs.re.kr
SUMMARY:Brenda Gavina\, Achieving Occam’s razor: Deep learning for optimal model reduction
DESCRIPTION:In this talk\, we discuss the paper “Achieving Occam’s razor: Deep learning for optimal model reduction” by Botond B. Antal et.al.\, PLOS Computational Biology\, 2024. \nAbstract  \nAll fields of science depend on mathematical models. Occam’s razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data\, and thus inaccurate or ambiguous conclusions. Here\, we show how deep learning can be powerfully leveraged to apply Occam’s razor to model parameters. Our method\, FixFit\, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First\, it provides a metric to quantify the original model’s degree of complexity. Second\, it allows for the unique fitting of data. Third\, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases\, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system\, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter\, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields\, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.
URL:https://www.ibs.re.kr/bimag/event/brenda-gavina-achieving-occams-razor-deep-learning-for-optimal-model-reduction/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240913T140000
DTEND;TZID=Asia/Seoul:20240913T160000
DTSTAMP:20260422T110642
CREATED:20240827T001735Z
LAST-MODIFIED:20240904T030726Z
UID:9958-1726236000-1726243200@www.ibs.re.kr
SUMMARY:Hyun Kim\, Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage
DESCRIPTION:In this talk\, we discuss the paper “Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage” by Zhiwei Huang\, et. al.\, bioRxiv\, 2024. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nCells must adopt flexible regulatory strategies to make decisions regarding their fate\, including differentiation\, apoptosis\, or survival in the face of various external stimuli. One key cellular strategy that enables these functions is stochastic gene expression programs. However\, understanding how transcriptional bursting\, and consequently\, cell fate\, responds to DNA damage on a genome-wide scale poses a challenge. In this study\, we propose an interpretable and scalable inference framework\, DeepTX\, that leverages deep learning methods to connect mechanistic models and scRNA-seq data\, thereby revealing genome-wide transcriptional burst kinetics. This framework enables rapid and accurate solutions to transcription models and the inference of transcriptional burst kinetics from scRNA-seq data. Applying this framework to several scRNA-seq datasets of DNA-damaging drug treatments\, we observed that fluctuations in transcriptional bursting induced by different drugs could lead to distinct fate decisions: IdU treatment induces differentiation in mouse embryonic stem cells by increasing the burst size of gene expression\, while 5FU treatment with low and high dose increases the burst frequency of gene expression to induce cell apoptosis and survival in human colon cancer cells. Together\, these results show that DeepTX can be used to analyze single-cell transcriptomics data and can provide mechanistic insights into cell fate decisions.
URL:https://www.ibs.re.kr/bimag/event/hyun-kim-deep-learning-linking-mechanistic-models-to-single-cell-transcriptomics-data-reveals-transcriptional-bursting-in-response-to-dna-damage/
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:20240906T140000
DTEND;TZID=Asia/Seoul:20240906T160000
DTSTAMP:20260422T110642
CREATED:20240730T001910Z
LAST-MODIFIED:20240904T030852Z
UID:9905-1725631200-1725638400@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Predicting multiple observations in complex systems through low-dimensional embeddings
DESCRIPTION:In this talk\, we discuss the paper\, “Predicting multiple observations in complex systems through low-dimensional embeddings”\, by Tao Wu et. al.\, Nature Communications\, 2024. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nForecasting all components in complex systems is an open and challenging task\, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework\, namely\, feature-and-reconstructed manifold mapping (FRMM)\, which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system\, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon\, electroencephalogram (EEG) signals\, foreign exchange market\, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor\, and thus has potential for applications in many other real-world systems.
URL:https://www.ibs.re.kr/bimag/event/olive-cawiding-a-flexible-symbolic-regression-method-for-constructing-interpretable-clinical-prediction-models/
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:20240816T140000
DTEND;TZID=Asia/Seoul:20240816T160000
DTSTAMP:20260422T110642
CREATED:20240730T001615Z
LAST-MODIFIED:20240730T001615Z
UID:9903-1723816800-1723824000@www.ibs.re.kr
SUMMARY:Kevin Spinicci\, SMSSVD : Submatrix selection singular value decomposition
DESCRIPTION:In this talk\, we discuss the paper\, “SMSSVD : Submatrix selection singular value decomposition”\, by Rasmus Henningsson and Magnus Fontes\, Bioinformatics\, 2019. \nAbstract \n\nMotivation\nHigh throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data analysis. Existing methods\, aimed at signal reconstruction and deconvolution\, in general\, are either supervised\, contain parameters that need to be estimated or present other types of ad hoc features. We here introduce SubMatrix Selection Singular Value Decomposition (SMSSVD)\, a parameter-free unsupervised signal decomposition and dimension reduction method\, designed to reduce noise\, adaptively for each low-rank-signal in a given data matrix\, and represent the signals in the data in a way that enable unbiased exploratory analysis and reconstruction of multiple overlaid signals\, including identifying groups of variables that drive different signals. \n\n\nResults\nThe SMSSVD method produces a denoised signal decomposition from a given data matrix. It also guarantees orthogonality between signal components in a straightforward manner and it is designed to make automation possible. We illustrate SMSSVD by applying it to several real and synthetic datasets and compare its performance to golden standard methods like PCA (Principal Component Analysis) and SPC (Sparse Principal Components\, using Lasso constraints). The SMSSVD is computationally efficient and despite being a parameter-free method\, in general\, outperforms existing statistical learning methods.
URL:https://www.ibs.re.kr/bimag/event/kevin-spinicci-smssvd-submatrix-selection-singular-value-decomposition/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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