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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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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:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260506T160000
DTEND;TZID=Asia/Seoul:20260506T170000
DTSTAMP:20260502T020454
CREATED:20260205T074722Z
LAST-MODIFIED:20260311T121631Z
UID:12189-1778083200-1778086800@www.ibs.re.kr
SUMMARY:Data-driven discovery of biological oscillator models - Lendert Gelens
DESCRIPTION:Oscillatory dynamics are a found everywhere in living systems\, underlying processes such as metabolic regulation\, cell division\, and embryonic development. Identifying the mechanisms that generate these rhythms is challenging due to nonlinear interactions\, multiple time scales\, and limited access to all relevant variables. Data-driven approaches offer a promising route to infer dynamical models directly from time-series data. In this talk\, I will discuss our work on data-driven discovery of models for (bio)chemical oscillators. In particular\, I will present CLINE\, a neural-network–based framework that infers key geometric features of phase space\, such as nullclines\, from oscillatory data and uses this information to construct low-dimensional dynamical models. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/data-driven-discovery-of-biological-oscillator-models-lendert-gelens/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/02/Gelens_Lendert_alumni-e1770278337776.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260508T100000
DTEND;TZID=Asia/Seoul:20260508T120000
DTSTAMP:20260502T020454
CREATED:20260406T041825Z
LAST-MODIFIED:20260429T070902Z
UID:12360-1778234400-1778241600@www.ibs.re.kr
SUMMARY:Digital biomarkers for brain health: passive and continuous assessment from wearable sensors - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “Digital biomarkers for brain health: passive and continuous assessment from wearable sensors” by Igor Matias et al.\, npj digital medicine\, 2026. \nAbstract\nContinuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health\, which is currently limited by the burden of active assessments. This study investigated the potential of consumer-grade wearable and mobile technologies to passively predict 21 cognitive and mental health outcomes in real-world conditions. We collected data from 82 cognitively healthy adults\, including passively measured behaviour\, physiology\, and environmental exposures longitudinally\, for 10 months. Active data were gathered in four waves using validated patient- and performance-reported outcomes. Data quality assurance involved a data filtering resulting in average wearable data coverage of 96% per day. Artificial Intelligence-powered prediction was applied\, and performance was assessed using subject- and wave-dependent cross-validation. Cognitive and affective outcomes were predicted with low scaled errors. Patient-reported outcomes were more predictable than performance-based ones. Environmental and physiological metrics emerged as the most informative predictors. Passive multimodal data captured meaningful variability in cognition and affect\, demonstrating the feasibility of low-burden\, scalable approaches to continuous brain-health monitoring. Feature-importance analyses suggested that environmental exposures better explained inter-individual differences\, whereas physiological and behavioural rhythms captured within-person changes. These findings highlight the potential of everyday technologies for population-level tracking of brain-health and deviations from expected trajectories.
URL:https://www.ibs.re.kr/bimag/event/digital-biomarkers-for-brain-health-passive-and-continuous-assessment-from-wearable-sensors-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:20260515T100000
DTEND;TZID=Asia/Seoul:20260515T120000
DTSTAMP:20260502T020454
CREATED:20260403T080250Z
LAST-MODIFIED:20260429T070938Z
UID:12338-1778839200-1778846400@www.ibs.re.kr
SUMMARY:High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis - Hyeong Jun Jang
DESCRIPTION:In this talk\, we discuss the paper “High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis” by Divya Singh et al.\, Nat. Comm.\, 2025. \nAbstract \nSingle-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end\, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the first moment of the enzymatic turnover time depends linearly on the reciprocal of the substrate concentration. This\, in turn\, provides robust and convenient means to determine the maximal turnover rate and the Michaelis-Menten constant. Yet\, the information provided by these parameters is incomplete and does not allow access to key observables such as the lifetime of the enzyme-substrate complex\, the rate of substrate-enzyme binding\, and the probability of successful product formation. Here we show that these quantities and others can be inferred via a set of high-order Michaelis-Menten equations that we derive. These equations capture universal linear relations between the reciprocal of the substrate concentration and distinguished combinations of turnover time moments\, essentially generalizing the Michaelis-Menten equation to moments of any order. We demonstrate how key observables such as the lifetime of the enzyme-substrate complex\, the rate of substrate-enzyme binding\, and the probability of successful product formation\, can all be inferred using these high-order Michaelis-Menten equations. We test our inference procedure to show that it is robust\, producing accurate results with only several thousand turnover events per substrate concentration.
URL:https://www.ibs.re.kr/bimag/event/high-order-michaelis-menten-equations-allow-inference-of-hidden-kinetic-parameters-in-enzyme-catalysis-hyeong-jun-jang/
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:20260518T123000
DTEND;TZID=Asia/Seoul:20260518T133000
DTSTAMP:20260502T020454
CREATED:20260427T132625Z
LAST-MODIFIED:20260427T133007Z
UID:12390-1779107400-1779111000@www.ibs.re.kr
SUMMARY:Heejung Shim - Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis
DESCRIPTION:Abstract: \nSpatial transcriptomics enables the study of gene expression within its spatial context\, but introduces key statistical challenges\, including mixed cellular composition and complex spatial structure. In this talk\, I present two complementary modelling approaches.First\, I introduce FlexiDeconv\, a cell-type deconvolution method based on a modified Latent Dirichlet Allocation framework. A key feature of this method is its flexible use of reference information\, allowing the model to balance prior information from scRNA-seq with signals from observed spatial data\, and to adapt when the reference is incomplete or mismatched\, a common challenge in practice.I then present WaviFM\, a wavelet-based Bayesian sparse factor model that captures spatial gene expression patterns across multiple spatial scales\, enabling the detection of both fine and broad spatial patterns. In addition\, WaviFM can incorporate gene-set information to guide factor inference\, while allowing for uncertainty and potential errors in these annotations.Together\, these methods illustrate how flexible modelling of prior information and multi-scale modelling of spatial structure can improve our ability to extract biologically meaningful signals from spatial transcriptomics data.
URL:https://www.ibs.re.kr/bimag/event/heejung-shim-modelling-spatial-transcriptomics-from-flexible-cell-type-deconvolution-to-multi-scale-spatial-factor-analysis/
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:20260522T100000
DTEND;TZID=Asia/Seoul:20260522T120000
DTSTAMP:20260502T020454
CREATED:20260429T070216Z
LAST-MODIFIED:20260429T070829Z
UID:12396-1779444000-1779451200@www.ibs.re.kr
SUMMARY:Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations” by Zheng-Meng Zhai et al.\, Nature Communications\, 2025. \nAbstract: \nIn applications\, an anticipated issue is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed? We address this challenge by developing a hybrid transformer and reservoir-computing scheme. The transformer is trained without using data from the target system\, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system\, and its output is further fed into a reservoir computer for predicting its long-term dynamics or the attractor. The proposed hybrid machine-learning framework is tested using various prototypical nonlinear systems\, demonstrating that the dynamics can be faithfully reconstructed from reasonably sparse data. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the situation where training data do not exist and the observations are random and sparse.
URL:https://www.ibs.re.kr/bimag/event/bridging-known-and-unknown-dynamics-by-transformer-based-machine-learning-inference-from-sparse-observations-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:20260522T110000
DTEND;TZID=Asia/Seoul:20260522T120000
DTSTAMP:20260502T020454
CREATED:20260205T075139Z
LAST-MODIFIED:20260311T121645Z
UID:12194-1779447600-1779451200@www.ibs.re.kr
SUMMARY:Mathematics of diffusive signaling - Alan Lindsay
DESCRIPTION:Diffusive transport is one of the most fundamental mechanisms by which information\, mass\, and chemical signals propagate in physical and biological systems. In many settings—ranging from cellular signaling to chemical sensing—communication is mediated by particles undergoing random motion and interacting with small\, spatially localized targets. This talk explores the mathematical structures underlying diffusive signaling\, emphasizing how geometry\, stochasticity\, and multiscale effects shape signal detection and reliability. Using tools from stochastic processes\, partial differential equations\, and asymptotic analysis\, I will describe how seemingly microscopic features can exert a dominant influence on macroscopic signaling outcomes\, and highlight recent progress on quantifying signal strength\, timing\, and variability in complex geometries. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/mathematics-of-diffusive-signaling-alan-lindsay/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/02/alan_lindsay-e1770278281837.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260529T100000
DTEND;TZID=Asia/Seoul:20260529T120000
DTSTAMP:20260502T020454
CREATED:20260429T070610Z
LAST-MODIFIED:20260429T071000Z
UID:12398-1780048800-1780056000@www.ibs.re.kr
SUMMARY:Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies” by Sam vidil et al.\, Nature Communications\, 2025. \nAbstract: \nAccelerometers allow objective measures of dimensions (rest-activity rhythm (RAR)\, daytime activity\, sleep\, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge\, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses\, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3\,991\, mean age=69.4years) and UK Biobank (N = 54\,995\, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity\, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented)\, one high-intensity physical activity\, and four poor RAR (one characterised by late chronotype\, two by low activity but opposite sleep profiles\, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic\, behavioural and health-related factors. Findings were similar in these two independent cohort studies\, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm\, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR\, daytime activity\, sleep\, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health.
URL:https://www.ibs.re.kr/bimag/event/circadian-rhythm-profiles-derived-from-accelerometer-measures-of-the-sleep-wake-cycle-in-two-cohort-studies-chitaranjan-mahapatra/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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