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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:20230101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240913T140000
DTEND;TZID=Asia/Seoul:20240913T160000
DTSTAMP:20260423T074959
CREATED:20240827T001735Z
LAST-MODIFIED:20240904T030726Z
UID:9958-1726236000-1726243200@www.ibs.re.kr
SUMMARY:Hyun Kim\, Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage
DESCRIPTION:In this talk\, we discuss the paper “Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage” by Zhiwei Huang\, et. al.\, bioRxiv\, 2024. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nCells must adopt flexible regulatory strategies to make decisions regarding their fate\, including differentiation\, apoptosis\, or survival in the face of various external stimuli. One key cellular strategy that enables these functions is stochastic gene expression programs. However\, understanding how transcriptional bursting\, and consequently\, cell fate\, responds to DNA damage on a genome-wide scale poses a challenge. In this study\, we propose an interpretable and scalable inference framework\, DeepTX\, that leverages deep learning methods to connect mechanistic models and scRNA-seq data\, thereby revealing genome-wide transcriptional burst kinetics. This framework enables rapid and accurate solutions to transcription models and the inference of transcriptional burst kinetics from scRNA-seq data. Applying this framework to several scRNA-seq datasets of DNA-damaging drug treatments\, we observed that fluctuations in transcriptional bursting induced by different drugs could lead to distinct fate decisions: IdU treatment induces differentiation in mouse embryonic stem cells by increasing the burst size of gene expression\, while 5FU treatment with low and high dose increases the burst frequency of gene expression to induce cell apoptosis and survival in human colon cancer cells. Together\, these results show that DeepTX can be used to analyze single-cell transcriptomics data and can provide mechanistic insights into cell fate decisions.
URL:https://www.ibs.re.kr/bimag/event/hyun-kim-deep-learning-linking-mechanistic-models-to-single-cell-transcriptomics-data-reveals-transcriptional-bursting-in-response-to-dna-damage/
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:20240906T140000
DTEND;TZID=Asia/Seoul:20240906T160000
DTSTAMP:20260423T074959
CREATED:20240730T001910Z
LAST-MODIFIED:20240904T030852Z
UID:9905-1725631200-1725638400@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Predicting multiple observations in complex systems through low-dimensional embeddings
DESCRIPTION:In this talk\, we discuss the paper\, “Predicting multiple observations in complex systems through low-dimensional embeddings”\, by Tao Wu et. al.\, Nature Communications\, 2024. \nZoom : https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nForecasting all components in complex systems is an open and challenging task\, possibly due to high dimensionality and undesirable predictors. We bridge this gap by proposing a data-driven and model-free framework\, namely\, feature-and-reconstructed manifold mapping (FRMM)\, which is a combination of feature embedding and delay embedding. For a high-dimensional dynamical system\, FRMM finds its topologically equivalent manifolds with low dimensions from feature embedding and delay embedding and then sets the low-dimensional feature manifold as a generalized predictor to achieve predictions of all components. The substantial potential of FRMM is shown for both representative models and real-world data involving Indian monsoon\, electroencephalogram (EEG) signals\, foreign exchange market\, and traffic speed in Los Angeles Country. FRMM overcomes the curse of dimensionality and finds a generalized predictor\, and thus has potential for applications in many other real-world systems.
URL:https://www.ibs.re.kr/bimag/event/olive-cawiding-a-flexible-symbolic-regression-method-for-constructing-interpretable-clinical-prediction-models/
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:20240905T100000
DTEND;TZID=Asia/Seoul:20240905T110000
DTSTAMP:20260423T074959
CREATED:20240830T085940Z
LAST-MODIFIED:20240904T030529Z
UID:10010-1725530400-1725534000@www.ibs.re.kr
SUMMARY:Make Your Science Friendly: A Guide to Engaging Visuals - Sunghwan Bae
DESCRIPTION:The talk will be hybrid\, participants may join via Zoom with the following link: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09
URL:https://www.ibs.re.kr/bimag/event/make-your-science-friendly-a-guide-to-engaging-visuals-sunghwan-bae/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 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:20240904T160000
DTEND;TZID=Asia/Seoul:20240904T170000
DTSTAMP:20260423T074959
CREATED:20240829T001214Z
LAST-MODIFIED:20240830T025822Z
UID:9974-1725465600-1725469200@www.ibs.re.kr
SUMMARY:Quantitative Ecology of Host-associated Microbiomes - Lei Dai
DESCRIPTION:Abstract: The realization that microbiomes\, associated with virtually all multicellular organisms\, have tremendous impact on their host health is considered as one of the most important scientific discoveries in the last decade. The host associated microbiomes\, composed of tens to hundreds of co-existing microbial species\, are highly heterogenous at multiple scales (e.g. between different hosts and within a host). In this talk\, I will share our recent works on understanding the heterogeneity of complex microbial communities\, and how these conceptual and technological advances in microbial ecology pave the way for precision microbiome engineering to prevent and treat diseases.
URL:https://www.ibs.re.kr/bimag/event/quantitative-ecology-of-host-associated-microbiomes/
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/2024/08/lei-dai-1-e1724986646267.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240816T140000
DTEND;TZID=Asia/Seoul:20240816T160000
DTSTAMP:20260423T074959
CREATED:20240730T001615Z
LAST-MODIFIED:20240730T001615Z
UID:9903-1723816800-1723824000@www.ibs.re.kr
SUMMARY:Kevin Spinicci\, SMSSVD : Submatrix selection singular value decomposition
DESCRIPTION:In this talk\, we discuss the paper\, “SMSSVD : Submatrix selection singular value decomposition”\, by Rasmus Henningsson and Magnus Fontes\, Bioinformatics\, 2019. \nAbstract \n\nMotivation\nHigh throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data analysis. Existing methods\, aimed at signal reconstruction and deconvolution\, in general\, are either supervised\, contain parameters that need to be estimated or present other types of ad hoc features. We here introduce SubMatrix Selection Singular Value Decomposition (SMSSVD)\, a parameter-free unsupervised signal decomposition and dimension reduction method\, designed to reduce noise\, adaptively for each low-rank-signal in a given data matrix\, and represent the signals in the data in a way that enable unbiased exploratory analysis and reconstruction of multiple overlaid signals\, including identifying groups of variables that drive different signals. \n\n\nResults\nThe SMSSVD method produces a denoised signal decomposition from a given data matrix. It also guarantees orthogonality between signal components in a straightforward manner and it is designed to make automation possible. We illustrate SMSSVD by applying it to several real and synthetic datasets and compare its performance to golden standard methods like PCA (Principal Component Analysis) and SPC (Sparse Principal Components\, using Lasso constraints). The SMSSVD is computationally efficient and despite being a parameter-free method\, in general\, outperforms existing statistical learning methods.
URL:https://www.ibs.re.kr/bimag/event/kevin-spinicci-smssvd-submatrix-selection-singular-value-decomposition/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240809T140000
DTEND;TZID=Asia/Seoul:20240809T160000
DTSTAMP:20260423T074959
CREATED:20240730T001308Z
LAST-MODIFIED:20240730T001308Z
UID:9901-1723212000-1723219200@www.ibs.re.kr
SUMMARY:Gyuyoung Hwang\, A universal description of stochastic oscillators
DESCRIPTION:In this talk\, we discuss the paper “A universal description of stochastic oscillators”\, by Alberto Perez-Cervera et. al.\, PNAS\, 2023. \nAbstract  \nMany systems in physics\, chemistry\, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms\, for example\, linear dynamics of a stable focus with fluctuations\, limit-cycle systems perturbed by noise\, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins\, the phenomenology of random oscillations can be strikingly similar. Here\, we introduce a nonlinear transformation of stochastic oscillators to a complex-valued function Q1*(x) that greatly simplifies and unifies the mathematical description of the oscillator’s spontaneous activity\, its response to an external time-dependent perturbation\, and the correlation statistics of different oscillators that are weakly coupled. The function Q1* (x) is the eigenfunction of the Kolmogorov backward operator with the least negative (but nonvanishing) eigenvalue λ1 = μ1 + iω1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency ω1 and half-width μ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter\, centered around ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable\, provides simple characteristics for the coherence of the random oscillation\, and gives a framework for the description of weakly coupled oscillators.
URL:https://www.ibs.re.kr/bimag/event/gyuyoung-hwang-a-universal-description-of-stochastic-oscillators/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240807T160000
DTEND;TZID=Asia/Seoul:20240807T170000
DTSTAMP:20260423T074959
CREATED:20240805T002501Z
LAST-MODIFIED:20240807T012802Z
UID:9911-1723046400-1723050000@www.ibs.re.kr
SUMMARY:Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling
DESCRIPTION:Abstract: \nWearable biosensors measure physiological variables with high temporal resolution over multiple days and are increasingly employed in clinical settings\, such as continuous glucose monitoring in diabetes care. Such datasets bring new opportunities and challenges\, and patients\, clinicians\, and researchers are today faced with a common challenge: how to best summarize and capture relevant information from multimodal wearable time series? Here\, we aim to provide insights into individual glucose dynamics and their relationships with food and drink ingestion\, time of day\, and coupling with other physiological states such as physical and heart activity. To this end\, we generate and analyze multiple wearable device data through the lens of a parsimonious mathematical model with interpretable components and parameters. A key innovation of our method is that the models are learned on a personalized level for each participant within a Bayesian framework\, which enables the characterization of interindividual heterogeneity in features such as the glucose response time following meals or underlying circadian baseline rhythm. I will also describe how we are currently applying this framework in the context of gestational diabetes.
URL:https://www.ibs.re.kr/bimag/event/uncovering-personalized-glucose-responses-and-circadian-rhythms-from-multiple-wearable-biosensors-with-bayesian-dynamical-modeling/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2024/08/20240305_234410-e1722990001623.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240802T140000
DTEND;TZID=Asia/Seoul:20240802T160000
DTSTAMP:20260423T074959
CREATED:20240729T000958Z
LAST-MODIFIED:20240729T001043Z
UID:9893-1722607200-1722614400@www.ibs.re.kr
SUMMARY:Yun Min Song\, RNA velocity of single cells
DESCRIPTION:In this talk\, we discuss the paper “RNA velocity of single sells” by Gioele La Manno et.al.\, Nature\, 2018. \nAbstract \nRNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy\, sensitivity and throughput. However\, this approach captures only a static snapshot at a point in time\, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage\, demonstrate its use on multiple published datasets and technical platforms\, reveal the branching lineage tree of the developing mouse hippocampus\, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics\, particularly in humans.
URL:https://www.ibs.re.kr/bimag/event/yun-min-song-rna-velocity-of-single-cells/
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:20240731T160000
DTEND;TZID=Asia/Seoul:20240731T170000
DTSTAMP:20260423T074959
CREATED:20240728T141528Z
LAST-MODIFIED:20240728T141528Z
UID:9889-1722441600-1722445200@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Koopman representation: Linear representation – not an approximation – of nonlinear dynamics
DESCRIPTION:Abstract: A system of ordinary differential equations (ODEs) is one of the most widely used tools to describe a deterministic dynamical system. In general\, such ODEs involve nonlinear equations\, which make analysis of dynamical systems difficult. In this talk\, we introduce Koopman theory\, which offers a linear representation – not an approximation – of nonlinear dynamics. In particular\, we present a data-driven algorithm to find such a linear representation
URL:https://www.ibs.re.kr/bimag/event/hyukpyo-hong-koopman-representation-linear-representation-not-an-approximation-of-nonlinear-dynamics/
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240731T103000
DTEND;TZID=Asia/Seoul:20240731T120000
DTSTAMP:20260423T074959
CREATED:20240730T102340Z
LAST-MODIFIED:20260404T011149Z
UID:9908-1722421800-1722427200@www.ibs.re.kr
SUMMARY:IBS BIMAG 2024 Summer Internship workshop
DESCRIPTION:  \n\n\n\nPresentor(s)\nMentor\nTalk title\n\n\nJaehun Jeong\nGyuyoung Hwang\nAnalyzing coupled SCN cell frequencies of mammals for multi-step transcriptional model\n\n\nHyunsuk Choo\, Yonghee Lee\nSeok Joo Chae\nDevelopment of a data-driven causality detection method using Taken’s Theorem\n\n\nJuhyeon Kim\nDongju Lim\nAccurate initial condition for circadian pacemaker model estimating the circadian phase\n\n\nKyeongtae Ko\nDongju Lim\nAccurate initial condition estimation of exposed individuals in SEIR model\n\n\nAshley L Lawas\nOlive R Cawiding\nAdvancing causal inference in complex systems through ODE-based methods\n\n\nSieun Lee\nOlive R Cawiding\nImproving efficiency of sleep disorder diagnosis via SymScore\n\n\nDaniel Shin\, Anar Rzayev\nOlive R Cawiding\nImproving Sleep Disorder Diagnosis Questionnaire (SLEEPS) by integrating Lifestyle Factors into Machine-learning algorithms\n\n\nShubhangi Kumar\nPan Li\nModelling cardiac pacemaking dysfunction in heart failure progression\n\n\nYejin Lee\nPan Li\nModelling beta-adrenergic regulation of calcium dynamics in human ventricular myocytes\n\n\nHyungu Lee\nYun Min Song\nPSG sleep pattern prediction from actigraphy data\n\n\nYujin Park\nYun Min Song\nValidating the usefulness of anchor sleep from sleep-wake patterns using ESS\n\n\nYoon Kim\nYun Min Song\nPredicting sleep onset latency\n\n\n\n 
URL:https://www.ibs.re.kr/bimag/event/summer-intern-workshop-2024/
CATEGORIES:Lunch Lab Meeting Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240726T140000
DTEND;TZID=Asia/Seoul:20240726T160000
DTSTAMP:20260423T074959
CREATED:20240624T003604Z
LAST-MODIFIED:20240709T021120Z
UID:9740-1722002400-1722009600@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, Temperature compensation through kinetic regulation in biochemical oscillators.
DESCRIPTION:In this talk\, we discuss the paper “Temperature compensation through kinetic regulation in biochemical oscillators” by HaochenFu\, Chenyi Fei\, Qi Ouyang\, and Yuhai Tu\, to appear in PNAS.  \nAbstract  \nAlthough individual kinetic rates in biochemical reactions are sensitive to temperature\, most circadian clocks exhibit a relatively constant period across a wide range of temperatures\, a phenomenon called temperature compensation (TC). However\, it remains unclear how different biochemical oscillators achieve TC. In this study\, using representative biochemical oscillator models with different underlying reaction networks\, we demonstrate a general kinetic regulation mechanism for TC regardless of the network structure. We find that by driving the system into a regime far from onset where the period increases strongly with at least one of the kinetic rates in the system to balance its inverse dependence on other rates\, robust TC can be achieved for a wide range of parameters in different networks. 
URL:https://www.ibs.re.kr/bimag/event/eui-min-jeong-temperature-compensation-through-kinetic-regulation-in-biochemical-oscillators/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240719T140000
DTEND;TZID=Asia/Seoul:20240719T160000
DTSTAMP:20260423T074959
CREATED:20240624T003304Z
LAST-MODIFIED:20240715T001749Z
UID:9738-1721397600-1721404800@www.ibs.re.kr
SUMMARY:Dongju Lim\, Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics.
DESCRIPTION:In this talk\, we discuss the paper “Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics” by D. F. Anderson\, B. Ermentrout and P. J. Thomas\, Journal of Computational Neuroscience\, 2015. \nAbstract \nIn this paper we provide two representations for stochastic ion channel kinetics\, and compare the perfor- mance of exact simulation with a commonly used numer- ical approximation strategy. The first representation we present is a random time change representation\, popular- ized by Thomas Kurtz\, with the second being analogous to a “Gillespie” representation. Exact stochastic algorithms are provided for the different representations\, which are prefer- able to either (a) fixed time step or (b) piecewise constant propensity algorithms\, which still appear in the literature. As examples\, we provide versions of the exact algorithms for the Morris-Lecar conductance based model\, and detail the error induced\, both in a weak and a strong sense\, by the use of approximate algorithms on this model. We include ready-to-use implementations of the random time change algorithm in both XPP and Matlab. Finally\, through the consideration of parametric sensitivity analysis\, we show how the representations presented here are useful in the development of further computational methods. The gen- eral representations and simulation strategies provided here are known in other parts of the sciences\, but less so in the present setting.
URL:https://www.ibs.re.kr/bimag/event/dongju-lim-feedback-between-stochastic-gene-networks-and-population-dynamics-enables-cellular-decision-making/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240712T140000
DTEND;TZID=Asia/Seoul:20240712T160000
DTSTAMP:20260423T074959
CREATED:20240624T002744Z
LAST-MODIFIED:20240709T021017Z
UID:9734-1720792800-1720800000@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Holimap: an accurate and efficient method for solving stochastic gene network dynamics
DESCRIPTION:In this talk\, we discuss the paper “Holimap: an accurate and efficient method for solving stochastic gene network dynamics” by Chen Jia and Ramon Grima\, bioRxiv\, 2024. \nAbstract  \nGene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of protein numbers for each gene vary across parameter space. To overcome these difficulties\, here we present Holimap (high-order linear-mapping approximation)\, an approach that approximates the protein number distributions of a complex gene network by the distributions of a much simpler reaction system. We demonstrate Holimap’s computational advantages over conventional methods by applying it to predict the stochastic time-dependent protein dynamics of several gene regulatory networks\, ranging from simple autoregulatory loops to complex randomly connected networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.
URL:https://www.ibs.re.kr/bimag/event/seokjoo-chae-feedback-between-stochastic-gene-networks-and-population-dynamics-enables-cellular-decision-making/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240625T140000
DTEND;TZID=Asia/Seoul:20240625T150000
DTSTAMP:20260423T074959
CREATED:20240623T122242Z
LAST-MODIFIED:20240625T002727Z
UID:9721-1719324000-1719327600@www.ibs.re.kr
SUMMARY:Hyungsuk Tak\, Statistical Challenges in Astronomical Time Delay Estimation (Cancelled)
DESCRIPTION:I present time delay estimation problems in astronomy as a part of time delay cosmography to infer the Hubble constant\, the current expansion rate of the Universe. Time delay cosmography is based on strong gravitational lensing\, an effect that multiple images of the same astronomical object appear in the sky because paths of the light (from the object to the Earth) are bent by the strong gravitational field of an intervening galaxy. By measuring brightness of multiply-lensed images\, we obtain several time series data of brightness\, and time delays can be inferred by modeling these data. I focus on challenges in modeling these time series data and computational issues in fitting the models. In particular\, I explain continuous-time auto-regressive models to account for stochastic variability of the time series data\, and several Monte Carlo samplers to sample from the target posterior distributions with multiple modes. At the end of the talk\, I show how these time delays estimates contribute to the Hubble constant estimation. Two main references of this talk are arXiv2207.09327 and arXiv2308.13018.
URL:https://www.ibs.re.kr/bimag/event/hyungsuk-tak-statistical-challenges-in-astronomical-time-delay-estimation/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 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:20240621T140000
DTEND;TZID=Asia/Seoul:20240621T160000
DTSTAMP:20260423T074959
CREATED:20240531T045615Z
LAST-MODIFIED:20240620T065839Z
UID:9654-1718978400-1718985600@www.ibs.re.kr
SUMMARY:Brenda Gavina\, A modified shuffled frog leaping algorithm with inertia weight
DESCRIPTION:In this talk\, we will discuss the paper\, “A modified shuffled frog leaping algorithm with inertia weight”\, by Zhuanzhe Zhao et.al. \, Scientific Reports\, 2024. \nAbstract  \nThe shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm\, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However\, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings\, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA\, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally\, we have compared the solution of the 7 benchmark functions with the original SFLA\, other improved SFLAs\, genetic algorithm\, PSO\, artificial bee colony algorithm\, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property\, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.
URL:https://www.ibs.re.kr/bimag/event/brenda-gavina-computational-screen-for-sex-specific-drug-effects-in-a-cardiac-fibroblast-signaling-network-model/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240614T140000
DTEND;TZID=Asia/Seoul:20240614T160000
DTSTAMP:20260423T074959
CREATED:20240531T044753Z
LAST-MODIFIED:20240614T002219Z
UID:9652-1718373600-1718380800@www.ibs.re.kr
SUMMARY:Hyun Kim\, MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing datamics data with TDEseq
DESCRIPTION:In this talk\, we discuss the paper\, “MultiK: an automated tool to determine optimal cluster numbers in single-cell RNA sequencing data” by Siyao Liu et.al.  Genome Biology\, 2024. \nAbstract  \nSingle-cell RNA sequencing (scRNA-seq) provides new opportunities to characterize cell populations\, typically accomplished through some type of clustering analysis. Estimation of the optimal cluster number (K) is a crucial step but often ignored. Our approach improves most current scRNA-seq cluster methods by providing an objective estimation of the number of groups using a multi-resolution perspective. MultiK is a tool for objective selection of insightful Ks and achieves high robustness through a consensus clustering approach. We demonstrate that MultiK identifies reproducible groups in scRNA-seq data\, thus providing an objective means to estimating the number of possible groups or cell-type populations present. \n 
URL:https://www.ibs.re.kr/bimag/event/hyun-kim-powerful-and-accurate-detection-of-temporal-gene-expression-patterns-from-multi-sample-multi-stage-single-cell-transcriptomics-data-with-tdeseq/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240607T140000
DTEND;TZID=Asia/Seoul:20240607T160000
DTSTAMP:20260423T074959
CREATED:20240531T044227Z
LAST-MODIFIED:20240606T054542Z
UID:9650-1717768800-1717776000@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
DESCRIPTION:In this talk\, we discuss the paper “Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe”\, by Xiaojie Qiu  et.al.\, Cell Syst. 2020. \nAbstract  \nHere\, we present Scribe (https://github.com/aristoteleo/Scribe-py)\, a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for “pseudotime”-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as “RNA velocity” restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.
URL:https://www.ibs.re.kr/bimag/event/olive-cawiding-causalxtract-a-flexible-pipeline-to-extract-causal-effects-from-live-cell-time-lapse-imaging-data/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240531T140000
DTEND;TZID=Asia/Seoul:20240531T160000
DTSTAMP:20260423T074959
CREATED:20240428T181746Z
LAST-MODIFIED:20240528T001427Z
UID:9538-1717164000-1717171200@www.ibs.re.kr
SUMMARY:Lucas MacQuarrie\, Data driven governing equations approximation using deep neural networks
DESCRIPTION:We will discuss about “Data driven governing equations approximation using deep neural networks” Journal of Computational Physics (2019). \nAbstract \n\nWe present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular\, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration. We then present two multi-step methods\, recurrent ResNet (RT-ResNet) method and recursive ReNet (RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps\, whereas the RS-ResNet is an adaptive multi-step method using variable time steps. All three methods presented here are based on integral form of the underlying dynamical system. As a result\, they do not require time derivative data for equation recovery and can cope with relatively coarsely distributed trajectory data. Several numerical examples are presented to demonstrate the performance of the methods.
URL:https://www.ibs.re.kr/bimag/event/2024-05-31-jc/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240527T160000
DTEND;TZID=Asia/Seoul:20240527T170000
DTSTAMP:20260423T074959
CREATED:20240326T150018Z
LAST-MODIFIED:20240326T150018Z
UID:9426-1716825600-1716829200@www.ibs.re.kr
SUMMARY:Timothy L. Downing\, Biophysical Regulation of Cell Fate\, from ECM to Nuclear Chromatin
DESCRIPTION:Abstract: The Downing lab investigates the intricate biophysical interactions between cells and their environment\, elucidating their role in modulating adult cell behavior and phenotypic transitions via epigenetic regulation of gene expression. Leveraging diverse genome-scale sequencing techniques\, we decipher mechanisms underlying cell fate transitions mediated through dynamic regulation of nuclear chromatin and heterogeneous gene activity. Our research endeavors aim to engineer molecular tools and biomaterials to synthetically modulate the epigenome\, enhancing control over cell fate and behavior. In this seminar presentation\, I will focus on how signaling pathways governing cell-cell and cell-ECM communication contribute to observed fate transitions during the acquisition of stemness phenotypes and lineage plasticity\, particularly in iPSC reprogramming and cancer contexts.
URL:https://www.ibs.re.kr/bimag/event/timothy-l-downing-biophysical-regulation-of-cell-fate-from-ecm-to-nuclear-chromatin/
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:20240524T140000
DTEND;TZID=Asia/Seoul:20240524T160000
DTSTAMP:20260423T074959
CREATED:20240428T181352Z
LAST-MODIFIED:20240428T181352Z
UID:9535-1716559200-1716566400@www.ibs.re.kr
SUMMARY:Kévin SPINICCI\, PenDA\, a rank-based method for personalized differential analysis: Application to lung cancer
DESCRIPTION:We will discuss about “PenDA\, a rank-based method for personalized differential analysis: Application to lung cancer” Plos Computational Biology (2020). \nAbstract \n\nThe hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here\, we propose a novel method\, PenDA\, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors\, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts\, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA\, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular\, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust\, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access\, user-friendly R package is available at https://github.com/bcm-uga/penda.
URL:https://www.ibs.re.kr/bimag/event/2024-05-24-jc/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240517T140000
DTEND;TZID=Asia/Seoul:20240517T160000
DTSTAMP:20260423T074959
CREATED:20240428T180844Z
LAST-MODIFIED:20240513T082339Z
UID:9532-1715954400-1715961600@www.ibs.re.kr
SUMMARY:Gyuyoung Hwang\, Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
DESCRIPTION:We will discuss about “Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming”\, Cell (2019). \n  \nAbstract \nUnderstanding the molecular programs that guide differentiation during development is a major challenge. Here\, we introduce Waddington-OT\, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315\,000 single-cell RNA sequencing (scRNA-seq) profiles\, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent\, extra-embryonic\, and neural cells\, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.
URL:https://www.ibs.re.kr/bimag/event/2024-05-17-jc/
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:20240510T110000
DTEND;TZID=Asia/Seoul:20240510T120000
DTSTAMP:20260423T074959
CREATED:20240219T044117Z
LAST-MODIFIED:20240728T142006Z
UID:9242-1715338800-1715342400@www.ibs.re.kr
SUMMARY:Jingyi Jessica Li\, ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping
DESCRIPTION:Abstract: In typical single-cell RNA-seq (scRNA-seq) data analysis\, a clustering algorithm is applied to find discrete cell clusters as putative cell types\, and then a statistical test is employed to identify the differentially expressed (DE) genes between the cell clusters. However\, this common procedure suffers the “double dipping” issue: the same data are used twice to find discrete cell clusters as putative cell types and DE genes as potential cell-type marker genes\, leading to false-positive cell-type marker genes even when the cell clusters are spurious. To overcome this challenge\, we propose ClusterDE\, a post-clustering DE method for controlling the false discovery rate (FDR) of identified DE genes regardless of clustering quality\, which can work as an add-on to popular pipelines such as Seurat. The core idea of ClusterDE is to generate real-data-based synthetic null data containing only one cell type\, in contrast to the real data\, for evaluating the whole procedure of clustering followed by a DE test. Using comprehensive simulation and real data analysis\, we show that ClusterDE has solid FDR control and the ability to identify canonical cell-type marker genes as top DE genes\, distinguishing them from common housekeeping genes. Notably\, the DE genes identified by ClusterDE are informative markers for discrete cell types and can guide the merging of spurious clusters. ClusterDE is fast\, transparent\, and adaptive to a wide range of clustering algorithms and DE tests.
URL:https://www.ibs.re.kr/bimag/event/jingyi-jessica-li-clusterde-a-post-clustering-differential-expression-de-method-robust-to-false-positive-inflation-caused-by-double-dipping/
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/2024/02/Jessica-li-e1722176393718.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240503T150000
DTEND;TZID=Asia/Seoul:20240503T160000
DTSTAMP:20260423T074959
CREATED:20240429T083052Z
LAST-MODIFIED:20240502T050439Z
UID:9543-1714748400-1714752000@www.ibs.re.kr
SUMMARY:(Cancelled) Sung Woong Cho - Estimating the distribution of parameters in differential equations with repeated cross-sectional data
DESCRIPTION:This presentation introduces an approach for estimating parameter distributions in dynamic systems modeled by differential equations. Traditional parameter estimation techniques often struggle with Repeated Cross-Sectional (RCS) data\, characteristic of many real-world scenarios where continuous data collection is impractical or impossible. Previous approaches\, like employing mean values or leveraging Gaussian Processes for time series generation\, fail to capture system parameters’ true heterogeneity and distributions. We introduce a novel approach to infer accurate parameter distributions from RCS data. By constructing artificial trajectories from randomly selected observations at each time point and iteratively refining parameter estimates to minimize discrepancies between observed and modeled dynamics\, our method enables the derivation of true parameter distributions even for RCS data. We demonstrate the efficacy of our method through its application to models including exponential growth\, logistic population dynamics\, and target cell-limited models with delayed virus production. Our findings offer a robust framework for understanding the full complexity of dynamic systems\, paving the way for more precise and insightful analyses across various fields of study.
URL:https://www.ibs.re.kr/bimag/event/sung-woong-cho-estimating-the-distribution-of-parameters-in-differential-equations-with-repeated-cross-sectional-data/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240503T110000
DTEND;TZID=Asia/Seoul:20240503T120000
DTSTAMP:20260423T074959
CREATED:20240219T043810Z
LAST-MODIFIED:20240728T142252Z
UID:9239-1714734000-1714737600@www.ibs.re.kr
SUMMARY:Pedro Mendes\, Multiscale hybrid differential equation and agent-based models
DESCRIPTION:Abstract: Biological phenomena are notorious for crossing several temporal and spatial scales. While often it may be sufficient to focus on a single scale\, it is not rare that we have to consider several scales simultaneously. Computational modeling and simulation of biological systems thus frequently requires to include diverse temporal and spatial scales. A popular approach in systems biology is to combine differential equations and agent-based models\, where usually small sets of differential equations are used to represent the internal state of each cell\, with the cells being represented as interacting autonomous agents on a lattice. This type of hybrid models allows for parallel solution of smaller sets of differential equations rather than the solution of a single but very large set of differential equations. At certain discrete times\, the agents are allowed to communicate\, and only then are the different sets of differential equations able to influence each other. This time discretization of the cell-cell interactions carries an inherent approximation error compared to the continuous interaction of these cells in the single model of a large set of coupled differential equations. Here we study this approximation error and investigate the conditions in which it becomes negligible\, thus defining the domain where the multiscale approach is valid. The approach is illustrated with a classic model of Drosophila segment polarity network\, where a model based on a full set of differential equations (the original version of that model) is compared with a hybrid model combining differential equations and agent-based approach (implemented with the open source software simulators Vivarium and COPASI). This study is also relevant to other hybrid simulations\, such as those representing “whole-cell models”\, where partitions may be done at other organizational scales.
URL:https://www.ibs.re.kr/bimag/event/pedro-mendes-multiscale-hybrid-differential-equation-and-agent-based-models/
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/2024/02/Pedro-Mendes-e1722176551946.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240426T140000
DTEND;TZID=Asia/Seoul:20240426T160000
DTSTAMP:20260423T074959
CREATED:20240326T142526Z
LAST-MODIFIED:20240423T002345Z
UID:9423-1714140000-1714147200@www.ibs.re.kr
SUMMARY:Yun Min Song\, An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells
DESCRIPTION:We will discuss about “An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells”\, ArXiv (2023). \n  \nAbstract \nDetecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology\, rhythms in time series (for instance gene expression\, eclosion\, egg-laying and feeding) datasets tend to be low amplitude\, display large variations amongst replicates\, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method\, ODeGP (Oscillation Detection using Gaussian Processes)\, which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data\, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values\, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next\, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations\, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally\, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells\, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly\, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene\, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation\, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories\, not genome-wide datasets.
URL:https://www.ibs.re.kr/bimag/event/2024-04-26-jc/
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:20240419T100000
DTEND;TZID=Asia/Seoul:20240419T120000
DTSTAMP:20260423T074959
CREATED:20240326T142035Z
LAST-MODIFIED:20240415T082050Z
UID:9421-1713520800-1713528000@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, Phenotypic switching in gene regulatory networks
DESCRIPTION:We will discuss about “Phenotypic switching in gene regulatory networks”\, PNAS (2014). \n  \nAbstract \nNoise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype\, the quantification of which is important for understanding cellular decision-making. Here\, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation\, we rigorously show that\, in the limit of slow promoter dynamics\, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks\, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically\, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator\, and to hysteresis in phenotypic induction\, thus highlighting the ability of regulatory networks to retain memory.
URL:https://www.ibs.re.kr/bimag/event/2024-04-19-jc/
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:20240412T110000
DTEND;TZID=Asia/Seoul:20240412T120000
DTSTAMP:20260423T074959
CREATED:20240219T043247Z
LAST-MODIFIED:20240728T142452Z
UID:9233-1712919600-1712923200@www.ibs.re.kr
SUMMARY:Michael Chee\, How Data from Sleep Trackers Can Transform Our Understanding of Sleep
DESCRIPTION:Abstract: Wearable health trackers have shifted from gadgets for sports enthusiasts to valuable health sentinels over the last few years and that transformation is gathering pace. What do these devices really measure about sleep? What types of devices are there\, and which can we trust? Which of the many sleep measures reported\, contribute to a better understanding of sleep\, sleep habits and sleep health? How can sleep data improve personal and public health? What new uses of sensor data can we look forward to in coming years? I seek to shed light on these issues in a presentation that will focus on distinguishing scientific and health-oriented perspectives from consumer-facing ones.
URL:https://www.ibs.re.kr/bimag/event/michael-chee-how-data-from-sleep-trackers-can-transform-our-understanding-of-sleep-2/
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/2024/02/Michael-Chee-e1722176681984.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240405T110000
DTEND;TZID=Asia/Seoul:20240405T120000
DTSTAMP:20260423T074959
CREATED:20240219T043532Z
LAST-MODIFIED:20240728T142635Z
UID:9236-1712314800-1712318400@www.ibs.re.kr
SUMMARY:Brian P. Delisle\, Circadian Regulation of Cardiac Electrophysiology
DESCRIPTION:Abstract: Circadian rhythms in physiology and behavior are regulated by circadian clocks\, ubiquitous molecular transcriptional-translational feedback loops that cycle with a periodicity of ~24 hours. Circadian clocks serve as cellular timekeepers regulating important cell-type specific functions. The phase of circadian rhythms and circadian clocks throughout the body are entrained to the light cycle by signals originating in the suprachiasmatic nucleus of the hypothalamus. The functional importance of circadian clocks in cardiomyocytes is underscored by the observation that genetic disruption of the circadian clock mechanism in mouse hearts alters the electrocardiogram (ECG)\, cardiac action potential\, and size of individual ionic currents. This presentation discusses recent basic science studies showing how daily environmental\, behavioral\, and circadian rhythms impact cardiac electrophysiology and cardiac arrhythmogenesis at the systems\, tissue\, and molecular levels. These studies provide new insights into how daily environmental\, behavioral\, and circadian rhythms affect the timing of cardiovascular events\, and they are starting to identify chronotherapeutic strategies that may mitigate the risk for cardiac arrhythmias.
URL:https://www.ibs.re.kr/bimag/event/brian-p-delisle-circadian-regulation-of-cardiac-electrophysiology/
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/2024/02/Brian-Delisle-e1722176786315.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240329T140000
DTEND;TZID=Asia/Seoul:20240329T160000
DTSTAMP:20260423T074959
CREATED:20240228T011339Z
LAST-MODIFIED:20240326T143210Z
UID:9279-1711720800-1711728000@www.ibs.re.kr
SUMMARY:Dongju Lim\, Anti-Windup Protection Circuits for Biomolecular Integral Controllers
DESCRIPTION:We will discuss about “Anti-Windup Protection Circuits for Biomolecular Integral Controllers”\, bioRxiv (2023). \n  \nAbstract \nRobust Perfect Adaptation (RPA) is a desired property of biological systems wherein a system’s output perfectly adapts to a steady state\, irrespective of a broad class of perturbations. Achieving RPA typically requires the deployment of integral controllers\, which continually adjust the system’s output based on the cumulative error over time. However\, the action of these integral controllers can lead to a phenomenon known as “windup”. Windup occurs when an actuator in the system is unable to respond to the controller’s commands\, often due to physical constraints\, causing the integral error to accumulate significantly. In biomolecular control systems\, this phenomenon is especially pronounced due to the positivity of molecular concentrations\, inevitable promoter saturation and resource limitations. To protect against such performance deterioration or even instability\, we present three biomolecular anti-windup topologies. The underlying architectures of these topologies are then linked to classical control-theoretic anti-windup strategies. This link is made possible due the development of a general model reduction result for chemical reaction networks with fast sequestration reactions that is valid in both the deterministic and stochastic settings. The topologies are realized as chemical reaction networks for which genetic designs\, harnessing the flexibility of inteins\, are proposed. To validate the efficacy of our designs in mitigating windup effects\, we perform simulations across a range of biological systems\, including a complex model of Type I diabetic patients and advanced biomolecular proportional-integral-derivative (PID) controllers. This work lays a foundation for developing robust and reliable biomolecular control systems\, providing necessary safety and protection against windup-induced instability.
URL:https://www.ibs.re.kr/bimag/event/dongju-lim-solving-the-time-dependent-protein-distributions-for-autoregulated-bursty-gene-expression-using-spectral-decomposition/
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:20240322T140000
DTEND;TZID=Asia/Seoul:20240322T160000
DTSTAMP:20260423T074959
CREATED:20240228T010806Z
LAST-MODIFIED:20240326T143602Z
UID:9277-1711116000-1711123200@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Transcriptome-wide analysis of cell cycle-dependent bursty gene expression from single-cell RNA-seq data using mechanistic model-based inference
DESCRIPTION:We will discuss about “Transcriptome-wide analysis of cell cycle-dependent bursty gene expression from single-cell RNA-seq data using mechanistic model-based inference”\, bioRxiv (2024) \nAbstract \nBursty gene expression is quantified by two intuitive parameters: the burst frequency and the burst size. While these parameters are known to be cell-cycle dependent for some genes\, a transcriptome-wide picture remains missing. Here we address this question by fitting a suite of mechanistic models of gene expression to mRNA count data for thousands of mouse genes\, obtained by sequencing of single cells for which the cell-cycle position has been inferred using a deep-learning approach. This leads to the estimation of the burst frequency and size per allele in the G1 and G2/M cell-cycle phases\, hence providing insight into the global patterns of transcriptional regulation. In particular\, we identify an interesting balancing mechanism: on average\, upon DNA replication\, the burst frequency decreases by ≈ 50%\, while the burst size increases by the same amount. We also show that for accurate estimation of the ratio of burst parameters in the G1 and G2/M phases\, mechanistic models must explicitly account for gene copy number differences between cells but\, surprisingly\, additional corrections for extrinsic noise due to the coupling of transcription to cell age within the cell cycle or technical noise due to imperfect capture of RNA molecules in sequencing experiments are unnecessary. \n 
URL:https://www.ibs.re.kr/bimag/event/2024-03-22-jc/
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