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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260904T100000
DTEND;TZID=Asia/Seoul:20260904T120000
DTSTAMP:20260828T000744Z
CREATED:20260828T000744Z
LAST-MODIFIED:20260828T000744Z
UID:12992-1788516000-1788523200@www.ibs.re.kr
SUMMARY:Dissociating the intensity and phase origins of sleepiness through a threshold-distance model of sleep-wake dynamics - Myna Lim
DESCRIPTION:In this Journal club\, we will discuss the paper “Dissociating the intensity and phase origins of sleepiness through a threshold-distance model of sleep-wake dynamics”\, Yi Yao Zijun Ning et al.\, bioRxiv\, 2026. \nAbstract: \nSleepiness is a leading proximate cause of drowsy-driving fatalities\, medical errors and industrial accidents\, yet it has resisted mechanistic prediction; although it arises from well-characterized sleep-wake physiology\, it is experienced as a subjective state and has lacked a quantitative link to the underlying dynamics. We previously showed that subjective sleepiness maps linearly\, with a protocol-invariant form\, onto the signed distance H − H+ between the homeostatic pressure H and the circadian-modulated sleep-onset threshold H+. This single quantity predicts sleepiness accurately but is mechanistically ambiguous: the same value can arise either because H sits far from the boundary or because the threshold H+(t) has shifted with circadian phase\, and these two origins call for entirely different interpretations and interventions. Here we resolve this ambiguity by decomposing H − H+ into two mechanistically separable axes–intensity and phase. The intensity axis is the time-averaged margin ⟨ H − H+⟩\, set by how far\, on average\, H sits from the sleep boundary: slowed homeostatic accumulation accounts for the paradoxically blunted sleepiness of older adults\, and pharmacological suppression of H accounts for the dose-dependent alerting effect of caffeine. The phase axis is set by the circadian modulation of H+(t): under a forced-desynchrony protocol\, in which the pacemaker free-runs and the homeostatic and circadian processes are experimentally decoupled\, sleepiness tracks the circadian profile of H+(t) across all phases while the intensity mapping itself remains unchanged–a clean dissociation of the two axes. By resolving felt sleepiness into these two physiological degrees of freedom\, this framework renders previously isolated phenomena–aging\, caffeine and circadian misalignment–commensurable within a single theory and provides a physiologically interpretable basis for prospective fatigue-risk prediction.
URL:https://www.ibs.re.kr/bimag/event/dissociating-the-intensity-and-phase-origins-of-sleepiness-through-a-threshold-distance-model-of-sleep-wake-dynamics-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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260904T110000
DTEND;TZID=Asia/Seoul:20260904T120000
DTSTAMP:20260829T093123Z
CREATED:20260829T051716Z
LAST-MODIFIED:20260829T093123Z
UID:12998-1788519600-1788523200@www.ibs.re.kr
SUMMARY:Design of Single-Cell Experiments to Efficiently Infer Predictive Mechanistic Models for Stochastic Gene Expression - Brian Munsky
DESCRIPTION:Biochemical assays have made outstanding progress to elucidate how cells sense and respond to stimuli\, but mechanistic and parametric uncertainties preclude quantitative predictions for the full spatial\, temporal\, and heterogeneous responses of signal-activated gene expression. Stochastic models use random noise as an abstraction to account for these unknown or uncertain dynamics. When inferred from appropriate single-cell experiments\, such as smFISH or immunocytochemistry (ICC)\, these models can quantitatively predict complex biological responses in new environments. However\, many smFISH/ICC experiments are possible for different induction levels\, measurement times\, or observables\, and each may be time-consuming\, expensive\, or subject to labeling\, imaging\, or data processing errors. We introduce the Finite State Projection based Fisher Information Matrix (FSP-FIM) as a rigorous guide for the design of single-cell experiments. We extend the FSP-FIM with empirical probabilistic distortion operators to account for unavoidable measurement errors. By analyzing different combinations of models\, experiment designs\, and data distortions\, we discover practical working principles to simplify single-cell experiments while allowing for the use of inexpensive (or ‘crappy’) imaging conditions. We validate the FSP-FIM approach in HeLa cells using ICC data for glucocorticoid receptor transport and smFISH data for DUSP1 gene regulation upon stimulation with a synthetic corticosteroid. \nZoom : 997 8258 4700 (pw : 1234) \n 
URL:https://www.ibs.re.kr/bimag/event/design-of-single-cell-experiments-to-efficiently-infer-predictive-mechanistic-models-for-stochastic-gene-expression-brian-munsky/
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/2026/08/images-4-e1787995840661.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260911T100000
DTEND;TZID=Asia/Seoul:20260911T120000
DTSTAMP:20260821T043336Z
CREATED:20260821T043336Z
LAST-MODIFIED:20260821T043336Z
UID:12848-1789120800-1789128000@www.ibs.re.kr
SUMMARY:Learning to learn ecosystems from limited data - Hyeong Jun Jang
DESCRIPTION:In this talk\, we discuss the paper “Learning to learn ecosystems from limited data” by Zheng-Meng Zhai et al.\, PNAS\, 2025. \nAbstract: \nA fundamental challenge in developing data-driven approaches to ecological systems for tasks such as state estimation and prediction is the paucity of the observational or measurement data. For example\, modern machine-learning techniques such as deep learning or reservoir computing typically require a large quantity of data. Leveraging synthetic data from paradigmatic nonlinear but non-ecological dynamical systems\, we develop a meta-learning framework with time-delayed feedforward neural networks to predict the long-term behaviors of ecological systems as characterized by their attractors. We show that the framework is capable of accurately reconstructing the “dynamical climate” of the ecological system with limited data. Three benchmark population models in ecology\, namely the Hastings-Powell model\, its variant\, and the Lotka-Volterra system\, are used to demonstrate the performance of the meta-learning based prediction framework. In all cases\, enhanced accuracy and robustness have been achieved using five to seven times less training data as compared with the corresponding machine-learning method trained solely from the ecosystem data. In addition\, two real-world ecological benchmark datasets: the microbial time-series dataset and global population dynamics database\, are tested to demonstrate the applicability of the meta-learning framework to the real world. A number of issues affecting the prediction performance are addressed.
URL:https://www.ibs.re.kr/bimag/event/learning-to-learn-ecosystems-from-limited-data-hyeong-jun-jang/
LOCATION:109\, Conference room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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