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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240202T140000
DTEND;TZID=Asia/Seoul:20240202T170000
DTSTAMP:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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:20260422T183335
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231027T140000
DTEND;TZID=Asia/Seoul:20231027T160000
DTSTAMP:20260422T183335
CREATED:20230929T230744Z
LAST-MODIFIED:20231018T020236Z
UID:8566-1698415200-1698422400@www.ibs.re.kr
SUMMARY:Hyun Kim\, Significance analysis for clustering with single-cell RNA-sequencing data
DESCRIPTION:We will discuss about “Significance analysis for clustering with single-cell RNA-sequencing data”\, Grabski\, Isabella N.\, Kelly Street\, and Rafael A. Irizarry.\, Nature Methods (2023): 1-7. \nAbstract \n\n\n\nUnsupervised clustering of single-cell RNA-sequencing data enables the identification of distinct cell populations. However\, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty. We find that not addressing known sources of variability in a statistically rigorous manner can lead to overconfidence in the discovery of novel cell types. Here we extend a previous method\, significance of hierarchical clustering\, to propose a model-based hypothesis testing approach that incorporates significance analysis into the clustering algorithm and permits statistical evaluation of clusters as distinct cell populations. We also adapt this approach to permit statistical assessment on the clusters reported by any algorithm. Finally\, we extend these approaches to account for batch structure. We benchmarked our approach against popular clustering workflows\, demonstrating improved performance. To show practical utility\, we applied our approach to the Human Lung Cell Atlas and an atlas of the mouse cerebellar cortex\, identifying several cases of over-clustering and recapitulating experimentally validated cell type definitions.
URL:https://www.ibs.re.kr/bimag/event/2023-10-27-jc/
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231020T140000
DTEND;TZID=Asia/Seoul:20231020T160000
DTSTAMP:20260422T183335
CREATED:20230929T231212Z
LAST-MODIFIED:20231018T020716Z
UID:8568-1697810400-1697817600@www.ibs.re.kr
SUMMARY:Hyeontae Jo\, AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records
DESCRIPTION:We will discuss about “AutoScore:A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records”\, Xie\, Feng\, et al.\, JMIR medical informatics 8.10 (2020): e21798. \nAbstract\nBackground: Risk scores can be useful in clinical risk stratification and accurate allocations of medical resources\, helping health providers improve patient care. Point-based scores are more understandable and explainable than other complex models and are now widely used in clinical decision making. However\, the development of the risk scoring model is nontrivial and has not yet been systematically presented\, with few studies investigating methods of clinical score generation using electronic health records. \nObjective: This study aims to propose AutoScore\, a machine learning-based automatic clinical score generator consisting of 6 modules for developing interpretable point-based scores. Future users can employ the AutoScore framework to create clinical scores effortlessly in various clinical applications. \nMethods: We proposed the AutoScore framework comprising 6 modules that included variable ranking\, variable transformation\, score derivation\, model selection\, score fine-tuning\, and model evaluation. To demonstrate the performance of AutoScore\, we used data from the Beth Israel Deaconess Medical Center to build a scoring model for mortality prediction and then compared the data with other baseline models using the receiver operating characteristic analysis. A software package in R 3.5.3 (R Foundation) was also developed to demonstrate the implementation of AutoScore. \nResults: Implemented on the data set with 44\,918 individual admission episodes of intensive care\, the AutoScore-created scoring models performed comparably well as other standard methods (ie\, logistic regression\, stepwise regression\, least absolute shrinkage and selection operator\, and random forest) in terms of predictive accuracy and model calibration but required fewer predictors and presented high interpretability and accessibility. The nine-variable\, AutoScore-created\, point-based scoring model achieved an area under the curve (AUC) of 0.780 (95% CI 0.764-0.798)\, whereas the model of logistic regression with 24 variables had an AUC of 0.778 (95% CI 0.760-0.795). Moreover\, the AutoScore framework also drives the clinical research continuum and automation with its integration of all necessary modules. \nConclusions: We developed an easy-to-use\, machine learning-based automatic clinical score generator\, AutoScore; systematically presented its structure; and demonstrated its superiority (predictive performance and interpretability) over other conventional methods using a benchmark database. AutoScore will emerge as a potential scoring tool in various medical applications.
URL:https://www.ibs.re.kr/bimag/event/2023-10-20-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:20231006T140000
DTEND;TZID=Asia/Seoul:20231006T160000
DTSTAMP:20260422T183335
CREATED:20230929T225914Z
LAST-MODIFIED:20231005T021126Z
UID:8564-1696600800-1696608000@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Power spectral estimate for discrete data
DESCRIPTION:We will discuss about “Power spectral estimate for discrete data”\, Nobert Marwan and Tobias Braun\, Chaos (2023). \n  \nAbstract \n\nThe identification of cycles in periodic signals is a ubiquitous problem in time series analysis. Many real-world datasets only record a signal as a series of discrete events or symbols. In some cases\, only a sequence of (non-equidistant) times can be assessed. Many of these signals are furthermore corrupted by noise and offer a limited number of samples\, e.g.\, cardiac signals\, astronomical light curves\, stock market data\, or extreme weather events. We propose a novel method that provides a power spectral estimate for discrete data. The edit distance is a distance measure that allows us to quantify similarities between non-equidistant event sequences of unequal lengths. However\, its potential to quantify the frequency content of discrete signals has so far remained unexplored. We define a measure of serial dependence based on the edit distance\, which can be transformed into a power spectral estimate (EDSPEC)\, analogous to the Wiener–Khinchin theorem for continuous signals. The proposed method is applied to a variety of discrete paradigmatic signals representing random\, correlated\, chaotic\, and periodic occurrences of events. It is effective at detecting periodic cycles even in the presence of noise and for short event series. Finally\, we apply the EDSPEC method to a novel catalog of European atmospheric rivers (ARs). ARs are narrow filaments of extensive water vapor transport in the lower troposphere and can cause hazardous extreme precipitation events. Using the EDSPEC method\, we conduct the first spectral analysis of European ARs\, uncovering seasonal and multi-annual cycles along different spatial domains. The proposed method opens new research avenues in studying of periodic discrete signals in complex real-world systems.
URL:https://www.ibs.re.kr/bimag/event/2023-10-06-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:20230922T140000
DTEND;TZID=Asia/Seoul:20230922T160000
DTSTAMP:20260422T183335
CREATED:20230901T091012Z
LAST-MODIFIED:20230906T083720Z
UID:8440-1695391200-1695398400@www.ibs.re.kr
SUMMARY:Yun Min Song\, A data-driven approach for timescale decomposition of biochemical reaction networks
DESCRIPTION:We will discuss about “A data-driven approach for timescale decomposition of biochemical reaction networks”\, Amir Akbari\, Zachary B. Haiman\, Bernhard O. Palsson\, bioRxiv (2023) \nAbstract \n\nUnderstanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here\, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies\, concentration pools\, and coherent structures from time-series data\, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways\, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover\, by analyzing the timescale hierarchy of the glycolytic pathway\, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically\, we show that glycolysis is a cofactor driven pathway\, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall\, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models\, thus facilitating model reduction and personalized medicine applications. \n\n 
URL:https://www.ibs.re.kr/bimag/event/2023-09-22-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:20230915T140000
DTEND;TZID=Asia/Seoul:20230915T160000
DTSTAMP:20260422T183335
CREATED:20230829T100538Z
LAST-MODIFIED:20230914T051626Z
UID:8371-1694786400-1694793600@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
DESCRIPTION:We will discuss about “Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks\n”\,Briat\, Corentin\, Ankit Gupta\, and Mustafa Khammash.\, Journal of The Royal Society Interface 15.143 (2018): 20180079 \nAbstract \n\n\n\n\n\n\n\nThe ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called homeostasis and this ability is crucial for the cell’s survival and proper functioning. Understanding how cells can achieve homeostasis\, despite the intrinsic noise or randomness in their dynamics\, is fundamentally important for both systems and synthetic biology. In this context\, a significant development is the proposed antithetic integral feedback (AIF) motif\, which is found in natural systems\, and is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. From the standpoint of applications\, one drawback of this motif is that it often leads to an increased cell-to-cell heterogeneity or variance when compared to a constitutive (i.e. open-loop) control strategy. Our goal in this paper is to show that this performance deterioration can be countered by combining the AIF motif and a negative feedback strategy. Using a tailored moment closure method\, we derive approximate expressions for the stationary variance for the controlled network that demonstrate that increasing the strength of the negative feedback can indeed decrease the variance\, sometimes even below its constitutive level. Numerical results verify the accuracy of these results and we illustrate them by considering three biomolecular networks with two types of negative feedback strategies. Our computational analysis indicates that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time.
URL:https://www.ibs.re.kr/bimag/event/2023-09-15-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:20230908T140000
DTEND;TZID=Asia/Seoul:20230908T160000
DTSTAMP:20260422T183335
CREATED:20230829T100233Z
LAST-MODIFIED:20230907T044351Z
UID:8369-1694181600-1694188800@www.ibs.re.kr
SUMMARY:Dongju Lim\, Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics
DESCRIPTION:We will discuss about “Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics”\, Wang\, Yiling\, et al.\, bioRxiv (2023): 2023-08. \n  \nAbstract \n\n\n\n\n\n\nThe classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching\, transcription\, translation\, degradation and dilution. While this model is easily simulated\, its analytical solution remains an unsolved problem. Here we modify this model to explicitly include cell-cycle dynamics and then derive an exact solution for the time-dependent joint distribution of mRNA and protein numbers. We show large differences between this model and the classical model which captures cell-cycle effects implicitly via effective first-order dilution reactions. In particular we find that the Fano factor of protein numbers calculated from a population snapshot measurement are underestimated by the classical model whereas the correlation between mRNA and protein can be either over- or underestimated\, depending on the timescales of mRNA degradation and promoter switching relative to the mean cell-cycle duration time. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-09-08-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:20230901T100000
DTEND;TZID=Asia/Seoul:20230901T120000
DTSTAMP:20260422T183335
CREATED:20230810T082738Z
LAST-MODIFIED:20230831T040832Z
UID:8236-1693562400-1693569600@www.ibs.re.kr
SUMMARY:Hyeongjun Jang\, Generalized Michaelis–Menten rate law with time-varying molecular concentrations
DESCRIPTION:We will discuss about “Generalized Michaelis–Menten rate law with time-varying molecular concentrations”\, Lim\, Roktaek\, et al.\,bioRxiv (2022): 2022-01 \n  \nAbstract \n\n\n\n\n\n\nThe Michaelis–Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry\, biophysics\, cell biology\, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules\, at each moment\, approaches an equilibrium much faster than the molecular concentrations change. Yet\, this assumption is not always justified. Here\, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations\, termed the effective time-delay scheme (ETS)\, is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein– protein and protein–DNA interaction modeling\, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover)\, which goes beyond the quasi-steady state assumption.
URL:https://www.ibs.re.kr/bimag/event/2023-09-01-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:20230811T150000
DTEND;TZID=Asia/Seoul:20230811T170000
DTSTAMP:20260422T183335
CREATED:20230730T230909Z
LAST-MODIFIED:20230809T124859Z
UID:8141-1691766000-1691773200@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Decomposing predictability to identify dominant causal drivers in complex ecosystems
DESCRIPTION:We will discuss about “ Decomposing predictability to identify dominant causal drivers in complex ecosystems ”\,Suzuki\, Kenta\, Shin-ichiro S. Matsuzaki\, and Hiroshi Masuya.\, Proceedings of the National Academy of Sciences 119.42 (2022): e2204405119. \n  \nAbstract \n\nEcosystems are complex systems of various physical\, biological\, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity\, handling these data is a challenge for existing methods of time series–based causal inferences. Here\, we show that\, by harnessing contemporary machine learning approaches\, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach\, EcohNet\, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms\, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities\, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.
URL:https://www.ibs.re.kr/bimag/event/2023-08-11-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:20230804T140000
DTEND;TZID=Asia/Seoul:20230804T160000
DTSTAMP:20260422T183335
CREATED:20230729T064450Z
LAST-MODIFIED:20230730T231258Z
UID:8131-1691157600-1691164800@www.ibs.re.kr
SUMMARY:Seokhwan Moon\, The Internal Model Principle for Biomolecular Control Theory
DESCRIPTION:We will discuss about “ The Internal Model Principle for Biomolecular Control Theory ”\, Gupta\, Ankit\, and Mustafa Khammash.\, IEEE Open Journal of Control Systems 2 (2023): 63-69. \n  \nAbstract \nThe well-known Internal Model Principle (IMP) is a cornerstone of modern control theory. It stipulates the necessary conditions for asymptotic robustness of disturbance-prone dynamical systems by asserting that such a system must embed a subsystem in a feedback loop\, and this subsystem must be able to reduplicate the dynamic disturbance using only the regulated variable as the input. The insights provided by IMP can help in both designing suitable controllers and also in analysing the regulatory mechanisms in complex systems. So far the application of IMP in biology has been case-specific and ad hoc\, primarily due to the lack of generic versions of the IMP for biomolecular reaction networks that model biological processes. In this short article we highlight the need for an IMP in biology and discuss a recently developed version of it for biomolecular networks that exhibit maximal Robust Perfect Adaptation (maxRPA) by being robust to the maximum number of disturbance sources.
URL:https://www.ibs.re.kr/bimag/event/2023-08-04-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:20230728T100000
DTEND;TZID=Asia/Seoul:20230728T110000
DTSTAMP:20260422T183335
CREATED:20230619T074840Z
LAST-MODIFIED:20230726T042649Z
UID:7948-1690538400-1690542000@www.ibs.re.kr
SUMMARY:Yun Min Song\, The singularity response reveals entrainment properties of the plant circadian clock
DESCRIPTION:We will discuss about “The singularity response reveals entrainment properties of the plant circadian clock”\, Masuda\, Kosaku\, et al.\, Nature Communications 12.1 (2021): 864. \nAbstract \n\n\n\n\n\n\nCircadian clocks allow organisms to synchronize their physiological processes to diurnal variations. A phase response curve allows researchers to understand clock entrainment by revealing how signals adjust clock genes differently according to the phase in which they are applied. Comprehensively investigating these curves is difficult\, however\, because of the cost of measuring them experimentally. Here we demonstrate that fundamental properties of the curve are recoverable from the singularity response\, which is easily measured by applying a single stimulus to a cellular network in a desynchronized state (i.e. singularity). We show that the singularity response of Arabidopsis to light/dark and temperature stimuli depends on the properties of the phase response curve for these stimuli. The measured singularity responses not only allow the curves to be precisely reconstructed but also reveal organ-specific properties of the plant circadian clock. The method is not only simple and accurate\, but also general and applicable to other coupled oscillator systems as long as the oscillators can be desynchronized. This simplified method may allow the entrainment properties of the circadian clock of both plants and other species in nature.
URL:https://www.ibs.re.kr/bimag/event/2023-07-28/
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:20230707T140000
DTEND;TZID=Asia/Seoul:20230707T160000
DTSTAMP:20260422T183335
CREATED:20230529T032440Z
LAST-MODIFIED:20230707T034944Z
UID:7807-1688738400-1688745600@www.ibs.re.kr
SUMMARY:Hyun Kim\, scPrisma infers\, filters and enhances topological signals in single-cell data using spectral template matching
DESCRIPTION:We will discuss about “scPrisma infers\, filters and enhances topological signals in single-cell data using spectral template matching”\, Karin\, Jonathan\, Yonathan Bornfeld\, and Mor Nitzan.\, Nature Biotechnology (2023): 1-10. \nAbstract \n\n\n\nSingle-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple\, potentially cross-interfering\, biological signals. Here we propose scPrisma\, a spectral computational method that uses topological priors to decouple\, enhance and filter different classes of biological processes in single-cell data\, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells\, circadian rhythm and spatial zonation in liver lobules\, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell–cell interactions specific to predefined biological signals\, such as the circadian rhythm. We show scPrisma’s flexibility in incorporating prior knowledge\, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.
URL:https://www.ibs.re.kr/bimag/event/2023-07-07-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:20230622T140000
DTEND;TZID=Asia/Seoul:20230622T160000
DTSTAMP:20260422T183335
CREATED:20230615T052932Z
LAST-MODIFIED:20230615T052932Z
UID:7932-1687442400-1687449600@www.ibs.re.kr
SUMMARY:Dae Wook kim\, "Wearable data science for personalized digital medicine"
DESCRIPTION:We will discuss about “Wearable data science for personalized digital medicine” \nAbstract \nMillions of people currently use wearables such as the Apple Watch to monitor their physical activity\, heart rate\, and other physiological signals\, generating an unprecedented amount of wearable data. This presents an opportunity for digital medicine to advance precision medicine. However\, the noisy nature of this wearable data makes it appear unusable without new mathematical techniques to extract key signals from it. In this talk\, I will discuss several techniques we have developed for analyzing this noisy time-series data\, including the level-set Kalman filter-based data assimilation technique – a new state space estimation method that can estimate the phase of circadian rhythms. Additionally\, I will introduce a Kalman filter-assisted autoencoder used for anomaly detection in time-series data\, as well as feature engineering based on persistent homology and mathematical modeling. These techniques have practical applications\, such as sleep scoring\, detection of physiological changes related to COVID-19\, and daily mood prediction.
URL:https://www.ibs.re.kr/bimag/event/2023-06-22-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:20230609T140000
DTEND;TZID=Asia/Seoul:20230609T160000
DTSTAMP:20260422T183335
CREATED:20230529T032327Z
LAST-MODIFIED:20230608T050231Z
UID:7805-1686319200-1686326400@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, The energy cost and optimal design of networks for biological discrimination
DESCRIPTION:We will discuss about “The energy cost and optimal design of networks for biological discrimination”\, Yu\, Qiwei\, Anatoly B. Kolomeisky\, and Oleg A. Igoshin.\, Journal of the Royal Society Interface 19.188 (2022): 20210883. \nAbstract \n\n\nMany biological processes discriminate between correct and incorrect substrates through the kinetic proofreading mechanism that enables lower error at the cost of higher energy dissipation. Elucidating physico-chemical constraints for global minimization of dissipation and error is important for understanding enzyme evolution. Here\, we identify theoretically a fundamental error–cost bound that tightly constrains the performance of proofreading networks under any parameter variations preserving the rate discrimination between substrates. The bound is kinetically controlled\, i.e. completely determined by the difference between the transition state energies on the underlying free energy landscape. The importance of the bound is analysed for three biological processes. DNA replication by T7 DNA polymerase is shown to be nearly optimized\, i.e. its kinetic parameters place it in the immediate proximity of the error–cost bound. The isoleucyl-tRNA synthetase (IleRS) of E. coli also operates close to the bound\, but further optimization is prevented by the need for reaction speed. In contrast\, E. coli ribosome operates in a high-dissipation regime\, potentially in order to speed up protein production. Together\, these findings establish a fundamental error–dissipation relation in biological proofreading networks and provide a theoretical framework for studying error–dissipation trade-off in other systems with biological discrimination.
URL:https://www.ibs.re.kr/bimag/event/2023-06-09-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:20230602T140000
DTEND;TZID=Asia/Seoul:20230602T160000
DTSTAMP:20260422T183335
CREATED:20230529T032114Z
LAST-MODIFIED:20230529T032114Z
UID:7803-1685714400-1685721600@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Uncovering specific mechanisms across cell types in dynamical models
DESCRIPTION:We will discuss about “Uncovering specific mechanisms across cell types in dynamical models”\, Hauber\, Adrian Lukas\, Marcus Rosenblatt\, and Jens Timmer.\, bioRxiv (2023): 2023-01. \nAbstract \nOrdinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types\, e.g.\, healthy and cancer cells\, including the LASSO (least absolute shrinkage and selection operator). However\, when analyzing more than two cell types\, these approaches are not consistent\, and require the selection of a reference cell type\, which can affect the results. \nTo make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types\, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data\, and demonstrate that it outperforms existing approaches. Finally\, we also exemplify its application to published biological models including experimental data\, and link the results to independent biological measurements.
URL:https://www.ibs.re.kr/bimag/event/2023-06-02-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:20230526T140000
DTEND;TZID=Asia/Seoul:20230526T160000
DTSTAMP:20260422T183335
CREATED:20230430T034034Z
LAST-MODIFIED:20230524T094243Z
UID:7650-1685109600-1685116800@www.ibs.re.kr
SUMMARY:Hyeontae Jo\,Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
DESCRIPTION:We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”\, Zhao\, Shuai\, et al.\, IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578. \nAbstract \nPhysics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics\, this article proposes a PIML-based parameter estimation method demonstrated by a case study of dc–dc Buck converter. A deep neural network and the dynamic models of the converter are seamlessly coupled. It overcomes the challenges related to training data\, accuracy\, and robustness which a typical data-driven approach has. This exemplary application envisions to provide a new perspective for tailoring existing machine learning tools for power electronics.
URL:https://www.ibs.re.kr/bimag/event/2023-05-26-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:20230519T140000
DTEND;TZID=Asia/Seoul:20230519T160000
DTSTAMP:20260422T183335
CREATED:20230430T033701Z
LAST-MODIFIED:20230515T040214Z
UID:7648-1684504800-1684512000@www.ibs.re.kr
SUMMARY:Dongju Lim\, A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease
DESCRIPTION:We will discuss about “A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease”\, Alexandersen\, Christoffer G.\, et al.\, Journal of the Royal Society Interface 20.198 (2023): 20220607. \nAbstract \n\n\n\n\n\n\nAlzheimer’s disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale\, Alzheimer’s patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein\, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology\, we find that our model explains AD-related frequency slowing\, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses\, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.
URL:https://www.ibs.re.kr/bimag/event/2023-05-19-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:20230512T110000
DTEND;TZID=Asia/Seoul:20230512T130000
DTSTAMP:20260422T183335
CREATED:20230430T155858Z
LAST-MODIFIED:20230508T134254Z
UID:7653-1683889200-1683896400@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Inference and uncertainty quantification of stochastic gene expression via synthetic models
DESCRIPTION:We will discuss about “Inference and uncertainty quantification of stochastic gene expression via synthetic models”\, Öcal et al.\, J. R. Soc. Interface. \nAbstract \n\n\n\n\nEstimating uncertainty in model predictions is a central task in quantitativebiology. Biological models at the single-cell level are intrinsically stochastic and nonlinear\, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest\, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations\, a synthetic model\, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets\, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
URL:https://www.ibs.re.kr/bimag/event/2023-05-12-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:20230421T140000
DTEND;TZID=Asia/Seoul:20230421T160000
DTSTAMP:20260422T183335
CREATED:20230331T040917Z
LAST-MODIFIED:20230419T071820Z
UID:7566-1682085600-1682092800@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Improving gene regulatory network inference and assessment: The importance of using network structure
DESCRIPTION:We will discuss about “Improving gene regulatory network inference and assessment: The importance of using network structure”\, Escorcia-Rodríguez\, Juan M.\, et al.\, bioRxiv (2023): 2023-01. \nAbstract \n\n\n\n\nGene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here\, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard\, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that methods inferring co-expression networks should no longer be assessed equally with those inferring regulatory interactions. While methods inferring regulatory interactions perform better in global regulatory network inference than co-expression-based methods\, the latter is better suited to infer function-specific regulons and co-regulation networks. When merging expression data\, the size increase should outweigh the noise inclusion and graph structure should be considered when integrating the inferences. We conclude with guidelines to take advantage of inference methods and their assessment based on the applications and available expression datasets. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-04-21-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:20230414T140000
DTEND;TZID=Asia/Seoul:20230414T160000
DTSTAMP:20260422T183335
CREATED:20230331T040622Z
LAST-MODIFIED:20230413T085616Z
UID:7564-1681480800-1681488000@www.ibs.re.kr
SUMMARY:Hyun Kim\, Comparison of transformations for single-cell RNA-seq data
DESCRIPTION:We will discuss about “Comparison of transformations for single-cell RNA-seq data”\,Ahlmann-Eltze\, Constantin\, and Wolfgang Huber\, Nature Methods (2023): 1-8. \nAbstract \n\n\n\nThe count table\, a numeric matrix of genes × cells\, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so that the variance is similar across the dynamic range. These steps are intended to make subsequent application of generic statistical methods more palatable. Here\, we describe four transformation approaches based on the delta method\, model residuals\, inferred latent expression state and factor analysis. We compare their strengths and weaknesses and find that the latter three have appealing theoretical properties; however\, in benchmarks using simulated and real-world data\, it turns out that a rather simple approach\, namely\, the logarithm with a pseudo-count followed by principal-component analysis\, performs as well or better than the more sophisticated alternatives. This result highlights limitations of current theoretical analysis as assessed by bottom-line performance benchmarks.
URL:https://www.ibs.re.kr/bimag/event/2023-04-14-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
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