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:20210101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221104T150000
DTEND;TZID=Asia/Seoul:20221104T170000
DTSTAMP:20260425T081135
CREATED:20220930T035218Z
LAST-MODIFIED:20221030T231656Z
UID:6648-1667574000-1667581200@www.ibs.re.kr
SUMMARY:Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach
DESCRIPTION:We will discuss about “Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach”\, Öcal\, Kaan\, Guido Sanguinetti\, and Ramon Grima.\, arXiv preprint arXiv:2210.05329 (2022). \nAbstract: \nThe complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist’s toolkit. For stochastic reaction networks described using the Chemical Master Equation\, commonly used methods include time-scale separation\, the Linear Mapping Approximation and state-space lumping. Despite the success of these techniques\, they appear to be rather disparate and at present no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimising a well-known information-theoretic quantity between the full model and its reduction\, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimisation approaches. In addition we derive general expressions for the propensities of a reduced system that generalise those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop\, the Michaelis-Menten enzyme system and a genetic oscillator.
URL:https://www.ibs.re.kr/bimag/event/2022-11-04-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20221108T160000
DTEND;TZID=Asia/Seoul:20221108T170000
DTSTAMP:20260425T081135
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:20221109T140000
DTEND;TZID=Asia/Seoul:20221109T150000
DTSTAMP:20260425T081135
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:20221109T160000
DTEND;TZID=Asia/Seoul:20221109T170000
DTSTAMP:20260425T081135
CREATED:20220825T012221Z
LAST-MODIFIED:20220902T003131Z
UID:6486-1668009600-1668013200@www.ibs.re.kr
SUMMARY:Modeling cell-to-cell heterogeneity from a signaling network
DESCRIPTION:Cells make individual fate decisions through linear and nonlinear regulation of gene network\, generating diverse dynamics from a single reaction pathway. In this colloquium\, I will present two topics of our recent work on signaling dynamics at cellular and patient levels. The first example is about the initial value of the model\, as a mechanism to generate different dynamics from a single pathway in cancer and the use of the dynamics for stratification of the patients [1-3]. Models of ErbB receptor signaling have been widely used in prediction of drug sensitivity for many types of cancers. We trained the ErbB model with the data obtained from cancer cell lines and predicted the common parameters of the model. By simulation of the ErbB model with those parameters and individual patient transcriptome data as initial values\, we were able to classify the prognosis of breast cancer patients and drug sensitivity based on their in silico signaling dynamics. This result raises the question whether gene expression levels\, rather than genetic mutations\, might be better suited to classify the disease. Another example is about the regulation of transcription factors\, the recipients of signal dynamics\, for target gene expression [4-6]. By focusing on the NFkB transcription factor\, we found that the opening and closing of chromatin at the DNA regions of the putative transcription factor binding sites and the cooperativity in their interaction significantly influenced the cell-to cell heterogeneity in gene expression levels. This study indicates that the noise in gene expression is rather strongly regulated by the DNA side\, even though the signals are similarly regulated in a cell population. Overall these mechanisms are important in our understanding the cell as a system for encoding and decoding signals for fate decisions and its application to human diseases. \n[References] \n[1] Nakakuki et al. Cell 2010\,\n[2] Imoto et al. iScience 2022\,\n[3] Imoto et al. STAR Protocols 2022\,\n[4] Shinohara et al. Science 2014\,\n[5] Michida et al. Cell Reports 2020\,\n[6] Wibisana et al. PLoS Genetics 2022
URL:https://www.ibs.re.kr/bimag/event/2022-11-09-colloquium/
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/2022/08/okada-250x250-1.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221111T150000
DTEND;TZID=Asia/Seoul:20221111T170000
DTSTAMP:20260425T081136
CREATED:20221028T015855Z
LAST-MODIFIED:20221107T064232Z
UID:6740-1668178800-1668186000@www.ibs.re.kr
SUMMARY:PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
DESCRIPTION:We will discuss about “PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations”\,\nZhong\, Weiheng\, and Hadi Meidani\, Computer Methods in Applied Mechanics and Engineering 403 (2023): 115664. \nAbstract\nWe propose a new class of physics-informed neural networks\, called the Physics-Informed Variational Auto-Encoder (PI-VAE)\, to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In these problems the governing equations are known but only a limited number of measurements of system parameters are available. PI-VAE consists of a variational autoencoder (VAE)\, which generates samples of system variables and parameters. This generative model is integrated with the governing equations. In this integration\, the derivatives of VAE outputs are readily calculated using automatic differentiation\, and used in the physics-based loss term. In this work\, the loss function is chosen to be the Maximum Mean Discrepancy (MMD) for improved performance\, and neural network parameters are updated iteratively using the stochastic gradient descent algorithm. We first test the proposed method on approximating stochastic processes. Then we study three types of problems related to SDEs: forward and inverse problems together with mixed problems where system parameters and solutions are simultaneously calculated. The satisfactory accuracy and efficiency of the proposed method are numerically demonstrated in comparison with physics-informed Wasserstein generative adversarial network (PI-WGAN).
URL:https://www.ibs.re.kr/bimag/event/2022-11-11-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20221118T110000
DTEND;TZID=Asia/Seoul:20221118T120000
DTSTAMP:20260425T081136
CREATED:20220825T012410Z
LAST-MODIFIED:20221114T224951Z
UID:6490-1668769200-1668772800@www.ibs.re.kr
SUMMARY:Quantifying dynamical changes in sparse\, noisy\, high-dimensional data
DESCRIPTION:The circadian clock orchestrates a vast array of behavioral and physiological processes with a 24-hour cycle\, enabling nearly all organisms — from bread mold to fruit-flies to humans — to anticipate and adapt to the Earth’s day. Entrainable by environmental cue\, the rhythm itself is generated by a self-sustained molecular oscillator present in nearly every cell. This in turn governs the expression of thousands of genes\, precisely coordinating biomolecular functions at the microscopic scale. While experimental evidence suggests that the clock is crucial for mediating the response to changes in an organism’s environment (such as temperature and food availability)\, the precise mechanisms underlying circadian regulation remain unclear. Today\, high-throughput omics assays enable us to probe these processes in molecular detail\, with the goal of making inferences about which genes are under circadian control and how their dynamics change under different environmental conditions. Analyzing this transcriptomic time-series data raises new challenges: that of characterizing dynamics when the data are noisy\, sparsely sampled in time\, and may not be strictly periodic. In this talk\, I will discuss our recent work on nonparametric methods to analyze circadian transcriptomic data by exploiting results from dynamical systems theory\, nonlinear dimension reduction\, and topological data analysis.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-colloquium/
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/2022/08/braun_rosemary.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221118T150000
DTEND;TZID=Asia/Seoul:20221118T170000
DTSTAMP:20260425T081136
CREATED:20221117T034958Z
LAST-MODIFIED:20221117T034958Z
UID:6871-1668783600-1668790800@www.ibs.re.kr
SUMMARY:Detecting critical state before phase transition of complex biological systems by hidden Markov model
DESCRIPTION:We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”\, Chen\, Pei\, et al. Bioinformatics 32.14 (2016): 2143-2150. \n  \nAbstract \nMotivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task\, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages\, i.e. before-transition state\, pre-transition state and after-transition state\, which can be considered as three different Markov processes. \nResults: By exploring the rich dynamical information provided by high-throughput data\, we present a novel computational method\, i.e. hidden Markov model (HMM) based approach\, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process)\, thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness\, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets\, and further identify the pre-transition states as well as their critical modules for three real datasets\, i.e. the acute lung injury triggered by phosgene inhalation\, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-jc-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, 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:20221123T160000
DTEND;TZID=Asia/Seoul:20221123T170000
DTSTAMP:20260425T081136
CREATED:20220825T012839Z
LAST-MODIFIED:20221119T072455Z
UID:6494-1669219200-1669222800@www.ibs.re.kr
SUMMARY:Assessing the limits of control of Covid-19 outbreaks using agent-based modeling
DESCRIPTION:Transmission of SARS-CoV-2 relies on interactions between humans. Heterogeneity and stochasticity both in human-human interactions and in the transmission of the virus give rise to non-linear infection networks that gain complexity with time. \nWe assessed the limits of control and the effect of pharmaceutical and non-pharmaceutical measures against COVID‐19 outbreaks with a detailed community‐specific agent-based model (GERDA). The demographic and geographic structure of the concrete communities influence the pattern of infection spreading. Stochastic community dynamics and limited vaccination can lead to bimodal outcomes\, rendering predictions about infection spreading and effects of nonpharmaceutical interventions uncertain. \n  \nBy comparing different vaccination strategies\, we found that the herd immunity threshold depends strongly on the applied vaccination strategy.  When vaccine supply is limited\, different vaccination strategies are optimal for the intended goal e.g.\, reducing fatalities or confining an outbreak. Prioritizing highly interactive people diminishes the risk for an infection wave\, while prioritizing the elderly minimizes fatalities. \nThe inherent stochasticity can lead to bimodality in predicting an outbreak in different low-incidence scenarios and\, thereby\, render the effect of limited NPI uncertain.  Further\, we found that for the low-incidence scenarios the reproduction number R0 is not a suitable predictor for the system behavior or the infectiousness of the virus. \nThe developed simulation platform can process and analyze dynamic COVID‐19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.
URL:https://www.ibs.re.kr/bimag/event/2022-11-23-colloquium/
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/2022/08/klipp2-250x250-1.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221130T160000
DTEND;TZID=Asia/Seoul:20221130T170000
DTSTAMP:20260425T081136
CREATED:20220825T013203Z
LAST-MODIFIED:20221124T211611Z
UID:6498-1669824000-1669827600@www.ibs.re.kr
SUMMARY:Brain dynamics during shiftwork: from maths and codes to real-world applications
DESCRIPTION:Abstract: \nCircadian clocks control the timing and 24-hour periodicity of virtually all physiological rhythms including sleep\, cognition\, and metabolism. There are optimal times for most behaviours; e.g.\, the best sleep is achieved during low circadian activity (night)\, while meals and physical exercise are best placed during high circadian activity (day) when metabolic rates\, stress hormone levels\, and blood pressure are higher. However\, the demands of our 24/7 society often result in misalignment of these environmental\, behavioural and physiological rhythms with the typical examples being shiftwork\, jetlag\, and circadian disorders. This circadian misalignment results in inadequate sleep\, fatigue\, increased risk of accidents\, and in the long-term\, development of disease including cancer and diabetes. Mathematical modelling of circadian misalignment is used to better understand the circadian and sleep regulation and make predictions to reduce risk of fatigue-related accidents. In this talk I will present an overview of our studies of shiftwork modelling and our journey from fundamental modelling research of sleep and circadian rhythms to development of software tools and real-world applications.
URL:https://www.ibs.re.kr/bimag/event/2022-11-30-colloquium/
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/2022/08/SvetlanaPostnova-250x250-1.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR