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PRODID:-//Biomedical Mathematics Group - ECPv6.16.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240816T140000
DTEND;TZID=Asia/Seoul:20240816T160000
DTSTAMP:20240730T001615Z
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/
LOCATION:Daejeon
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:20240730T001308Z
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20240729T001043Z
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:20240726T140000
DTEND;TZID=Asia/Seoul:20240726T160000
DTSTAMP:20240709T021120Z
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/
LOCATION:Daejeon
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:20240715T001749Z
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/
LOCATION:Daejeon
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:20240709T021017Z
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20240620T065839Z
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/
LOCATION:Daejeon
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:20240614T002219Z
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/
LOCATION:Daejeon
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:20240606T054542Z
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/
LOCATION:Daejeon
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:20240528T001427Z
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/
LOCATION:Daejeon
CATEGORIES:Journal Club
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:20240428T181352Z
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/
LOCATION:Daejeon
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:20240513T082339Z
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:20240426T140000
DTEND;TZID=Asia/Seoul:20240426T160000
DTSTAMP:20240423T002345Z
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:20240415T082050Z
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:20240329T140000
DTEND;TZID=Asia/Seoul:20240329T160000
DTSTAMP:20240326T143210Z
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:20240326T143602Z
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240312T163000
DTEND;TZID=Asia/Seoul:20240312T183000
DTSTAMP:20240307T011616Z
CREATED:20240228T005750Z
LAST-MODIFIED:20240307T011616Z
UID:9273-1710261000-1710268200@www.ibs.re.kr
SUMMARY:Brenda Lyn Gavina\, Reduced model for female endocrine dynamics: Validation and functional variations
DESCRIPTION:We will discuss about “Reduced model for female endocrine dynamics: Validation and functional variations.” Mathematical Biosciences 358 (2023): 108979. \nAbstract \n\n\n\n\nA normally functioning menstrual cycle requires significant crosstalk between hormones originating in ovarian and brain tissues. Reproductive hormone dysregulation may cause abnormal function and sometimes infertility. The inherent complexity in this endocrine system is a challenge to identifying mechanisms of cycle disruption\, particularly given the large number of unknown parameters in existing mathematical models. We develop a new endocrine model to limit model complexity and use simulated distributions of unknown parameters for model analysis. By employing a comprehensive model evaluation\, we identify a collection of mechanisms that differentiate normal and abnormal phenotypes. We also discover an intermediate phenotype—displaying relatively normal hormone levels and cycle dynamics—that is grouped statistically with the irregular phenotype. Results provide insight into how clinical symptoms associated with ovulatory disruption may not be detected through hormone measurements alone. \n 
URL:https://www.ibs.re.kr/bimag/event/2024-03-13-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:20240223T140000
DTEND;TZID=Asia/Seoul:20240223T170000
DTSTAMP:20240222T233219Z
CREATED:20240127T065045Z
LAST-MODIFIED:20240222T233219Z
UID:9153-1708696800-1708707600@www.ibs.re.kr
SUMMARY:Hyun Kim\, A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples
DESCRIPTION:We will discuss about “A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples\n”\, Nature communications 14.1 (2023): 7286. \n  \nAbstract \n\n\n\nPseudotime analysis with single-cell RNA-sequencing (scRNA-seq) data has been widely used to study dynamic gene regulatory programs along continuous biological processes. While many methods have been developed to infer the pseudotemporal trajectories of cells within a biological sample\, it remains a challenge to compare pseudotemporal patterns with multiple samples (or replicates) across different experimental conditions. Here\, we introduce Lamian\, a comprehensive and statistically-rigorous computational framework for differential multi-sample pseudotime analysis. Lamian can be used to identify changes in a biological process associated with sample covariates\, such as different biological conditions while adjusting for batch effects\, and to detect changes in gene expression\, cell density\, and topology of a pseudotemporal trajectory. Unlike existing methods that ignore sample variability\, Lamian draws statistical inference after accounting for cross-sample variability and hence substantially reduces sample-specific false discoveries that are not generalizable to new samples. Using both real scRNA-seq and simulation data\, including an analysis of differential immune response programs between COVID-19 patients with different disease severity levels\, we demonstrate the advantages of Lamian in decoding cellular gene expression programs in continuous biological processes. \n\n\n\n\n 
URL:https://www.ibs.re.kr/bimag/event/2024-02-23-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:20240216T140000
DTEND;TZID=Asia/Seoul:20240216T170000
DTSTAMP:20240215T084643Z
CREATED:20240127T064902Z
LAST-MODIFIED:20240215T084643Z
UID:9150-1708092000-1708102800@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Anticipating the occurrence and type of critical transitions
DESCRIPTION:We will discuss about “Anticipating the occurrence and type of critical transitions”\, Science Advances 9.1 (2023): eabq4558. \n  \nAbstract \nCritical transition can occur in many real-world systems. The ability to forecast the occurrence of transition is of major interest in a range of contexts. Various early warning signals (EWSs) have been developed to anticipate the coming critical transition or distinguish types of transition. However\, no effective method allows to establish practical threshold indicating the condition when the critical transition is most likely to occur. Here\, we introduce a powerful EWS\, named dynamical eigenvalue (DEV)\, that is rooted in bifurcation theory of dynamical systems to estimate the dominant eigenvalue of the system. Theoretically\, the absolute value of DEV approaches 1 when the system approaches bifurcation\, while its position in the complex plane indicates the type of transition. We demonstrate the efficacy of the DEV approach in model systems with known bifurcation types and also test the DEV approach on various critical transitions in real-world systems. \n 
URL:https://www.ibs.re.kr/bimag/event/2024-02-16-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:20240202T140000
DTEND;TZID=Asia/Seoul:20240202T170000
DTSTAMP:20240128T132151Z
CREATED:20240127T064735Z
LAST-MODIFIED:20240128T132151Z
UID:9148-1706882400-1706893200@www.ibs.re.kr
SUMMARY:Yun Min Song\, A trade-off in controlling upstream and downstream noise in signaling networks
DESCRIPTION:We will discuss about “A trade-off in controlling upstream and downstream noise in signaling networks”\,  bioRxiv (2023): 2023-08. \n  \nAbstract\nSignal transduction\, underpinning the function of a variety of biological systems\, is inevitably affected by fluctuations. It remains intriguing how the timescale of a signaling network relates to its capability of noise control\, specifically\, whether long timescale can average out fluctuation or accumulate fluctuation. Here\, we consider two noise components of the signaling system: the upstream noise from the fluctuation of the input signal and the downstream noise from the stochastic fluctuations of the network. We discover a fundamental trade-off in controlling the upstream and downstream noise: a longer timescale of the signaling network can buffer upstream noise\, while accumulate downstream noise. Moreover\, we confirm that this trade-off relation exists in real biological signaling networks such as a fold-change detection circuit and the p53 activation signaling system.
URL:https://www.ibs.re.kr/bimag/event/2024-02-02-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:20240126T140000
DTEND;TZID=Asia/Seoul:20240126T160000
DTSTAMP:20240105T093349Z
CREATED:20231229T030126Z
LAST-MODIFIED:20240105T093349Z
UID:8991-1706277600-1706284800@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, "Linear mapping approximation of gene regulatory networks with stochastic dynamics"
DESCRIPTION:We will discuss about “Linear mapping approximation of gene regulatory networks with stochastic dynamics”\, Nature communications 9.1 (2018): 3305. \n  \nAbstract \nThe presence of protein–DNA binding reactions often leads to analytically intractable models of stochastic gene expression. Here we present the linear-mapping approximation that maps systems with protein–promoter interactions onto approximately equivalent systems with no binding reactions. This is achieved by the marriage of conditional mean-field approximation and the Magnus expansion\, leading to analytic or semi-analytic expressions for the approximate time-dependent and steady-state protein number distributions. Stochastic simulations verify the method’s accuracy in capturing the changes in the protein number distributions with time for a wide variety of networks displaying auto- and mutual-regulation of gene expression and independently of the ratios of the timescales governing the dynamics. The method is also used to study the first-passage time distribution of promoter switching\, the sensitivity of the size of protein number fluctuations to parameter perturbation and the stochastic bifurcation diagram characterizing the onset of multimodality in protein number distributions.
URL:https://www.ibs.re.kr/bimag/event/2024-01-26-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:20240119T140000
DTEND;TZID=Asia/Seoul:20240119T160000
DTSTAMP:20240105T093238Z
CREATED:20231229T025616Z
LAST-MODIFIED:20240105T093238Z
UID:8985-1705672800-1705680000@www.ibs.re.kr
SUMMARY:Dongju Lim\, The timing of cellular events: a stochastic vs deterministic perspective
DESCRIPTION:We will discuss about “The timing of cellular events: a stochastic vs deterministic perspective”\, bioRxiv (2023): 2023-07. \n  \nAbstract \nChanges in cell state are driven by key molecular events whose timing can often be measured experimentally. Of particular interest is the time taken for the levels of RNA or protein molecules to reach a critical threshold defining the triggering of a cellular event. While this mean trigger time can be estimated by numerical integration of deterministic models\, these ignore intrinsic noise and hence their predictions may be inaccurate. Here we study the differences between deterministic and stochastic model predictions for the mean trigger times using simple models of gene expression\, post-transcriptional feedback control\, and enzyme-mediated catalysis. By comparison of the two predictions\, we show that when promoter switching is present there exists a transition from a parameter regime where deterministic models predict a longer trigger time than stochastic models to a regime where the opposite occurs. Furthermore\, the ratio of the trigger times of the two models can be large\, particularly for auto-regulatory genetic feedback loops. Our theory provides intuitive insight into the origin of these effects and shows that deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells. \n 
URL:https://www.ibs.re.kr/bimag/event/2024-01-19-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:20240112T140000
DTEND;TZID=Asia/Seoul:20240112T160000
DTSTAMP:20240106T124522Z
CREATED:20231229T025818Z
LAST-MODIFIED:20240106T124522Z
UID:8988-1705068000-1705075200@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, AI Feynman: A physics-inspired method for symbolic regression
DESCRIPTION:We will discuss about “AI Feynman: A physics-inspired method for symbolic regression”\,Science Advances 6.16 (2020): eaay2631. \nAbstract \nA core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle\, functions of practical interest often exhibit symmetries\, separability\, compositionality\, and other simplifying properties. In this spirit\, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics\, and it discovers all of them\, while previous publicly available software cracks only 71; for a more difficult physics-based test set\, we improve the state-of-the-art success rate from 15 to 90%.
URL:https://www.ibs.re.kr/bimag/event/2024-01-12-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:20240105T140000
DTEND;TZID=Asia/Seoul:20240105T160000
DTSTAMP:20231215T004743Z
CREATED:20231130T084919Z
LAST-MODIFIED:20231215T004743Z
UID:8754-1704463200-1704470400@www.ibs.re.kr
SUMMARY:Hyeontae Jo\, Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
DESCRIPTION:We will discuss about “Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery” IEEE Transactions on neural networks and learning systems 32.9 (2020): 4166-4177. \nAbstract \n\nSymbolic regression is a powerful technique to discover analytic equations that describe data\, which can lead to explainable models and the ability to predict unseen data. In contrast\, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks\, but they are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. In this article\, we use a neural network-based architecture for symbolic regression called the equation learner (EQL) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation. To demonstrate the power of such systems\, we study their performance on several substantially different tasks. First\, we show that the neural network can perform symbolic regression and learn the form of several functions. Next\, we present an MNIST arithmetic task where a convolutional network extracts the digits. Finally\, we demonstrate the prediction of dynamical systems where an unknown parameter is extracted through an encoder. We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared with a standard neural network-based architecture\, paving the way for deep learning to be applied in scientific exploration and discovery
URL:https://www.ibs.re.kr/bimag/event/2024-01-05-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:20231229T140000
DTEND;TZID=Asia/Seoul:20231229T160000
DTSTAMP:20231228T025820Z
CREATED:20231130T085100Z
LAST-MODIFIED:20231228T025820Z
UID:8756-1703858400-1703865600@www.ibs.re.kr
SUMMARY:Hyun Kim\, MultiVI: deep generative model for the integration of multimodal data
DESCRIPTION:We will discuss about “MultiVI: deep generative model for the integration of multimodal data” Nature Methods 20.8 (2023): 1222-1231. \nAbstract \n\n\n\nJointly profiling the transcriptome\, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI\, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data\, even for cells for which one or more modalities are missing. It is available at scvi-tools.org.
URL:https://www.ibs.re.kr/bimag/event/2023-12-29-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:20231215T140000
DTEND;TZID=Asia/Seoul:20231215T160000
DTSTAMP:20231214T000605Z
CREATED:20231130T085305Z
LAST-MODIFIED:20231214T000605Z
UID:8758-1702648800-1702656000@www.ibs.re.kr
SUMMARY:Yun Min Song\, Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination
DESCRIPTION:We will discuss about “Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination” bioRxiv (2023): 2023-10. \nAbstract \n\n\nEntrainment to an external stimulus enables a synchronized oscillatory response across a population of cells\, increasing coherent responses by reducing cell-to-cell heterogeneity. It is unclear whether the property of entrainability extends to systems where responses are intrinsic to the individual cell\, rather than dependent on coherence across a population of cells. Using a combination of mathematical modeling\, time-lapse fluorescence microscopy\, and single-cell tracking\, we demonstrated that p53 oscillations triggered by DNA double-strand breaks (DSBs) can be entrained with a periodic damage stimulus\, despite such synchrony not known to function in effective DNA damage responses. Surprisingly\, p53 oscillations were experimentally entrained over a wider range of DSB frequencies than predicted by an established computational model for the system. We determined that recapitulating the increased range of entrainment frequencies required\, non-intuitively\, a less robust oscillator and wider steady-state valley on the energy landscape. Further\, we show that p53 entrainment can lead to altered expression dynamics of downstream targets responsible for cell fate in a manner dependent on target mRNA stability. Overall\, this study demonstrates that entrainment can occur in a biological oscillator despite the apparent lack of an evolutionary advantage conferred through synchronized responses and highlights the potential of externally entraining p53 dynamics to reduce cellular variability and synchronize cell-fate responses for therapeutic outcomes.
URL:https://www.ibs.re.kr/bimag/event/2023-12-15-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:20231208T153000
DTEND;TZID=Asia/Seoul:20231208T173000
DTSTAMP:20231207T014408Z
CREATED:20231130T084548Z
LAST-MODIFIED:20231207T014408Z
UID:8751-1702049400-1702056600@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Time delays modulate the stability of complex ecosystems
DESCRIPTION:We will discuss about “Time delays modulate the stability of complex ecosystems” Nature Ecology & Evolution 7.10 (2023): 1610-1619. \nAbstract \nWhat drives the stability\, or instability\, of complex ecosystems? This question sits at the heart of community ecology and has motivated a large body of theoretical work exploring how community properties shape ecosystem dynamics. However\, the overwhelming majority of current theory assumes that species interactions are instantaneous\, meaning that changes in the abundance of one species will lead to immediate changes in the abundances of its partners. In practice\, time delays in how species respond to one another are widespread across ecological contexts\, yet the impact of these delays on ecosystems remains unclear. Here we derive a new body of theory to comprehensively study the impact of time delays on ecological stability. We find that time delays are important for ecosystem stability. Large delays are typically destabilizing but\, surprisingly\, short delays can substantially increase community stability. Moreover\, in stark contrast to delay-free systems\, delays dictate that communities with more abundant species can be less stable than ones with less abundant species. Finally\, we show that delays fundamentally shift how species interactions impact ecosystem stability\, with communities of mixed interaction types becoming the most stable class of ecosystem. Our work demonstrates that time delays can be critical for the stability of complex ecosystems. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-12-08-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:20231201T140000
DTEND;TZID=Asia/Seoul:20231201T160000
DTSTAMP:20231114T081702Z
CREATED:20231030T040908Z
LAST-MODIFIED:20231114T081702Z
UID:8665-1701439200-1701446400@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Hard Limits and Performance Tradeoffs in a Class of Antithetic Integral Feedback Networks
DESCRIPTION:We will discuss about “Hard limits and performance tradeoffs in a class of antithetic integral feedback networks.” Cell systems 9.1 (2019): 49-63. \nAbstract \n\nFeedback regulation is pervasive in biology at both the organismal and cellular level. In this article\, we explore the properties of a particular biomolecular feedback mechanism called antithetic integral feedback\, which can be implemented using the binding of two molecules. Our work develops an analytic framework for understanding the hard limits\, performance tradeoffs\, and architectural properties of this simple model of biological feedback control. Using tools from control theory\, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed\, robustness\, steady-state error\, and leakiness. These findings yield a holistic understanding of the behavior of antithetic integral feedback and contribute to a more general theory of biological control systems.
URL:https://www.ibs.re.kr/bimag/event/2023-12-01-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:20231124T140000
DTEND;TZID=Asia/Seoul:20231124T160000
DTSTAMP:20231114T081738Z
CREATED:20231030T040641Z
LAST-MODIFIED:20231114T081738Z
UID:8663-1700834400-1700841600@www.ibs.re.kr
SUMMARY:Dongju Lim\, An accurate probabilistic step finder for time-series analysis
DESCRIPTION:We will discuss about “An accurate probabilistic step finder for time-series analysis.” bioRxiv (2023): 2023-09. \nAbstract \n\n\n\n\nNoisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments\, patch clamp and force spectroscopy setups\, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data include: hidden Markov models (HMMs) and step-finding algorithms. HMMs\, including their extensions to infinite state-spaces\, inherently assume in analysis that holding times in discrete states visited are geometrically–or\, loosely speaking in common language\, exponentially–distributed. Thus the determination of step locations\, especially in sparse and noisy data\, is biased by HMMs toward identifying steps resulting in geometric holding times. In contrast\, existing step-finding algorithms\, while free of this restraint\, often rely on ad hoc metrics to penalize steps recovered in time traces (by using various information criteria) and otherwise rely on approximate greedy algorithms to identify putative global optima. Here\, instead\, we devise a robust and general probabilistic (Bayesian) step-finding tool that neither relies on ad hoc metrics to penalize step numbers nor assumes geometric holding times in each state. As the number of steps themselves in a time-series are\, a priori unknown\, we treat these within a Bayesian nonparametric (BNP) paradigm. We find that the method developed\, Bayesian Nonparametric Step (BNP-Step)\, accurately determines the number and location of transitions between discrete states without any assumed kinetic model and learns the emission distribution characteristic of each state. In doing so\, we verify that BNP-Step can analyze sparser data sets containing higher noise and more closely-spaced states than otherwise resolved by current state-of-the-art methods. What is more\, BNP-Step rigorously propagates measurement uncertainty into uncertainty over state transition locations\, numbers\, and emission levels as characterized by the posterior. We demonstrate the performance of BNP-Step on both synthetic data as well as data drawn from force spectroscopy experiments. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-11-24-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:20231110T140000
DTEND;TZID=Asia/Seoul:20231110T160000
DTSTAMP:20231109T000051Z
CREATED:20231030T040327Z
LAST-MODIFIED:20231109T000051Z
UID:8661-1699624800-1699632000@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning
DESCRIPTION:We will discuss about “Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning.” bioRxiv (2023): 2023-09. \n  \nAbstract \nThe recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable\, the CRNN acts as a digital twin of a classical chemical reaction network. In this study\, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this\, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework\, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally\, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together\, this provides a new framework for robust\, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2023-11-10-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
END:VCALENDAR