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CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230303T140000
DTEND;TZID=Asia/Seoul:20230303T160000
DTSTAMP:20260424T062821
CREATED:20230127T063333Z
LAST-MODIFIED:20230130T080633Z
UID:7280-1677852000-1677859200@www.ibs.re.kr
SUMMARY:Seho Park\, Dynamical information enables inference of gene regulation at single-cell scale
DESCRIPTION:We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”\, Zhang\, Stephen Y.\, and Michael PH Stumpf.\, bioRxiv (2023): 2023-01. \nAbstract \n\nCellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes\, we propose locaTE\, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific\, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods\, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets\, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data\, locaTE provides a powerful\, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.
URL:https://www.ibs.re.kr/bimag/event/2023-03-03-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:20230303T110000
DTEND;TZID=Asia/Seoul:20230303T120000
DTSTAMP:20260424T062821
CREATED:20230213T110430Z
LAST-MODIFIED:20230227T013418Z
UID:7331-1677841200-1677844800@www.ibs.re.kr
SUMMARY:Shinya Kuroda\, Systems Biology of Insulin Action
DESCRIPTION:Abstract: \n1. The “temporal information code” of insulin action: a bottom-up approach One of the essential elements of signaling networks is to encode information from a wide variety of inputs into a limited set of molecules. We have proposed a “temporal information code” that regulates a variety of physiological functions by encoding input information in temporal patterns of molecular activity\, and based on this concept\, we are analyzing biological homeostasis by insulin signaling. Taking blood insulin as an example\, we will explain how the temporal information of blood insulin is selectively decoded by downstream networks. \n2. Transomics of insulin action: a top-down approach In order to obtain a complete picture of insulin action\, we performed transomics measurements integrating metabolomics and transcriptomics\, and found that metabolism is regulated by allosteric regulation in the liver of normal mice and by compensatory gene expression in the liver of obese mice. (Top-down approach). I will talk about approach the principle of homeostasis of living organisms by temporal patterns\, using the analysis of systems biology of insulin action using two different approaches.
URL:https://www.ibs.re.kr/bimag/event/systems-biology-of-insulin-action/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230217T140000
DTEND;TZID=Asia/Seoul:20230217T160000
DTSTAMP:20260424T062821
CREATED:20230127T011541Z
LAST-MODIFIED:20230214T144610Z
UID:7278-1676642400-1676649600@www.ibs.re.kr
SUMMARY:Hyeontae Jo\, Characterizing possible failure modes in physics-informed neural networks
DESCRIPTION:We will discuss about “Characterizing possible failure modes in physics-informed neural networks”\, Krishnapriyan\, Aditi\, et al.\, Advances in Neural Information Processing Systems 34 (2021): 26548-26560. \nAbstract \n\n\n\n\n\n\nRecent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss function and use existing machine learning methodologies to train the model. We demonstrate that\, while existing PINN methodologies can learn good models for relatively trivial problems\, they can easily fail to learn relevant physical phenomena for even slightly more complex problems. In particular\, we analyze several distinct situations of widespread physical interest\, including learning differential equations with convection\, reaction\, and diffusion operators. We provide evidence that the soft regularization in PINNs\, which involves PDE-based differential operators\, can introduce a number of subtle problems\, including making the problem more ill-conditioned. Importantly\, we show that these possible failure modes are not due to the lack of expressivity in the NN architecture\, but that the PINN’s setup makes the loss landscape very hard to optimize. We then describe two promising solutions to address these failure modes. The first approach is to use curriculum regularization\, where the PINN’s loss term starts from a simple PDE regularization\, and becomes progressively more complex as the NN gets trained. The second approach is to pose the problem as a sequence-to-sequence learning task\, rather than learning to predict the entire space-time at once. Extensive testing shows that we can achieve up to 1-2 orders of magnitude lower error with these methods as compared to regular PINN training. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-02-17-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:20230210T140000
DTEND;TZID=Asia/Seoul:20230210T160000
DTSTAMP:20260424T062821
CREATED:20230126T235218Z
LAST-MODIFIED:20230208T015345Z
UID:7276-1676037600-1676044800@www.ibs.re.kr
SUMMARY:Dongju Lim\, Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors
DESCRIPTION:We will discuss about “Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors”\, Magal\, Noa\, et al.\, Chronic Stress 6 (2022): 24705470221100987. \nAbstract \n\n\n\n\nBackground: Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior\, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources. \nMethods: For that\, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data\, alongside demographic features\, were used to predict high versus low chronic stress with support vector machine classifiers\, applying out-of-sample model testing. \nResults: The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate\, heart-rate circadian characteristics)\, lifestyle (steps count\, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification. \nConclusion: As wearable technologies continue to rapidly evolve\, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior.
URL:https://www.ibs.re.kr/bimag/event/2023-02-10-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:20230203T140000
DTEND;TZID=Asia/Seoul:20230203T160000
DTSTAMP:20260424T062821
CREATED:20230126T234906Z
LAST-MODIFIED:20230130T080459Z
UID:7274-1675432800-1675440000@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Estimating and Assessing Differential Equation Models with Time-Course Data
DESCRIPTION:We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”\, Wong\, Samuel WK\, Shihao Yang\, and S. C. Kou\, arXiv preprint arXiv:2212.10653 (2022). \nAbstract \n\nOrdinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations\, time-course data are often noisy and some components of the system may not be observed. Furthermore\, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges\, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First\, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories\, including unobserved components\, with appropriate uncertainty quantification. Second\, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI’s efficient computation of model predictions. Overall\, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models\, which bypasses the need for any numerical integration.
URL:https://www.ibs.re.kr/bimag/event/2023-02-03-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:20230127T110000
DTEND;TZID=Asia/Seoul:20230127T130000
DTSTAMP:20260424T062821
CREATED:20221227T081814Z
LAST-MODIFIED:20230126T010049Z
UID:7180-1674817200-1674824400@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Optimal information networks: Application for data-driven integrated health in populations
DESCRIPTION:We will discuss about “Optimal information networks: Application for data-driven integrated health in populations”\, Servadio\, Joseph L.\, and Matteo Convertino\, Science Advances 4.2 (2018): e1701088. \nAbstract \n\n\n\nDevelopment of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free\, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables\, instead considering only individual correlations. In addition\, a unified method for assessing integrated health statuses of populations is lacking\, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets\, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator\, representing integrated health in a city.
URL:https://www.ibs.re.kr/bimag/event/2023-01-27-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:20230120T110000
DTEND;TZID=Asia/Seoul:20230120T130000
DTSTAMP:20260424T062821
CREATED:20221228T011141Z
LAST-MODIFIED:20221228T011141Z
UID:7186-1674212400-1674219600@www.ibs.re.kr
SUMMARY:Yun Min Song\, A scalable approach for solving chemical master equations based on modularization and filtering
DESCRIPTION:We will discuss about “A scalable approach for solving chemical master equations based on modularization and filtering\n”\, Fang\, Zhou\, Ankit Gupta\, and Mustafa Khammash.\, bioRxiv (2022). \nAbstract \n\nSolving the chemical master equation (CME) that characterizes the probability evolution of stochastically reacting processes is greatly important for analyzing intracellular reaction systems. Conventional methods for solving CMEs include the simulation-based Monte-Carlo methods\, the direct approach (e.g.\, the finite state projection)\, and so on; however\, they usually do not scale very well with the system dimension either in terms of accuracy or efficiency. To mitigate this problem\, we propose a new computational method based on modularization and filtering. Our method first divides the whole system into a leader system and several conditionally independent follower subsystems. Then\, we solve the CME by applying the Monte Carlo method to the leader system and the direct approach to the filtered CMEs that characterize the conditional probabilities of the follower subsystems. The system decomposition involved in our method is optimized so that all the subproblems above are low dimensional\, and\, therefore\, our approach scales more favorably with the system dimension. Finally\, we demonstrate the efficiency and accuracy of our approach in high-dimensional estimation and inference problems using several biologically relevant examples.
URL:https://www.ibs.re.kr/bimag/event/2023-01-20-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:20230119T100000
DTEND;TZID=Asia/Seoul:20230119T110000
DTSTAMP:20260424T062821
CREATED:20230109T090635Z
LAST-MODIFIED:20230109T090635Z
UID:7229-1674122400-1674126000@www.ibs.re.kr
SUMMARY:Jong-Eun Park\, Single-cell analysis reveals recurring programs in cancer microenvironment
DESCRIPTION:Complexity of the cellular organization of the tumor microenvironment as an ecosystem remains to be fully appreciated. Here\, for a comprehensive investigation of tumor ecosystems across a wide variety of cancer types\, we performed integrative transcriptome analyses of 4.4 million single cells from 978 tumor and 474 normal samples in combination with 9\,510 TCGA and 1\,339 checkpoint inhibitor-treated bulk tumors. Our analysis enabled us to define 28 different epithelial cell states\, some of which had prognostic effects in cancers of relevant origin. Malignant fibroblast signatures defined according to the organ of origin demonstrated prognostic significance across diverse cancer types and revealed FN1\, BGN\, THBS2\, and CTHRC1 as common cancer-associated fibroblast genes. Novel associations were revealed between the AKR1C1+ inflammatory fibroblast and myeloid-derived PRR-induced activation states and between the CXCL10+ fibroblast and squamous/LAMP3+ DC/SPP1+ macrophage states. We discovered tumor-specific rewiring of the tertiary lymphoid structure (TLS) network\, involving previously unappreciated DC1\, and pDC.. Along with other TLS component states\, the tumor-associated germinal center B cell state identified from adjacent normal tissues was able to predict responses to checkpoint immunotherapy. Distinct groups of pan-cancer ecosystems were identified and characterized along the axis of immunotherapy responses. Our systematic\, high-resolution dissection of tumor ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.
URL:https://www.ibs.re.kr/bimag/event/jong-eun-park-single-cell-analysis-reveals-recurring-programs-in-cancer-microenvironment/
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:20230112T130000
DTEND;TZID=Asia/Seoul:20230112T150000
DTSTAMP:20260424T062821
CREATED:20221228T005748Z
LAST-MODIFIED:20230108T080223Z
UID:7183-1673528400-1673535600@www.ibs.re.kr
SUMMARY:Hyun Kim\, Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics
DESCRIPTION:We will discuss about “Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics”\n\, Lin\, Baihan.\, arXiv preprint arXiv:2204.14048 (2022). \nAbstract \n\nThe absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate\, differentiate\, and compete\, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq)\, we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs\, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks\, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38\,731 cells\, 25 cell types and 12 time steps\, our approach highlights the gastrulation as the most critical stage\, consistent with consensus in developmental biology. As a nonlinear\, model-independent\, and unsupervised framework\, our approach can also be applied to tracing multi-scale cell lineage\, identifying critical stages\, or creating pseudo-time series.
URL:https://www.ibs.re.kr/bimag/event/2023-01-13-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:20230106T150000
DTEND;TZID=Asia/Seoul:20230106T170000
DTSTAMP:20260424T062821
CREATED:20221227T081429Z
LAST-MODIFIED:20230102T121025Z
UID:7178-1673017200-1673024400@www.ibs.re.kr
SUMMARY:Aurelio A. de los Reyes V\, Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
DESCRIPTION:We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”\, Linka\, Kevin\, et al.\, Computer Methods in Applied Mechanics and Engineering Volume 402\, 1 December 2022\, 115346 \nAbstract \n\n\n\n\nUnderstanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines\, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems\, identify patterns in big data\, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However\, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data\, physics\, and uncertainties by combining neural networks\, physics informed modeling\, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system\, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics\, robustly solve forward and inverse problems\, and perform well for both interpolation and extrapolation\, even for a small amount of noisy and incomplete data. At only minor additional cost\, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference\, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks\, Bayesian Inference\, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease\, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and\, more broadly\, to a wide variety of nonlinear dynamical systems. Our source code and examples are available at https://github.com/LivingMatterLab/xPINNs.
URL:https://www.ibs.re.kr/bimag/event/2023-01-06-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:20221230T150000
DTEND;TZID=Asia/Seoul:20221230T170000
DTSTAMP:20260424T062821
CREATED:20221222T082525Z
LAST-MODIFIED:20221230T060020Z
UID:7080-1672412400-1672419600@www.ibs.re.kr
SUMMARY:Candan Celik\, Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms
DESCRIPTION:We will discuss about “Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms”\,Jia\, Chen\, and Youming Li\, BioRxiv (2022). \nAbstract \n\n\n\nClassical gene expression models assume exponential switching time distributions between the active and inactive promoter states. However\, recent experiments have shown that many genes in mammalian cells may produce non-exponential switching time distributions\, implying the existence of multiple promoter states and molecular memory in the promoter switching dynamics. Here we analytically solve a gene expression model with random bursting and complex promoter switching\, and derive the time-dependent distributions of the mRNA and protein copy numbers\, generalizing the steady-state solution obtained in [SIAM J. Appl. Math. 72\, 789-818 (2012)] and [SIAM J. Appl. Math. 79\, 1007-1029 (2019)]. Using multiscale simplification techniques\, we find that molecular memory has no influence on the time-dependent distribution when promoter switching is very fast or very slow\, while it significantly affects the distribution when promoter switching is neither too fast nor too slow. By analyzing the dynamical phase diagram of the system\, we also find that molecular memory in the inactive gene state weakens transient and stationary bimodality of the copy number distribution\, while molecular memory in the active gene state enhances such bimodality.
URL:https://www.ibs.re.kr/bimag/event/2022-12-30-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:20221228T140000
DTEND;TZID=Asia/Seoul:20221228T150000
DTSTAMP:20260424T062821
CREATED:20221215T221715Z
LAST-MODIFIED:20221222T082709Z
UID:7043-1672236000-1672239600@www.ibs.re.kr
SUMMARY:Ji Won Oh\, From Grave to Cradle: Human Somatic Mosaicism and Unsolved Questions
DESCRIPTION:사람이 어떻게 만들어지고 각 기관이 어떻게 발달하는지에 대한 질문은 아주 오래전부터 있었습니다. 체외수정(IVF)의 고유의 장점으로 인해 과학자들이 수정란을 외부에서 관찰할 수 있게 되었습니다. 하지만\, 1979년도에 제정된 14일 규정(the 14-day rule)으로 인해\, 수정 후 최대 14일까지의 배아 만의 연구가 가능합니다. 따라서\, 이 14일 규정은 발생 생물학자들이 사람 발생학 연구에 있어서 수정 후 2주 이상(신경계 발달\, 기관 형성 등)에 나타나는 현상을 연구하고자 할 경우 다른 방향을 모색할 수밖에 없게 되었습니다. 본 연구는 이 지점에서부터 시작합니다. 연구진들은 세포 분열 때 우연히 발생하는 생리학적 체세포 변이(Post-zygotic Variants)를 추적하여 각 세포들의 운명을 재구성하였습니다. 특히 사망 후 기증된 시신에서 단일 세포를 배양하고\, 최근 개발된 차세대 염기서열 분석 기술을 사용하여 인간 발생 연구의 후향적 혈통 추적(Retrospective Lineage Tracing)을 수행하는 과정을 발표하고자 합니다. 이번 발표를 통해서 이런 방법론이 어떻게 가능했는지에 대한 생물학적 및 과학적 배경과 인간 발생학의 미래에서 해결해야 할 과제와 가설을 강조할 예정입니다. 추가로\, 이 과정에서 필요한 수학적인 해석이 필요한 질문들에 대해서도 논의할 예정입니다. 여러분들의 참신한 시각과 질문을 크게 환영합니다. \n\n\n\n\n1) Park\, S.\, Mali\, N.M.\, Kim\, R. et al. Clonal dynamics in early human embryogenesis inferred from somatic mutation. Nature 597\, 393–397 (2021). https://doi.org/10.1038/s41586-021-03786-8 \n2) Kwon\, S.G.\, Bae\, G.H.\, Choi\, J.H. et al. Asymmetric Contribution of Blastomere Lineages of First Division of the Zygote to Entire Human Body Using Post-Zygotic Variants. Tissue Eng Regen Med 19\, 809–821 (2022). https://doi.org/10.1007/s13770-022-00443-7
URL:https://www.ibs.re.kr/bimag/event/from-grave-to-cradle-human-somatic-mosaicism-and-unsolved-questions/
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:20221226T100000
DTEND;TZID=Asia/Seoul:20221226T120000
DTSTAMP:20260424T062821
CREATED:20221226T004917Z
LAST-MODIFIED:20260404T011224Z
UID:7164-1672048800-1672056000@www.ibs.re.kr
SUMMARY:IBS BIMAG 2022 Winter Internship Workshop
DESCRIPTION:IBS BIMAG will host a kick-off workshop for the winter internships on Monday\, 26 December 2022. The internship participants from Pusan National University and Postech will give 8 minutes presentations on their research topics. \nPresentation List:\n\n김미지 (Miji Kim) – A Comparison Study of Dropout to Prevent Overfitting Problem in CNN Image Data Classification\n김지현 (Jihyeon Kim)- Study of Ensemble Kalman Filter\n이시은 (Sieun Lee) – Early Detection using Epidemic Data\n이유진 (Youjin Lee) – On Parameter Estimation Approaches for Biomathematical Models through Physics-Informed Neural Networks\n장근수 (Geunsoo Jang) – Development of mathematical model for impact evaluation of Radioactive Water Discharge in Fukushima\n김진영 (Jinyoung Kim) – Stochastic aggregation models in 2D and 3D spaces to describe Liquid-Liquid Phase Separation (LLPS)\n김민준 (Minjoon Kim) –  Stability of Chemical reaction networks
URL:https://www.ibs.re.kr/bimag/event/ibs-bimag-winter-internship-workshop/
LOCATION:IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Lunch Lab Meeting Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221223T150000
DTEND;TZID=Asia/Seoul:20221223T170000
DTSTAMP:20260424T062821
CREATED:20221222T082248Z
LAST-MODIFIED:20221222T082248Z
UID:7075-1671807600-1671814800@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Optimal control of aging in complex networks
DESCRIPTION:We will discuss about “Optimal control of aging in complex networks”\,\nSun\, Eric D.\, Thomas CT Michaels\, and L. Mahadevan\, Proceedings of the National Academy of Sciences 117.34 (2020): 20404-20410. \nAbstract \n\n\n\nMany complex systems experience damage accumulation\, which leads to aging\, manifest as an increasing probability of system collapse with time. This naturally raises the question of how to maximize health and longevity in an aging system at minimal cost of maintenance and intervention. Here\, we pose this question in the context of a simple interdependent network model of aging in complex systems and show that it exhibits cascading failures. We then use both optimal control theory and reinforcement learning alongside a combination of analysis and simulation to determine optimal maintenance protocols. These protocols may motivate the rational design of strategies for promoting longevity in aging complex systems with potential applications in therapeutic schedules and engineered system maintenance.
URL:https://www.ibs.re.kr/bimag/event/2022-12-23-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:20221216T130000
DTEND;TZID=Asia/Seoul:20221216T150000
DTSTAMP:20260424T062821
CREATED:20221214T122407Z
LAST-MODIFIED:20221214T122407Z
UID:7022-1671195600-1671202800@www.ibs.re.kr
SUMMARY:Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators
DESCRIPTION:We will discuss about “Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators”\, Karapetyan\, Sargis\, and Nicolas E. Buchler\,Physical Review E 92.6 (2015): 062712. \nAbstract \n\n\n\nGenetic oscillators\, such as circadian clocks\, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study\, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA\, despite traditionally being considered a fast parameter\, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple\, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics\, which are often omitted in biophysical models of gene circuits\, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.
URL:https://www.ibs.re.kr/bimag/event/2022-12-16-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:20221213T160000
DTEND;TZID=Asia/Seoul:20221213T170000
DTSTAMP:20260424T062821
CREATED:20221209T045119Z
LAST-MODIFIED:20221211T121541Z
UID:6984-1670947200-1670950800@www.ibs.re.kr
SUMMARY:Static and Dynamic Absolute Concentration Robustness
DESCRIPTION:Absolute Concentration Robustness (ACR) was introduced by Shinar and Feinberg (Science 327:1389-1391\, 2010) as robustness of equilibrium species concentration in a mass action dynamical system. Their aim was to devise a mathematical condition that will ensure robustness in the function of the biological system being modeled. The robustness of function rests on what we refer to as empirical robustness — the concentration of a species remains unvarying\, when measured in the long run\, across arbitrary initial conditions. Even simple examples show that the ACR notion introduced in Shinar and Feinberg (here referred to as static ACR) is neither necessary nor sufficient for empirical robustness. To make a stronger connection with empirical robustness\, we define dynamic ACR\, a property related to long-term\, global dynamics\, rather than only to equilibrium behavior. We discuss general dynamical systems with dynamic ACR properties as well as parametrized families of dynamical systems related to reaction networks. In particular\, we find necessary and sufficient conditions for dynamic ACR in complex balanced reaction networks\, a class of networks that is central to the theory of reaction networks.This is joint work with Badal Joshi (CSUSM)
URL:https://www.ibs.re.kr/bimag/event/static-and-dynamic-absolute-concentration-robustness/
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:20221209T110000
DTEND;TZID=Asia/Seoul:20221209T120000
DTSTAMP:20260424T062821
CREATED:20220825T013528Z
LAST-MODIFIED:20221207T064542Z
UID:6504-1670583600-1670587200@www.ibs.re.kr
SUMMARY:Taming Complexity in Data-Limited Nonlinear Nonequilibrium Settings
DESCRIPTION:Abstract: \nSince before the time of Aristotle and the natural philosophers\, reductionism has played a foundational role in western scientific thought. The premise of reductionism is that systems can be broken down into constituent pieces and studied independently\, then reassembled to understand the behavior of the system as a whole. It embodies the classical linear perspective. This approach has been successful in developing basic physical laws and especially in engineering where linear analysis dominates and systems are purposefully designed that way. However\, reductionism is not universally applicable for natural complex systems where behavior is driven\, not by a few factors acting independently\, but by complex interactions between many components acting together and changing in time. \nNonlinearity in living systems means that its parts are interdependent – variables do not act in a mutually independent manner; rather they interact\, and as a consequence associations (correlations) between them will change as the overall system context (state) changes.  This problem is highlighted when extrapolating the results of single-factor experiments to nature\, and surely contributes to the frustrating disconnect between experimental findings and clinical outcomes in drug trials. Indeed\, while everyone knows Berkeley’s 1710 dictum “correlation does not imply causation” few realize that for nonlinear systems the converse “causation does not imply correlation” is also true. This conundrum runs counter to deeply ingrained heuristic thinking that is at the basis of modern science. Biological systems (esp. ecosystems) are particularly perverse on this issue by exhibiting mirage correlations that can continually cause us to rethink relationships we thought we understood. \nHere we examine a minimalist paradigm\, empirical dynamics (EDM)\, for studying non-linear systems and a method (CCM) that can detect causality when there is no correlation among variables. It is a data-driven approach that uses time series to study a system holistically by reconstructing its attractor – a geometric object that embodies the rules of a full set of equations for the system.  The ideas are intuitive and will be illustrated with examples from genetics\, ecology and epidemiology. \nA python version of EDM tools can be found at https://pepy.tech/project/pyEDM
URL:https://www.ibs.re.kr/bimag/event/2022-12-09-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/Sugihara_George_250x250.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221202T150000
DTEND;TZID=Asia/Seoul:20221202T170000
DTSTAMP:20260424T062821
CREATED:20221128T010402Z
LAST-MODIFIED:20221128T010402Z
UID:6906-1669993200-1670000400@www.ibs.re.kr
SUMMARY:Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors
DESCRIPTION:We will discuss about “Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors”\, Vipond\, Oliver\, et al\, Proceedings of the National Academy of Sciences 118.41 (2021): e2102166118. \nAbstract\nHighly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data—often with outliers\, artifacts\, and mislabeled points—such as those from tissues\, remains a challenge. The mathematical field that extracts information from the shape of data\, topological data analysis (TDA)\, has expanded its capability for analyzing real-world datasets in recent years by extending theory\, statistics\, and computation. An extension to the standard theory to handle heterogeneous data is multiparameter persistent homology (MPH). Here we provide an application of MPH landscapes\, a statistical tool with theoretical underpinnings. MPH landscapes\, computed for (noisy) data from agent-basedMultiparameter persistent homology landscapes identify immune cell spatial patterns in tumors model simulations of immune cells infiltrating into a spheroid\, are shown to surpass existing spatial statistics and one-parameter persistent homology. We then apply MPH landscapes to study immune cell location in digital histology images from head and neck cancer. We quantify intratumoral immune cells and find that infiltrating regulatory T cells have more prominent voids in their spatial patterns than macrophages. Finally\, we consider how TDA can integrate and interrogate data of different types and scales\, e.g.\, immune cell locations and regions with differing levels of oxygenation. This work highlights the power of MPH landscapes for quantifying\, characterizing\, and comparing features within the tumor microenvironment in synthetic and real datasets.
URL:https://www.ibs.re.kr/bimag/event/2022-12-02-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:20221202T110000
DTEND;TZID=Asia/Seoul:20221202T120000
DTSTAMP:20260424T062821
CREATED:20220825T011607Z
LAST-MODIFIED:20220828T060439Z
UID:6474-1669978800-1669982400@www.ibs.re.kr
SUMMARY:Mammalian synthetic biology by controller design
DESCRIPTION:Abstract: The ability to reliably engineer the mammalian cell will impact a variety of applications in a disruptive way\, including cell fate control and reprogramming\, targeted drug delivery\, and regenerative medicine. However\, our current ability to engineer mammalian genetic circuits that behave as predicted remains limited. These circuits depend on the intra and extra cellular environment in ways that are difficult to anticipate\, and this fact often hampers genetic circuit performance. This lack of robustness to poorly known and often variable cellular environment is the subject of this talk. Specifically\, I will describe control engineering approaches that make the performance of genetic devices robust to context. I will show a feedforward controller that makes gene expression robust to variability in cellular resources and\, more generally\, to changes in intra-cellular context linked to differences in cell type. I will then show a feedback controller that uses bacterial two component signaling systems to create a quasi-integral controller that makes the input/output response of a genetic device robust to a variety of perturbations that affect gene expression. These solutions support rational and modular design of sophisticated genetic circuits and can serve for engineering biological circuits that are more robust and predictable across changing contexts.
URL:https://www.ibs.re.kr/bimag/event/2022-12-02-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/Domitilla-Del-Vecchio-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:20260424T062821
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221123T160000
DTEND;TZID=Asia/Seoul:20221123T170000
DTSTAMP:20260424T062821
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:20221118T150000
DTEND;TZID=Asia/Seoul:20221118T170000
DTSTAMP:20260424T062821
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:20221118T110000
DTEND;TZID=Asia/Seoul:20221118T120000
DTSTAMP:20260424T062821
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:20221111T150000
DTEND;TZID=Asia/Seoul:20221111T170000
DTSTAMP:20260424T062821
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:20221109T160000
DTEND;TZID=Asia/Seoul:20221109T170000
DTSTAMP:20260424T062821
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:20221109T140000
DTEND;TZID=Asia/Seoul:20221109T150000
DTSTAMP:20260424T062821
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:20260424T062821
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:20221104T150000
DTEND;TZID=Asia/Seoul:20221104T170000
DTSTAMP:20260424T062821
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:20221028T140000
DTEND;TZID=Asia/Seoul:20221028T160000
DTSTAMP:20260424T062821
CREATED:20220930T035148Z
LAST-MODIFIED:20221027T083230Z
UID:6646-1666965600-1666972800@www.ibs.re.kr
SUMMARY:Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19
DESCRIPTION:We will discuss about “Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19”\, Cheng\, Jinyu\, et al.\, Briefings in bioinformatics 22.2 (2021): 988-1005. \nAbstract: \nInferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study\, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF\, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression\, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition\, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.
URL:https://www.ibs.re.kr/bimag/event/2022-10-28-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:20221026T160000
DTEND;TZID=Asia/Seoul:20221026T170000
DTSTAMP:20260424T062821
CREATED:20220825T012029Z
LAST-MODIFIED:20220925T142427Z
UID:6482-1666800000-1666803600@www.ibs.re.kr
SUMMARY:Mathematical modelling of the sleep-wake cycle: light\, clocks and social rhythms
DESCRIPTION:Abstract: \nWe’re all familiar with sleep\, but how can we mathematically model it? And what determines how long and when we sleep? In this talk I’ll introduce the nonsmooth coupled oscillator systems that form the basis of current models of sleep-wake regulation and discuss their dynamical behaviour. I will describe how we are using models to unravel environmental\, societal and physiological factors that determine sleep timing and outline how we are using models to inform the quantitative design of light interventions for mental health disorders and address contentious societal questions such as whether to move school start time for adolescents.
URL:https://www.ibs.re.kr/bimag/event/2022-10-26-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/anne-skeldon.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR