BEGIN:VCALENDAR
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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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:20240101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251001T160000
DTEND;TZID=Asia/Seoul:20251001T170000
DTSTAMP:20260509T003819
CREATED:20250826T003244Z
LAST-MODIFIED:20250922T073504Z
UID:11458-1759334400-1759338000@www.ibs.re.kr
SUMMARY:Topological Data Analysis for Multiscale Biology - Heather Harrington
DESCRIPTION:Abstract \nMany processes in the life sciences are inherently multi-scale and dynamic. Spatial structures and patterns vary across levels of organisation\, from molecular to multi-cellular to multi-organism. With more sophisticated mechanistic models and data available\, quantitative tools are needed to study their evolution in space and time. Topological data analysis (TDA) provides a multi-scale summary of data. I will review the main tools in topological data analysis and how single and multi-parameter persistent homology provide insights to biological systems.
URL:https://www.ibs.re.kr/bimag/event/tbd-heather-harrington/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/Heather-Harrington.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251015T160000
DTEND;TZID=Asia/Seoul:20251015T170000
DTSTAMP:20260509T003819
CREATED:20250826T003811Z
LAST-MODIFIED:20250826T003811Z
UID:11464-1760544000-1760547600@www.ibs.re.kr
SUMMARY:Developing time-series machine learning methods to unlock new insights from large-scale biomedical resources - Aiden Doherty
DESCRIPTION:Abstract \nSmartphones and wearable devices provide a major opportunity to transform our understanding of the mechanisms\, determinants\, and consequences of diseases. For example\, around 9 in 10 people own a smartphone in the United Kingdom\, while one-fifth of US adults own wearable technologies. This high level of device ownership means that many people could contribute to health research from the comfort of their home by offering small amounts of time to share data and help address health-related questions that matter to them. A leading example is the seven day wrist-worn accelerometer data measured in 100\,000 UK Biobank participants between 2013-2015 that has led to important new findings. These include discoveries of: new genetic variants for sleep and activity; small amounts of vigorous non-exercise physical activity being associated with substantially lower mortality; and no apparent upper threshold to the benefits of physical activity with respect to cardiovascular disease risk. However\, challenges exist around cost\, access\, validity\, and training. In this talk I will review progress made in this exciting new area of health data science and share opportunities for self-supervised time-series machine learning to provide new insights into physical activity\, sleep\, heart rhythms and other exposures relevant to health and disease.
URL:https://www.ibs.re.kr/bimag/event/developing-time-series-machine-learning-methods-to-unlock-new-insights-from-large-scale-biomedical-resources-aiden-doherty/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/webp:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/Aiden-Doherty-e1756168683328.webp
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251017T100000
DTEND;TZID=Asia/Seoul:20251017T120000
DTSTAMP:20260509T003819
CREATED:20250928T143709Z
LAST-MODIFIED:20251004T064334Z
UID:11627-1760695200-1760702400@www.ibs.re.kr
SUMMARY:Simulating the Spread of Infection in Networks with Quantum Computers - Shingo Gibo
DESCRIPTION:In this talk\, we discuss the paper “Simulating the Spread of Infection in Networks with Quantum Computers” by Xiaoyang Wang\, Yinchenguang Lyu\, Changyu Yao and Xiao Yuan\, Physical Review Applied\, vol.19\, 064035 (2023). \nAbstract \nWe propose to use quantum computers to simulate infection spreading in networks. We first show the analogy between the infection distribution and spin-lattice configurations with Ising-type interactions. Then\, since the spreading process can be modeled as a classical Markovian process\, we show that the spreading process can be simulated using the evolution of a quantum thermal dynamic model with a parameterized Hamiltonian. In particular\, we analytically and numerically analyze the evolution behavior of the Hamiltonian\, and prove that the evolution simulates a classical Markovian process\, which describes the well-known epidemiological stochastic susceptible and infectious (SI) model. A practical method to determine the parameters of the thermal dynamic Hamiltonian from epidemiological inputs is exhibited. As an example\, we simulate the infection spreading process of the SARS-Cov-2 variant Omicron in a small-world network.
URL:https://www.ibs.re.kr/bimag/event/simulating-the-spread-of-infection-in-networks-with-quantum-computers-shingo-gibo/
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:20251024T100000
DTEND;TZID=Asia/Seoul:20251024T120000
DTSTAMP:20260509T003819
CREATED:20250928T144047Z
LAST-MODIFIED:20251023T035250Z
UID:11629-1761300000-1761307200@www.ibs.re.kr
SUMMARY:Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Dynamical Mean-Field Theory of Complex Systems on Sparse Directed Networks” by Fernando L. Metz\, Phys. Rev. Letters\, 2025. \nAbstract \nAlthough real-world complex systems typically interact through sparse and heterogeneous networks\, analytic solutions of their dynamics are limited to models with all-to-all interactions. Here\, we solve the dynamics of a broad range of nonlinear models of complex systems on sparse directed networks with a random structure. By generalizing dynamical mean-field theory to sparse systems\, we derive an exact equation for the path probability describing the effective dynamics of a single degree of freedom. Our general solution applies to key models in the study of neural networks\, ecosystems\, epidemic spreading\, and synchronization. Using the population dynamics algorithm\, we solve the path-probability equation to determine the phase diagram of a seminal neural network model in the sparse regime\, showing that this model undergoes a transition from a fixed-point phase to chaos as a function of the network topology.
URL:https://www.ibs.re.kr/bimag/event/dynamical-mean-field-theory-of-complex-systems-on-sparse-directed-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:20251029T160000
DTEND;TZID=Asia/Seoul:20251029T170000
DTSTAMP:20260509T003819
CREATED:20250826T004028Z
LAST-MODIFIED:20250826T004028Z
UID:11468-1761753600-1761757200@www.ibs.re.kr
SUMMARY:Dynamical data science and AI for Biology and Medicine - Luonan Chen
DESCRIPTION:Abstract \nI will present a talk on “Dynamical data science and AI” for quantifying dynamical biological processes\, disease progressions and various phenotypes\, including dynamic network biomarkers (DNB) for early-warning signals of critical transitions\, spatial-temporal information (STI) transformation for short-term time-series prediction\, knockoff conditional mutual information (KOCMI) for quantifying interventional causality\, partial cross-mapping (PCM) for causal inference among variables\, and further AI applications to medicine. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamical data-science approaches for phenotype quantification as explicable\, quantifiable\, and generalizable. In particular\, different from statistical data-science\, dynamical data-science approaches exploit the essential features of dynamical systems in terms of data\, e.g. strong fluctuations near a bifurcation point\, low-dimensionality of a center manifold or an attractor\, and phase-space reconstruction from a single variable by delay embedding theorem\, and thus are able to provide different or additional information to the traditional approaches\, i.e. statistics-based data science approaches. The dynamical data-science approaches for the quantifications of various phenotypes will further play an important role in the systematical research of various fields in biology and AI.
URL:https://www.ibs.re.kr/bimag/event/dynamical-data-science-and-ai-for-biology-and-medicine-luonan-chen/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/Luonan-Chen-e1756168815720.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251031T090000
DTEND;TZID=Asia/Seoul:20251031T103000
DTSTAMP:20260509T003819
CREATED:20251004T064151Z
LAST-MODIFIED:20251023T035154Z
UID:11754-1761901200-1761906600@www.ibs.re.kr
SUMMARY:Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology” by Ning Wei and Casey O Diekman\, J. Biol. Rhythms\, 2025.  \nAbstract \nCircadian clocks regulate many aspects of human physiology\, including cardiovascular function and drug metabolism. Administering drugs at optimal times of the day may enhance effectiveness and reduce side effects. Certain cardiac antiarrhythmic drugs have been withdrawn from the market due to unexpected proarrhythmic effects such as fatal Torsade de Pointes (TdP) ventricular tachycardia. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a recent global initiative to create guidelines for the assessment of drug-induced arrhythmias that recommends a central role for computational modeling of ion channels and in silico evaluation of compounds for TdP risk. We simulated circadian regulation of cardiac excitability and explored how dosing time of day affects TdP risk for 11 drugs previously classified into risk categories by CiPA. The model predicts that a high-risk drug taken at the most optimal time of day may actually be safer than a low-risk drug taken at the least optimal time of day. Based on these proof-of-concept results\, we advocate for the incorporation of circadian clock modeling into the CiPA paradigm for assessing drug-induced TdP risk. Since cardiotoxicity is the leading cause of drug discontinuation\, modeling cardiac-related chronopharmacology has significant potential to improve therapeutic outcomes.
URL:https://www.ibs.re.kr/bimag/event/dosing-time-of-day-impacts-the-safety-of-antiarrhythmic-drugs-in-a-computational-model-of-cardiac-electrophysiology-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
END:VCALENDAR