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PRODID:-//Biomedical Mathematics Group - ECPv6.17.3.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20250101T000000
END:STANDARD
END:VTIMEZONE
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260918T100000
DTEND;TZID=Asia/Seoul:20260918T120000
DTSTAMP:20260828T001201Z
CREATED:20260824T001413Z
LAST-MODIFIED:20260828T001201Z
UID:12985-1789725600-1789732800@www.ibs.re.kr
SUMMARY:Ptolemy's Equant Equates to a Universal Dynamical Clock via Machine Learning - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Ptolemy’s Equant Equates to a Universal Dynamical Clock via Machine Learning” by Jindong Zhang et al.\, arXiv\, 2026. \nAbstract: \nOscillatory dynamics arise ubiquitously in nonlinear systems\, yet identifying a physically interpretable phase and phase dynamics in nonlinear\, high-dimensional oscillations remains a central unresolved problem. Here we establish the principle of a universal dynamical clock\, a physical perspective in which oscillations of arbitrary dimensionality and geometry are equivalently represented as uniform rotation through an equant-induced nonlinear viewing coordinate\, inspired by Ptolemy’s equant and formalised through an areal-uniformity principle reminiscent of Kepler’s second law. Using a machine-learning framework\, we demonstrate the existence of such an equant for a broad class of oscillatory dynamics and construct the associated dynamical clock and phase dynamics under additive forces\, including noise\, periodic perturbations\, and coupling. Its value in uncovering new physical rules and phenomena is demonstrated by four findings: (i) collective oscillations in Escherichia coli populations obey a previously unexplained superlinear scaling law\, resolving a long-standing open problem posed in 2004; (ii) the response mechanisms of engineered genetic circuits to changes in gene expression and environmental conditions; (iii) a classical-mechanics counterpart of the Berry geometric phase emerges naturally from the phase of the dynamical clock; and (iv) optimal equant non-uniformity provides a geometric early-warning signal for critical transitions and enables prediction of critical parameters. By providing operational and system-agnostic phase dynamics that can be constructed directly from data\, the dynamical clock enables principled classification\, comparison\, and control of oscillatory systems\, and offers a new route to understanding how specific dynamical regimes support distinct functional behaviours in networked systems
URL:https://www.ibs.re.kr/bimag/event/ptolemys-equant-equates-to-a-universal-dynamical-clock-via-machine-learning-gyuyoung-hwang/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20261002T110000
DTEND;TZID=Asia/Seoul:20261002T120000
DTSTAMP:20260829T092354Z
CREATED:20260829T052114Z
LAST-MODIFIED:20260829T092354Z
UID:13001-1790938800-1790942400@www.ibs.re.kr
SUMMARY:Reservoir Computing: Machine Learning Meets Nonlinear Dynamics - Ying-Cheng Lai
DESCRIPTION:Reservoir computing has recently been exploited to solve a variety of challenging problems in complex nonlinear dynamical systems. The speaker will review some recent works from his group in this area: predicting tipping point and critical transitions\, digital twins of nonlinear dynamical systems\, parameter and trajectory tracking\, and associative memory for complex dynamical patterns. Some open questions will be discussed. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/reservoir-computing-machine-learning-meets-nonlinear-dynamics-ying-cheng-lai/
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-e1787995413852.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20261007T160000
DTEND;TZID=Asia/Seoul:20261007T170000
DTSTAMP:20260829T092657Z
CREATED:20260829T052624Z
LAST-MODIFIED:20260829T092657Z
UID:13005-1791388800-1791392400@www.ibs.re.kr
SUMMARY:Physical reservoir computing and beyond - Kohei Nakajima
DESCRIPTION:Modern computing has been fundamentally built upon the separation of hardware and software. This separation has enabled programs to be replicated and executed on interchangeable hardware\, making computation effectively “immortal.” In contrast\, recent approaches to physical computing seek to exploit the intrinsic dynamics of physical systems as computational resources\, thereby crossing the abstraction layers that traditionally separate hardware from computation. Such computation is inherently tied to its physical substrate and can therefore be regarded as “mortal computation” (Hinton\, 2022). \nIn this talk\, I will introduce physical reservoir computing (PRC) as a representative framework for mortal computation\, in which the intrinsic dynamics of physical systems perform information processing (Nakajima 2020). I will discuss how diverse physical substrates—including soft robots\, neuromorphic devices\, and living systems—can serve as computational resources\, and how their embodiment can be exploited for sensing\, computation\, and control. I will then extend this perspective beyond reservoir computing to physicalizing deep learning\, where not only inference but also learning processes are implemented in physical substrates. In particular\, I will introduce a gradient-free approach to physical deep learning and discuss recent efforts to physicalize learning in neuromorphic devices and soft robots. \nThrough these examples\, I will argue that physicalizing computation is not merely a strategy for developing energy-efficient and task-specific computing systems. It also provides a new perspective on intelligence and learning in systems whose physical dynamics are intrinsically time-varying\, adaptive\, and ultimately mortal. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/physical-reservoir-computing-and-beyond-kohei-nakajima/
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/artworks-OxsgF0kW6Jd7athY-T73SEA-t500x500-e1787995608534.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20261104T160000
DTEND;TZID=Asia/Seoul:20261104T170000
DTSTAMP:20260829T093240Z
CREATED:20260829T054752Z
LAST-MODIFIED:20260829T093240Z
UID:13017-1793808000-1793811600@www.ibs.re.kr
SUMMARY:Using Disease Trajectories for Early Detection of Disease - Søren Brunak
DESCRIPTION:Abstract: TBD \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/using-disease-trajectories-for-early-detection-of-disease/
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-3-e1787995921885.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20261127T110000
DTEND;TZID=Asia/Seoul:20261127T120000
DTSTAMP:20260829T092806Z
CREATED:20260829T053051Z
LAST-MODIFIED:20260829T092806Z
UID:13009-1795777200-1795780800@www.ibs.re.kr
SUMMARY:Using stochastic calculus to study tethered molecular reactions - Jun Allard
DESCRIPTION:Abstract: TBD \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/using-stochastic-calculus-to-study-tethered-molecular-reactions-jun-allard/
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-1-e1787995675474.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20261202T160000
DTEND;TZID=Asia/Seoul:20261202T170000
DTSTAMP:20260829T092901Z
CREATED:20260829T053347Z
LAST-MODIFIED:20260829T092901Z
UID:13013-1796227200-1796230800@www.ibs.re.kr
SUMMARY:TBD - Katarzyna Wac
DESCRIPTION:Abstract: TBD \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/tbd-katarzyna-wac/
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-2-e1787995720632.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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