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X-WR-CALNAME:Biomedical Mathematics Group
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:20200101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221109T140000
DTEND;TZID=Asia/Seoul:20221109T150000
DTSTAMP:20260522T074705
CREATED:20221028T010418Z
LAST-MODIFIED:20221031T003941Z
UID:6747-1668002400-1668006000@www.ibs.re.kr
SUMMARY:Developing and designing dynamic mobile applications that transform wearable data with machine learning and mathematical models.
DESCRIPTION:Wearable analytics hold far more potential than sleep tracking or step counting. In recent years\, a number of applications have emerged which leverage the massive quantities of data being amassed by wearables around the world\, such as real-time mood detection\, advanced COVID screening\, and heart rate variability analysis. Yet packaging insights from research for success in the consumer market means prioritizing design and understandability\, while also seamlessly managing the sometimes-unreliable stream of data from the device. In this presentation\, I will discuss my own experiences building apps which interface with wearable data and process the data using mathematical modeling\, as well as recent work extending to other wearable streams and environmental controls.
URL:https://www.ibs.re.kr/bimag/event/2022-11-09/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/10/KakaoTalk_Photo_2022-10-28-10-19-48.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221108T160000
DTEND;TZID=Asia/Seoul:20221108T170000
DTSTAMP:20260522T074705
CREATED:20221028T010543Z
LAST-MODIFIED:20221028T012054Z
UID:6748-1667923200-1667926800@www.ibs.re.kr
SUMMARY:Shift: A mobile application for shift workers leveraging wearable data\, mathematical models\, and connected devices
DESCRIPTION:Shift workers experience profound circadian disruption due to the nature of their work\, which often has them working at times when their internal clock is sending a strong signal for sleep. Mathematical models can be used to generate recommendations for shift workers that shift their body’s clock to better align with their work schedules\, to help them sleep\, feel\, and perform better. In this talk\, I will discuss our recent mobile app\, Shift\, which pulls wearable data from user’s devices and generates personalized recommendations to help them manage shift work schedules. I will also discuss how this product was designed\, how it can interface with Internet of Things devices\, and how its insights can be useful for other groups beyond shift workers.
URL:https://www.ibs.re.kr/bimag/event/developing-and-designing-dynamic-mobile-applications-that-transform-wearable-data-with-machine-learning-and-mathematical-models-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/10/KakaoTalk_Photo_2022-10-28-10-19-48.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220927T160000
DTEND;TZID=Asia/Seoul:20220927T170000
DTSTAMP:20260522T074705
CREATED:20220920T065117Z
LAST-MODIFIED:20220920T080942Z
UID:6633-1664294400-1664298000@www.ibs.re.kr
SUMMARY:Causal Inference – basics and examples
DESCRIPTION:Abstract: \nIn real world\, people are interested in causality rather than association. For example\, pharmaceutical companies want to know effectiveness of their new drugs against diseases. South Korea Government officials are concerned about the effects of recent regulation with respect to an electric car subsidy from United States. Due to this reason\, causal inference has been received much attention in decades and it is now a big research field in statistics. In this seminar\, I will talk about basic idea and theory in the causal inference. Real data examples will be discussed.
URL:https://www.ibs.re.kr/bimag/event/2022-09-27-seminar/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220919T133000
DTEND;TZID=Asia/Seoul:20220919T140000
DTSTAMP:20260522T074705
CREATED:20220904T124842Z
LAST-MODIFIED:20220904T124842Z
UID:6555-1663594200-1663596000@www.ibs.re.kr
SUMMARY:Design frameworks for engineering efficient cell factory performance within host and industrial constraints
DESCRIPTION:This talk will be given online. \nAbstract: \nSynthetic biology and microbial biotechnology offer sustainable routes to the manufacture of commodity and high value chemicals from agricultural by-products instead of petrochemical feedstocks. However\, engineered gene circuits and metabolic pathways both co-opt the cell’s gene expression machinery for protein/enzyme production and divert metabolic flux away from key host biosynthetic building blocks to a desired product. These interactions impair host growth and complicate the engineering of synthetic functions. To overcome these difficulties\, we propose a host-aware engineering approach which accounts for these constraints during the circuit/pathway design process. Here we present a dynamic whole cell modelling framework of microbial growth\, metabolism\, and gene expression which captures key host-circuit/pathway interactions. By coupling our modelling framework with systems engineering approaches and multi-objective optimization tools\, we identify key design trade-offs\, make recommendations for optimal host resource usage\, and develop feedback control strategies which improve pathway productivity and yields.
URL:https://www.ibs.re.kr/bimag/event/2022-09-19-seminar-2/
LOCATION:Daejeon
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220919T130000
DTEND;TZID=Asia/Seoul:20220919T133000
DTSTAMP:20260522T074705
CREATED:20220904T124617Z
LAST-MODIFIED:20220904T125510Z
UID:6552-1663592400-1663594200@www.ibs.re.kr
SUMMARY:STEM Initiatives for Agricultural 4.0 and Beyond
DESCRIPTION:This talk will be given online. \nAbstract: \nThe establishment of UN Sustainable Development Goals (SDG) has led to widespread initiative in STEM learning and research in realising these goals. Here\, we will look at some of the works that use control engineering approaches for smart farming (also known as Agriculture 4.0) applications that addresses UN SDG Goal No. 2 – ZERO HUNGER. The tools developed have tremendous potential in optimising conditions required for enhanced crop efficiency and productivity for Agriculture 4.0.
URL:https://www.ibs.re.kr/bimag/event/2022-09-19-seminar-1/
LOCATION:Daejeon
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220816T100000
DTEND;TZID=Asia/Seoul:20220816T110000
DTSTAMP:20260522T074705
CREATED:20220815T160000Z
LAST-MODIFIED:20220815T124820Z
UID:6376-1660644000-1660647600@www.ibs.re.kr
SUMMARY:Circadian Interventions in Shift Workers
DESCRIPTION:This talk will be given online (If you want to join\, please send me an email to jaekkim@ibs.re.kr) \nAbstract \nCoupling Math with User-Centric Design Shift workers experience profound circadian disruption due to the nature of their work\, which often has them on-the-clock at times when their internal clock is sending a strong\, sleep-promoting signal. Mathematical models can be used to generate recommendations for shift workers that move their internal clock state to better align with their work schedules\, promote overall sleep\, promote alertness at key times\, or achieve other desired outcomes. Yet for these schedules to have a positive effect in the real world\, they need to be acceptable to the shift workers themselves. In this talk\, I will survey the types of schedules a shift worker may be recommended by an algorithm\, and how they can collide with the preferences of the real people being asked to follow them\, and how math can be used to arrive at new schedules that take these human factors into account.
URL:https://www.ibs.re.kr/bimag/event/2022-08-16-seminar/
LOCATION:Daejeon
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220705T160000
DTEND;TZID=Asia/Seoul:20220705T170000
DTSTAMP:20260522T074705
CREATED:20220704T220000Z
LAST-MODIFIED:20220625T051518Z
UID:6240-1657036800-1657040400@www.ibs.re.kr
SUMMARY:TENET+: a tool for reconstructing gene networks by integrating single cell expression and chromatin accessibility data
DESCRIPTION:Reconstruction of gene regulatory networks (GRNs) is a powerful approach to capture a prioritized gene set controlling cellular processes. In our previous study\, we developed TENET a GRN reconstructor from single cell RNA sequencing (scRNAseq). TENET has a superior capability to identify key regulators compared with other algorithms. However\, accurate inference of gene regulation is still challenging. Here\, we suggest an integrative strategy called TENET+ by combining single cell transcriptome and chromatin accessibility data. By applying TENET+ to a paired scRNAseq and scATACseq dataset of human peripheral blood mononuclear cells\, we found critical regulators and their epigenetic regulations for the differentiations of CD4 T cells\, CD8 T cells\, B cells and monocytes. Interestingly\, TENET+ predicted LRRFIP1 and ZBTB16 as top regulators of CD4 and CD8 T cells which were not predicted in a motif-based tool SCENIC. In sum\, TENET+ is a tool predicting epigenetic gene regulatory programs in unbiased way\, suggesting that novel epigenetic regulations can be identified by TENET+.
URL:https://www.ibs.re.kr/bimag/event/2022-07-05-seminar/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220616T160000
DTEND;TZID=Asia/Seoul:20220616T170000
DTSTAMP:20260522T074705
CREATED:20220613T130628Z
LAST-MODIFIED:20220613T130628Z
UID:6180-1655395200-1655398800@www.ibs.re.kr
SUMMARY:Deep Learning-based Uncertainty Quantification for Mathematical Models
DESCRIPTION:Over the recent years\, various methods based on deep neural networks have been developed and utilized in a wide range of scientific fields. Deep neural networks are highly suitable for analyzing time series or spatial data with complicated dependence structures\, making them particularly useful for environmental sciences and biosciences where such type of simulation model output and observations are prevalent. In this talk\, I will introduce my recent efforts in utilizing various deep learning methods for statistical analysis of mathematical simulations and observational data in those areas\, including surrogate modeling\, parameter estimation\, and long-term trend reconstruction. Various scientific application examples will also be discussed\, including ocean diffusivity estimation\, WRF-hydro calibration\, AMOC reconstruction\, and SIR calibration.  
URL:https://www.ibs.re.kr/bimag/event/2022-06-13-seminar-wonchang/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220615T160000
DTEND;TZID=Asia/Seoul:20220615T170000
DTSTAMP:20260522T074705
CREATED:20220613T144731Z
LAST-MODIFIED:20220613T144731Z
UID:6188-1655308800-1655312400@www.ibs.re.kr
SUMMARY:Optimized persistent random walk in zebrafish airineme search process
DESCRIPTION:In addition to diffusive signals\, cells in tissue also communicate via long\, thin cellular protrusions\, such as airinemes in zebrafish. Before establishing communication\, cellular protrusions must find their target cell. In this talk\, we demonstrate that the shapes of airinemes in zebrafish are consistent with a persistent random walk model. The probability of contacting the target cell is maximized for a balance between ballistic search (straight) and diffusive search (highly curved\, random). We find that the curvature of airinemes in zebrafish\, extracted from live cell microscopy\, is approximately the same value as the optimum in the simple persistent random walk model. We also explore the ability of the target cell to infer direction of the airineme’s source\, finding that there is a theoretical trade-off between search optimality and directional information. This provides a framework to characterize the shape\, and performance objectives\, of non-canonical cellular protrusions in general.
URL:https://www.ibs.re.kr/bimag/event/2022-06-15-seminar-hjkim/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220613T160000
DTEND;TZID=Asia/Seoul:20220613T170000
DTSTAMP:20260522T074705
CREATED:20220612T220000Z
LAST-MODIFIED:20220529T114627Z
UID:6088-1655136000-1655139600@www.ibs.re.kr
SUMMARY:Dynamical System Perspective for Machine Learning
DESCRIPTION:Abstract: The connection between deep neural networks and ordinary differential equations (ODEs) is an active field of research in machine learning. In this talk\, we view the hidden states of a neural network as a continuous object governed by a dynamical system. The underlying vector field is written using a dictionary representation motivated by the equation discovery method. Within this framework\, we develop models for two particular machine learning tasks: time-series classification and dimension reduction. We train the parameters in the models by minimizing a loss\, which is defined using the solution to the governing ODE. To attain a regular vector field\, we introduce a regularization term measuring the mean total kinetic energy of the flow\, which is motivated by optimal transportation theory. We solve the optimization problem using a gradient-based method where the gradients are computed via the adjoint method from optimal control theory. Through various experiments on synthetic and real-world datasets\, we demonstrate the performance of the proposed models. We also interpret the learned models by visualizing the phase plots of the underlying vector field and solution trajectories.  \n 
URL:https://www.ibs.re.kr/bimag/event/2022-06-13-sem/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220610T130000
DTEND;TZID=Asia/Seoul:20220610T140000
DTSTAMP:20260522T074705
CREATED:20220530T075825Z
LAST-MODIFIED:20220530T075825Z
UID:6133-1654866000-1654869600@www.ibs.re.kr
SUMMARY:Phase Estimation of Nonlinear State-space Model of the Circadian Pacemaker Using Level Set Kalman Filter and Raw Wearable Data
DESCRIPTION:Abstract: \nCircadian rhythm is a robust internal 24 hours timekeeping mechanism maintained by the master circadian pacemaker Suprachiasmatic Nuclei (SCN). Numerous mathematical models have been proposed to capture SCN’s timekeeping mechanism and predict the circadian phase. There has been an increased demand for applying these models to the various unexplored data sets. One potential application is on data from commercially available wearable devices\, which provide the noninvasive measurements of physiological proxies\, such as activity and heart rate. Using these physiological proxies\, we can estimate the circadian phase of the central and peripheral circadian pacemakers. Here\, we propose a new framework for estimating the circadian phase using wearable data and the Level Set Kalman Filter on the nonlinear state-space model of the human circadian pacemaker. Analysis of over 200\,000 days of wearable data from over 3\,000 subjects using our framework successfully identified misalignment in central and peripheral pacemakers with a significantly smaller uncertainty than previous methods.
URL:https://www.ibs.re.kr/bimag/event/2022-06-10-sem/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220602T160000
DTEND;TZID=Asia/Seoul:20220602T170000
DTSTAMP:20260522T074705
CREATED:20220520T122202Z
LAST-MODIFIED:20220520T122202Z
UID:6028-1654185600-1654189200@www.ibs.re.kr
SUMMARY:Introduction to matrix and tensor factorization models and related stochastic nonconvex and constrained optimization algorithms
DESCRIPTION:Abstract. Matrix/tensor factorization models such as principal component analysis \, nonnegative matrix factorization\, and CANDECOM/PARAFAC tensor decomposition provide powerful framework for dimension reduction and interpretable feature extraction\, which are important in analyzing high-dimensional data that comes in large volume. Their diverse applications include image denoising and reconstruction\, dictionary learning\, topic modeling\, and network data analysis. Fitting such factorization models to training data gives rise to various nonconvex and constrained optimization algorithms. Moreover\, such models can be trained efficiently for streaming data using stochastic/online versions of such algorithms. After introducing matrix/tensor factorization models and their applications in various contexts\, we survey some well-known nonconvex constrained optimization algorithms such as block coordinate descent and projected gradient descent. We also discuss some recent developments in general stochastic optimization algorithms such as stochastic proximal gradient descent and stochastic regularized majorization-minimization and their convergence and complexity guarantees under general Markovian streaming data.
URL:https://www.ibs.re.kr/bimag/event/2022-06-02-sem/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220421T160000
DTEND;TZID=Asia/Seoul:20220421T170000
DTSTAMP:20260522T074705
CREATED:20220420T220000Z
LAST-MODIFIED:20220416T063046Z
UID:5864-1650556800-1650560400@www.ibs.re.kr
SUMMARY:Dynamical and topological hallmarks of regulatory networks driving phenotypic plasticity and heterogeneity in cancers
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: \nMetastasis and therapy resistance cause over 90% of cancer-related deaths. Despite extensive ongoing efforts\, no unique genetic or mutational signature has emerged for metastasis. Instead\, the ability of genetically identical cells to adapt reversibly by exhibiting multiple phenotypes (phenotypic/non-genetic heterogeneity) and switch among them (phenotypic plasticity) is proposed as a hallmark of metastasis. Also\, drug resistance can emerge from such non-genetic adaptive cellular changes. However\, the origins of such non-genetic heterogeneity in most cancers are poorly understood. I will present our findings on a) how non-genetic heterogeneity emerges in a population of cancer\, and b) what design principles underlie regulatory networks enabling non-genetic heterogeneity across multiple cancers. Our results unravel how systems-levels approaches integrating mechanistic mathematical modeling with in vitro and in vivo data can identify causes and consequences of such non-genetic heterogeneity.
URL:https://www.ibs.re.kr/bimag/event/2022-04-21/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220208T113000
DTEND;TZID=Asia/Seoul:20220208T120000
DTSTAMP:20260522T074705
CREATED:20220208T173000Z
LAST-MODIFIED:20220207T064404Z
UID:5673-1644319800-1644321600@www.ibs.re.kr
SUMMARY:수리모델을 통한 전염병 통제 분석
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2022-02-09-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220208T110000
DTEND;TZID=Asia/Seoul:20220208T113000
DTSTAMP:20260522T074705
CREATED:20220208T170000Z
LAST-MODIFIED:20220207T064429Z
UID:5670-1644318000-1644319800@www.ibs.re.kr
SUMMARY:Stochastic Modeling of Foot and Mouth Diseases with Vehicle Network & Assessment of Social Distancing for Controlling COVID-19 in Korea
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2022-02-09/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220127T110000
DTEND;TZID=Asia/Seoul:20220127T130000
DTSTAMP:20260522T074705
CREATED:20220126T170000Z
LAST-MODIFIED:20220125T115708Z
UID:5507-1643281200-1643288400@www.ibs.re.kr
SUMMARY:Introduction to Bayesian Variable Selection.  
DESCRIPTION:Abstract:\nVariable selection is an approach to identifying a set of covariates that are truly important to explain the feature of a response variable. It is closely connected or belongs to model selection approaches. This talk provides a gentle introduction to Bayesian variable selection methods. The basic notion of variable selection is introduced\, followed by several Bayesian approaches with a simple application example.
URL:https://www.ibs.re.kr/bimag/event/2022-01-27-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220118T160000
DTEND;TZID=Asia/Seoul:20220118T170000
DTSTAMP:20260522T074705
CREATED:20220117T220000Z
LAST-MODIFIED:20220115T115221Z
UID:5466-1642521600-1642525200@www.ibs.re.kr
SUMMARY:다중 오믹스 분야의 현황 및 유전자-환경 상호 모델링의 필요성 (Current status of multi-omics research field and necessity of gene-by-environment (GxE) interaction modeling)
DESCRIPTION:본 발표에서는 다양한 기초 생명-의학 분야에서 생성되고 있는 오믹스 자료의 연구 개발 현황에 대해서 다룰 예정이다. 보다 큰 규모로\, 보다 빠르게\, 보다 정확하게\, 보다 정밀하게 라는 궁극적인 목표하에 이뤄지고 있는 오믹스 자료의 진화에 발맞춰\, 이를 분석하는 수리통계적 모형 역시 진화하고 있다. 그 중\, 이번 발표에서는 미국의 초 대형 정밀의료 프로젝트인 TopMed에서 진행하고 있는 COPD에 관한 다중 오믹스 자료의 통합 분석 방법 및 결과에 대해서 자세히 다룰 예정이다. 아울러 정밀의료라는 목표를 달성하기 위해 반드시 모형에서 고려해야 하는 “환경 특이적 효과”에 대해 강연할 예정이다. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-01-18/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220106T160000
DTEND;TZID=Asia/Seoul:20220106T173000
DTSTAMP:20260522T074705
CREATED:20220105T220000Z
LAST-MODIFIED:20211224T001917Z
UID:5369-1641484800-1641490200@www.ibs.re.kr
SUMMARY:Structure-based analysis of chemical reaction networks 2/2
DESCRIPTION:Inside living cells\, chemical reactions form a large web of networks. Understanding the behavior of those complex reaction networks is an important and challenging problem. In many situations\, it is hard to identify the details of the reactions\, such as the reaction kinetics and parameter values. It would be good if we can clarify what we can say about the behavior of reaction systems\, when we know the structure of reaction networks but reaction kinetics is unknown. In these talks\, I plan to introduce two approaches in this spirit. Firstly\, we will discuss the sensitivity analysis of reaction systems based on the structural information of reaction networks [1]. I will give an introduction to the method of identifying subnetworks inside which the effects of the perturbation of reaction parameters are confined. Secondly\, I will introduce the reduction method that we developed recently [2]. In those two methods\, an integer determined by the topology of a subnetwork\, which we call an influence index\, plays a crucial role. \n[1] T. Okada\, A. Mochizuki\, “Law of Localization in Chemical Reaction Networks\,” Phys. Rev. Lett. 117\, 048101 (2016); T. Okada\, A. Mochizuki\, “Sensitivity and network topology in chemical reaction systems\,” Phys. Rev. E 96\, 022322 (2017). \n[2] Y. Hirono\, T. Okada\, H. Miyazaki\, Y. Hidaka\, “Structural reduction of chemical reaction networks based on topology”\, Phys. Rev. Research 3\, 043123 (2021).
URL:https://www.ibs.re.kr/bimag/event/2022-01-06/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220105T160000
DTEND;TZID=Asia/Seoul:20220105T173000
DTSTAMP:20260522T074705
CREATED:20220104T220000Z
LAST-MODIFIED:20211224T001927Z
UID:5366-1641398400-1641403800@www.ibs.re.kr
SUMMARY:Structure-based analysis of chemical reaction networks 1/2
DESCRIPTION:Abstract: Inside living cells\, chemical reactions form a large web of networks. Understanding the behavior of those complex reaction networks is an important and challenging problem. In many situations\, it is hard to identify the details of the reactions\, such as the reaction kinetics and parameter values. It would be good if we can clarify what we can say about the behavior of reaction systems\, when we know the structure of reaction networks but reaction kinetics is unknown. In these talks\, I plan to introduce two approaches in this spirit. Firstly\, we will discuss the sensitivity analysis of reaction systems based on the structural information of reaction networks [1]. I will give an introduction to the method of identifying subnetworks inside which the effects of the perturbation of reaction parameters are confined. Secondly\, I will introduce the reduction method that we developed recently [2]. In those two methods\, an integer determined by the topology of a subnetwork\, which we call an influence index\, plays a crucial role. \nReferences \n[1] T. Okada\, A. Mochizuki\, “Law of Localization in Chemical Reaction Networks\,” Phys. Rev. Lett. 117\, 048101 (2016); T. Okada\, A. Mochizuki\, “Sensitivity and network topology in chemical reaction systems\,” Phys. Rev. E 96\, 022322 (2017). \n[2] Y. Hirono\, T. Okada\, H. Miyazaki\, Y. Hidaka\, “Structural reduction of chemical reaction networks based on topology”\, Phys. Rev. Research 3\, 043123 (2021).
URL:https://www.ibs.re.kr/bimag/event/2022-01-05/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20220104T111000
DTEND;TZID=Asia/Seoul:20220104T120000
DTSTAMP:20260522T074705
CREATED:20220103T002320Z
LAST-MODIFIED:20220103T002320Z
UID:5432-1641294600-1641297600@www.ibs.re.kr
SUMMARY:Stem cell dynamics in the intestine and stomach
DESCRIPTION:In adult tissues\, stem cells undergo clonal competition because they proliferate while the stem cell niche provides limited space. This clonal competition allows the selection of healthy stem cells over time as unfit stem cells tend to lose from the competition. It could also be a mechanism to select a supercompetitor with tumorigenic mutations\, which may lead to tumorigenesis. I am going to explain general concepts of clonal competition and how a simple model can explain the behaviour of adult stem cells. I will also show how geometric constraints affect the overall dynamics of stem cell competition.
URL:https://www.ibs.re.kr/bimag/event/2022-01-03/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20211229T150000
DTEND;TZID=Asia/Seoul:20211229T160000
DTSTAMP:20260522T074705
CREATED:20211228T210000Z
LAST-MODIFIED:20211227T001218Z
UID:5385-1640790000-1640793600@www.ibs.re.kr
SUMMARY:디지털 표현형의 진단 및 치료적 적용
DESCRIPTION:디지털 표현형의 진단 및 치료적 적용 조철현(세종충남대학교병원) 디지털 표현형(digital phenotype)은 각 개개인이 일상생활에서 사용하는 다양한 디지털 기기를 통해서 실시간으로 얻어지는 다양한 형태의 데이터를 뜻하는 것으로\, 디지털 기기의 사용이 보편화되면서 의료적 적용에 대한 가능성이 한층 높아지고 있다. 디지털 표현형은 이전에는 측정(measure)하기 힘들었던 영역에 대한 측정을 가능케 함으로써\, 의학적 평가나 진단적인 측면에서 임상적 함의를 갖는다고 볼 수 있겠다. 실제 의료현장에서 충분히 접근하고 파악하지 못했던 임상적인 의미를 도출해 내거나 새로운 발견을 할 수 있는 근거로 활용할 수도 있겠다. 임상적 상태의 변화나 치료 효과\, 예후 평가를 위한 기준으로 활용할 수도 있겠다. 또한\, 디지털치료제의 개발과 적용에 있어서 디지털 표현형을 고려하고 반영하는 것은 매우 중요한 부분이 될 것이다. 디지털치료제(Digital Therapeutics)는 사람을 대상으로 치료\, 예방\, 예후 개선 등을 목적으로 인지\, 행동\, 생활습관 등의 변화를 이끌어내기 위한 소프트웨어 형태로서 디지털 시대의 새로운 치료적 옵션으로 주목받고 있다. 특히\, 개인별\, 맞춤형 치료적 접근을 위해서는 디지털 표현형에 대한 이해를 높이고 잘 활용하는 것이 필수적이다. 본 발표에서는 디지털 표현형의 정의와 특성\, 임상적으로 어떤 함의를 가지고 있는 지에 대해 논의하고자 한다. 아울러\, 디지털 표현형의 활용 가능성\, 실제적 적용\, 디지털치료제에의 적용을 위한 방향성에 대해 발표하고자 한다.
URL:https://www.ibs.re.kr/bimag/event/2021-12-29/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20211223T163000
DTEND;TZID=Asia/Seoul:20211223T173000
DTSTAMP:20260522T074705
CREATED:20211222T220000Z
LAST-MODIFIED:20211220T121513Z
UID:5357-1640277000-1640280600@www.ibs.re.kr
SUMMARY:Methods for characterizing circadian physiology from wearables
DESCRIPTION:Abstract \nNon-invasive data collection in real-world settings with wearables provides a new opportunity for characterizing daily physiology. However\, accurate and efficient characterization remains an open problem because the complex autoregressive noise of the data makes it challenging to use a simple and efficient method for inference of clock proxies\, least squares method. In this talk\, we will introduce a simple approximation that alters the noise structure and thus enables one to use the least squares method. We will show its usefulness for real-time personalized fever detection in cancer patients.
URL:https://www.ibs.re.kr/bimag/event/2021-12-23-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20211210T150000
DTEND;TZID=Asia/Seoul:20211210T170000
DTSTAMP:20260522T074705
CREATED:20211209T210000Z
LAST-MODIFIED:20211209T112916Z
UID:5122-1639148400-1639155600@www.ibs.re.kr
SUMMARY:The Graph convolutional Networks (GCN) with Persistent Homology and its applications 3/4
DESCRIPTION:Neural Networks with the Persistent Diagrams and Graph Classification. We introduce the first paper connecting persistent diagrams to the Neural Networks by Carrier et al\,” A neural Network Layer for Persistent Diagrams and New Graph Topological Signatures\, 2019\, arXiv. We are going to analyse the End-to-End algorithm and learning processes and applications.\nCode; tensorflow at https:// github.com/MathieuCarriere/perslay
URL:https://www.ibs.re.kr/bimag/event/2021-12-10/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20211112T133000
DTEND;TZID=Asia/Seoul:20211112T153000
DTSTAMP:20260522T074705
CREATED:20211111T210000Z
LAST-MODIFIED:20211109T062309Z
UID:5120-1636723800-1636731000@www.ibs.re.kr
SUMMARY:The Graph convolutional Networks (GCN) with Persistent Homology and its application 2/4
DESCRIPTION:Simplicial Complexes\, Persistent Homology and Persistent Diagrams. After a brief review on the persistent homology( Cohen-Steiner\, Edelsbrunner\, Harer\,2010)\, we discuss constructive procedures persistent diagrams from the persistent homology. Code; 9 software packages generating persistent homology are introduced at ” A roadmap for the computation of persistent homology”\, EPJ Data Science\, a Springer Open Journal.
URL:https://www.ibs.re.kr/bimag/event/2021-11-12/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20211029T150000
DTEND;TZID=Asia/Seoul:20211029T170000
DTSTAMP:20260522T074705
CREATED:20211028T210000Z
LAST-MODIFIED:20211029T113540Z
UID:5117-1635519600-1635526800@www.ibs.re.kr
SUMMARY:The Graph convolutional Networks (GCN) with Persistent Homology and its application 1/4
DESCRIPTION:(1) GCN and its Application.\nWe introduce the GCN by reviewing the monumental paper ” Semi-Supervised Classification with the Graph Convolutional Networks”\, ICLR 2018 by Kipf and Welling. We are going to much detail the algorithm of message aggregation and passings and learning processes.\nCode ; https://github.com/tkipf/gcn \n(2) Graph Attention networks(GAT) and its Applications. Bengio et al improved greatly the capability of GCN by employing the Attention mechanism to GCN on the paper\,” Graph Attention Networks\, ICLR\,2018. We review closely the derivation of algirithms\, learning processes and discuss its super performance.\nCode; https://github.com/PetarV-/GAT
URL:https://www.ibs.re.kr/bimag/event/2021-10-29/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20211027T110000
DTEND;TZID=Asia/Seoul:20211027T120000
DTSTAMP:20260522T074705
CREATED:20211028T190000Z
LAST-MODIFIED:20211024T160104Z
UID:5058-1635332400-1635336000@www.ibs.re.kr
SUMMARY:Inferring causality in biological oscillators
DESCRIPTION:Abstract \nTBA
URL:https://www.ibs.re.kr/bimag/event/2021-10-29-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20210909T110000
DTEND;TZID=Asia/Seoul:20210909T120000
DTSTAMP:20260522T074705
CREATED:20210902T140000Z
LAST-MODIFIED:20210903T055016Z
UID:4981-1631185200-1631188800@www.ibs.re.kr
SUMMARY:COVID19 – Mathematical Modeling and Machine Learning
DESCRIPTION:Abstract \nThis presentation include the following two topics. First of all\, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore\, we validated the model with the conventional model to confirm its convergent nature. Next\, We also developed a machine learning model that predicts the mortality of infected patients by using basic patients information such as age\, residence\, comorbidity\, and past medical history. Furthermore\, we aim to establish a medical system that allows patients to check their own severity\, and informs them to visit the appropriate clinic center by referring to the past treatment details of other patients with similar severity.
URL:https://www.ibs.re.kr/bimag/event/covid19-mathematical-modeling-and-machine-learning/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20210813T110000
DTEND;TZID=Asia/Seoul:20210813T120000
DTSTAMP:20260522T074705
CREATED:20210727T190000Z
LAST-MODIFIED:20210731T015814Z
UID:4750-1628852400-1628856000@www.ibs.re.kr
SUMMARY:Bayesian model calibration and sensitivity analysis for oscillating biochemical experiments
DESCRIPTION:Abstract: Most organisms exhibit various endogenous oscillating behaviors\, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments\, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments\, however\, poses a significant statistical challenge due to its ill behavior in high dimension\, identifiability\, and numerical instability. This article devises a new Bayesian calibration strategy for oscillating biochemical models. The proposed methodology can efficiently estimate the computer experiments’ (ODE) parameters that match the physical experiments. The proposed framework is illustrated with circadian oscillations observed in a model filamentous fungus\, Neurospora crassa.
URL:https://www.ibs.re.kr/bimag/event/2021-08-13/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/07/HJK_profile-e1626653369732.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210723T150000
DTEND;TZID=Asia/Seoul:20210723T160000
DTSTAMP:20260522T074705
CREATED:20210629T013222Z
LAST-MODIFIED:20210629T013222Z
UID:4686-1627052400-1627056000@www.ibs.re.kr
SUMMARY:Scalable Modeling Approaches in Systems Immunology
DESCRIPTION:Abstract: \nSystems biology seeks to build quantitative predictive models of biological system behavior. Biological systems\, such as the mammalian immune system\, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus\, mechanistic\, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. First\, we aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions\, we developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high-dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next\, we sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal\, we built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells\, followed by experimental validation. Moreover\, by bridging these models across organizational scales\, we derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells\, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. We propose that our framework and predictions may help guide therapeutic manipulation of immune homeostasis to treat cancer and autoimmune diseases. \n  \nReferences: \nPark\, K.\, Prüstel\, T.\, Lu\, Y.\, and Tsang\, J.S. (2019). Machine learning of stochastic gene network phenotypes. BioRxiv 825943. \nWong\, H.S.\, Park\, K.\, Gola\, A.\, Baptista\, A.P.\, Miller\, C.H.\, Deep\, D.\, Lou\, M.\, Boyd\, L.F.\, Rudensky\, A.Y.\, Savage\, P.A.\, et al. (2021). A local regulatory T cell feedback circuit maintains immune homeostasis by pruning self-activated T cells. Cell S0092867421006589.
URL:https://www.ibs.re.kr/bimag/event/2021-07-23/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20210723T110000
DTEND;TZID=Asia/Seoul:20210723T120000
DTSTAMP:20260522T074705
CREATED:20210707T160416Z
LAST-MODIFIED:20210707T160416Z
UID:4715-1627038000-1627041600@www.ibs.re.kr
SUMMARY:Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC
DESCRIPTION:Abstract: Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC In this study\, we discuss model robustness. Model robustness is consistent performance over variations of parameters. We formulate a stochastic target-mediated drug (TMDD) model\, one of the pharmacokinetic models\, to capture bi-exponential drug decay in plasma. A stochastic process is used to account for system randomness\, and this process is transformed into system of stochastic differential equations. Parameter inference is performed by Approximation Bayesian Computation using the likelihood-free method. Using these collected samples\, global sensitivity of parameters is compared to Uniform and Normal distributions. This approach in the TMDD model may improve model robustness without changing the global sensitivity of parameters and the model.
URL:https://www.ibs.re.kr/bimag/event/inference-method-for-a-stochastic-target-mediated-drug-disposition-model-via-abc-mcmc/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR