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PRODID:-//Biomedical Mathematics Group - ECPv6.16.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230901T100000
DTEND;TZID=Asia/Seoul:20230901T120000
DTSTAMP:20230831T040832Z
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:20230809T124859Z
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:20230730T231258Z
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:20230726T042649Z
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:20230707T034944Z
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:20230615T052932Z
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:20230608T050231Z
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:20230529T032114Z
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:20230524T094243Z
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:20230515T040214Z
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:20230508T134254Z
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:20230419T071820Z
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:20230413T085616Z
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230407T140000
DTEND;TZID=Asia/Seoul:20230407T160000
DTSTAMP:20230331T040312Z
CREATED:20230331T040259Z
LAST-MODIFIED:20230331T040312Z
UID:7562-1680876000-1680883200@www.ibs.re.kr
SUMMARY:Yun Min Song\, The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms
DESCRIPTION:We will discuss about “The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms”\,Rombouts\, Jan\, Sarah Verplaetse\, and Lendert Gelens.\, bioRxiv (2023) \nAbstract \n\n\n\nMany biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here\, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models\, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay\, of double regulation (acting on production and degradation)\, and of enzymatic degradation.
URL:https://www.ibs.re.kr/bimag/event/2023-04-07-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:20230324T140000
DTEND;TZID=Asia/Seoul:20230324T160000
DTSTAMP:20230228T075941Z
CREATED:20230228T075941Z
LAST-MODIFIED:20230228T075941Z
UID:7395-1679666400-1679673600@www.ibs.re.kr
SUMMARY:Candan Celik\, The effect of microRNA on protein variability and gene expression fidelity
DESCRIPTION:We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”\, Hilfinger\, Andreas\, and Raymond Fan.\, Biophysical journal 122.3 (2023): 537a. \nAbstract \n\nSmall regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability of gene expression around average levels. Here we quantify noise in stochastic gene expression models with and without such regulation. Our results suggest that silencing mRNA post-transcriptionally will always increase rather than decrease gene expression noise when the silencing of mRNA also increases its degradation as is expected for microRNA interactions with mRNA. In that regime we also find that silencing mRNA generally reduces the fidelity of signal transmission from deterministically varying upstream factors to protein levels. These findings suggest that microRNA binding to mRNA does not generically confer precision to protein expression
URL:https://www.ibs.re.kr/bimag/event/2023-03-24-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:20230317T140000
DTEND;TZID=Asia/Seoul:20230317T160000
DTSTAMP:20230315T020610Z
CREATED:20230228T075515Z
LAST-MODIFIED:20230315T020610Z
UID:7393-1679061600-1679068800@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Single-sample landscape entropy reveals the imminent phase transition during disease progression
DESCRIPTION:We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”\, Liu R\, Chen P\, Chen L.\, Bioinformatics. 2020 Mar 1;36(5):1522-1532. \nAbstract \n\n\nMotivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt\, that is\, there is a tipping point during such a process at which the system state shifts from the normal state to a disease state. It is challenging to predict such disease state with the measured omics data\, in particular when only a single sample is available. \nResults: In this study\, we developed a novel approach\, i.e. single-sample landscape entropy (SLE) method\, to identify the tipping point during disease progression with only one sample data. Specifically\, by evaluating the disorder of a network projected from a single-sample data\, SLE effectively characterizes the criticality of this single sample network in terms of network entropy\, thereby capturing not only the signals of the impending transition but also its leading network\, i.e. dynamic network biomarkers. Using this method\, we can characterize sample-specific state during disease progression and thus achieve the disease prediction of each individual by only one sample. Our method was validated by successfully identifying the tipping points just before the serious disease symptoms from four real datasets of individuals or subjects\, including influenza virus infection\, lung cancer metastasis\, prostate cancer and acute lung injury.
URL:https://www.ibs.re.kr/bimag/event/2023-03-17-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230310T140000
DTEND;TZID=Asia/Seoul:20230310T160000
DTSTAMP:20230309T012315Z
CREATED:20230228T075546Z
LAST-MODIFIED:20230309T012315Z
UID:7391-1678456800-1678464000@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks
DESCRIPTION:We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”\, Briat\, Corentin\, Ankit Gupta\, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26. \nAbstract \n\nThe ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback can be used to achieve homeostasis when networks behave deterministically\, the effect of noise on their regulatory function is not understood. Here\, we combine probability and control theory to develop a theory of biological regulation that explicitly takes into account the noisy nature of biochemical reactions. We introduce tools for the analysis and design of robust homeostatic circuits and propose a new regulation motif\, which we call antithetic integral feedback. This motif exploits stochastic noise\, allowing it to achieve precise regulation in scenarios where similar deterministic regulation fails. Specifically\, antithetic integral feedback preserves the stability of the overall network\, steers the population of any regulated species to a desired set point\, and adapts perfectly. We suggest that this motif may be prevalent in endogenous biological circuits and useful when creating synthetic circuits.
URL:https://www.ibs.re.kr/bimag/event/2023-03-10-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:20230303T140000
DTEND;TZID=Asia/Seoul:20230303T160000
DTSTAMP:20230130T080633Z
CREATED:20230127T063333Z
LAST-MODIFIED:20230130T080633Z
UID:7280-1677852000-1677859200@www.ibs.re.kr
SUMMARY:Seho Park\, Dynamical information enables inference of gene regulation at single-cell scale
DESCRIPTION:We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”\, Zhang\, Stephen Y.\, and Michael PH Stumpf.\, bioRxiv (2023): 2023-01. \nAbstract \n\nCellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes\, we propose locaTE\, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific\, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods\, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets\, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data\, locaTE provides a powerful\, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.
URL:https://www.ibs.re.kr/bimag/event/2023-03-03-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230217T140000
DTEND;TZID=Asia/Seoul:20230217T160000
DTSTAMP:20230214T144610Z
CREATED:20230127T011541Z
LAST-MODIFIED:20230214T144610Z
UID:7278-1676642400-1676649600@www.ibs.re.kr
SUMMARY:Hyeontae Jo\, Characterizing possible failure modes in physics-informed neural networks
DESCRIPTION:We will discuss about “Characterizing possible failure modes in physics-informed neural networks”\, Krishnapriyan\, Aditi\, et al.\, Advances in Neural Information Processing Systems 34 (2021): 26548-26560. \nAbstract \n\n\n\n\n\n\nRecent work in scientific machine learning has developed so-called physics-informed neural network (PINN) models. The typical approach is to incorporate physical domain knowledge as soft constraints on an empirical loss function and use existing machine learning methodologies to train the model. We demonstrate that\, while existing PINN methodologies can learn good models for relatively trivial problems\, they can easily fail to learn relevant physical phenomena for even slightly more complex problems. In particular\, we analyze several distinct situations of widespread physical interest\, including learning differential equations with convection\, reaction\, and diffusion operators. We provide evidence that the soft regularization in PINNs\, which involves PDE-based differential operators\, can introduce a number of subtle problems\, including making the problem more ill-conditioned. Importantly\, we show that these possible failure modes are not due to the lack of expressivity in the NN architecture\, but that the PINN’s setup makes the loss landscape very hard to optimize. We then describe two promising solutions to address these failure modes. The first approach is to use curriculum regularization\, where the PINN’s loss term starts from a simple PDE regularization\, and becomes progressively more complex as the NN gets trained. The second approach is to pose the problem as a sequence-to-sequence learning task\, rather than learning to predict the entire space-time at once. Extensive testing shows that we can achieve up to 1-2 orders of magnitude lower error with these methods as compared to regular PINN training. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-02-17-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230210T140000
DTEND;TZID=Asia/Seoul:20230210T160000
DTSTAMP:20230208T015345Z
CREATED:20230126T235218Z
LAST-MODIFIED:20230208T015345Z
UID:7276-1676037600-1676044800@www.ibs.re.kr
SUMMARY:Dongju Lim\, Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors
DESCRIPTION:We will discuss about “Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors”\, Magal\, Noa\, et al.\, Chronic Stress 6 (2022): 24705470221100987. \nAbstract \n\n\n\n\nBackground: Chronic stress is a highly prevalent condition that may stem from different sources and can substantially impact physiology and behavior\, potentially leading to impaired mental and physical health. Multiple physiological and behavioral lifestyle features can now be recorded unobtrusively in daily-life using wearable sensors. The aim of the current study was to identify a distinct set of physiological and behavioral lifestyle features that are associated with elevated levels of chronic stress across different stress sources. \nMethods: For that\, 140 healthy female participants completed the Trier inventory for chronic stress (TICS) before wearing the Fitbit Charge3 sensor for seven consecutive days while maintaining their daily routine. Physiological and lifestyle features that were extracted from sensor data\, alongside demographic features\, were used to predict high versus low chronic stress with support vector machine classifiers\, applying out-of-sample model testing. \nResults: The model achieved 79% classification accuracy for chronic stress from a social tension source. A mixture of physiological (resting heart-rate\, heart-rate circadian characteristics)\, lifestyle (steps count\, sleep onset and sleep regularity) and non-sensor demographic features (smoking status) contributed to this classification. \nConclusion: As wearable technologies continue to rapidly evolve\, integration of daily-life indicators could improve our understanding of chronic stress and its impact of physiology and behavior.
URL:https://www.ibs.re.kr/bimag/event/2023-02-10-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230203T140000
DTEND;TZID=Asia/Seoul:20230203T160000
DTSTAMP:20230130T080459Z
CREATED:20230126T234906Z
LAST-MODIFIED:20230130T080459Z
UID:7274-1675432800-1675440000@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Estimating and Assessing Differential Equation Models with Time-Course Data
DESCRIPTION:We will discuss about “Estimating and Assessing Differential Equation Models with Time-Course Data”\, Wong\, Samuel WK\, Shihao Yang\, and S. C. Kou\, arXiv preprint arXiv:2212.10653 (2022). \nAbstract \n\nOrdinary differential equation (ODE) models are widely used to describe chemical or biological processes. This article considers the estimation and assessment of such models on the basis of time-course data. Due to experimental limitations\, time-course data are often noisy and some components of the system may not be observed. Furthermore\, the computational demands of numerical integration have hindered the widespread adoption of time-course analysis using ODEs. To address these challenges\, we explore the efficacy of the recently developed MAGI (MAnifold-constrained Gaussian process Inference) method for ODE inference. First\, via a range of examples we show that MAGI is capable of inferring the parameters and system trajectories\, including unobserved components\, with appropriate uncertainty quantification. Second\, we illustrate how MAGI can be used to assess and select different ODE models with time-course data based on MAGI’s efficient computation of model predictions. Overall\, we believe MAGI is a useful method for the analysis of time-course data in the context of ODE models\, which bypasses the need for any numerical integration.
URL:https://www.ibs.re.kr/bimag/event/2023-02-03-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230127T110000
DTEND;TZID=Asia/Seoul:20230127T130000
DTSTAMP:20230126T010049Z
CREATED:20221227T081814Z
LAST-MODIFIED:20230126T010049Z
UID:7180-1674817200-1674824400@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Optimal information networks: Application for data-driven integrated health in populations
DESCRIPTION:We will discuss about “Optimal information networks: Application for data-driven integrated health in populations”\, Servadio\, Joseph L.\, and Matteo Convertino\, Science Advances 4.2 (2018): e1701088. \nAbstract \n\n\n\nDevelopment of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free\, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables\, instead considering only individual correlations. In addition\, a unified method for assessing integrated health statuses of populations is lacking\, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets\, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator\, representing integrated health in a city.
URL:https://www.ibs.re.kr/bimag/event/2023-01-27-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230120T110000
DTEND;TZID=Asia/Seoul:20230120T130000
DTSTAMP:20221228T011141Z
CREATED:20221228T011141Z
LAST-MODIFIED:20221228T011141Z
UID:7186-1674212400-1674219600@www.ibs.re.kr
SUMMARY:Yun Min Song\, A scalable approach for solving chemical master equations based on modularization and filtering
DESCRIPTION:We will discuss about “A scalable approach for solving chemical master equations based on modularization and filtering\n”\, Fang\, Zhou\, Ankit Gupta\, and Mustafa Khammash.\, bioRxiv (2022). \nAbstract \n\nSolving the chemical master equation (CME) that characterizes the probability evolution of stochastically reacting processes is greatly important for analyzing intracellular reaction systems. Conventional methods for solving CMEs include the simulation-based Monte-Carlo methods\, the direct approach (e.g.\, the finite state projection)\, and so on; however\, they usually do not scale very well with the system dimension either in terms of accuracy or efficiency. To mitigate this problem\, we propose a new computational method based on modularization and filtering. Our method first divides the whole system into a leader system and several conditionally independent follower subsystems. Then\, we solve the CME by applying the Monte Carlo method to the leader system and the direct approach to the filtered CMEs that characterize the conditional probabilities of the follower subsystems. The system decomposition involved in our method is optimized so that all the subproblems above are low dimensional\, and\, therefore\, our approach scales more favorably with the system dimension. Finally\, we demonstrate the efficiency and accuracy of our approach in high-dimensional estimation and inference problems using several biologically relevant examples.
URL:https://www.ibs.re.kr/bimag/event/2023-01-20-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230112T130000
DTEND;TZID=Asia/Seoul:20230112T150000
DTSTAMP:20230108T080223Z
CREATED:20221228T005748Z
LAST-MODIFIED:20230108T080223Z
UID:7183-1673528400-1673535600@www.ibs.re.kr
SUMMARY:Hyun Kim\, Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics
DESCRIPTION:We will discuss about “Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics”\n\, Lin\, Baihan.\, arXiv preprint arXiv:2204.14048 (2022). \nAbstract \n\nThe absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate\, differentiate\, and compete\, i.e. the cell ecology. With the recent advancement of the single-cell RNA-sequencing (RNA-seq)\, we can potentially describe such a link by constructing network graphs that characterize the similarity of the gene expression profiles of the cell-specific transcriptional programs\, and analyzing these graphs systematically using the summary statistics informed by the algebraic topology. We propose the single-cell topological simplicial analysis (scTSA). Applying this approach to the single-cell gene expression profiles from local networks of cells in different developmental stages with different outcomes reveals a previously unseen topology of cellular ecology. These networks contain an abundance of cliques of single-cell profiles bound into cavities that guide the emergence of more complicated habitation forms. We visualize these ecological patterns with topological simplicial architectures of these networks\, compared with the null models. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38\,731 cells\, 25 cell types and 12 time steps\, our approach highlights the gastrulation as the most critical stage\, consistent with consensus in developmental biology. As a nonlinear\, model-independent\, and unsupervised framework\, our approach can also be applied to tracing multi-scale cell lineage\, identifying critical stages\, or creating pseudo-time series.
URL:https://www.ibs.re.kr/bimag/event/2023-01-13-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230106T150000
DTEND;TZID=Asia/Seoul:20230106T170000
DTSTAMP:20230102T121025Z
CREATED:20221227T081429Z
LAST-MODIFIED:20230102T121025Z
UID:7178-1673017200-1673024400@www.ibs.re.kr
SUMMARY:Aurelio A. de los Reyes V\, Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
DESCRIPTION:We will discuss about “Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems”\, Linka\, Kevin\, et al.\, Computer Methods in Applied Mechanics and Engineering Volume 402\, 1 December 2022\, 115346 \nAbstract \n\n\n\n\nUnderstanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines\, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems\, identify patterns in big data\, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However\, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data\, physics\, and uncertainties by combining neural networks\, physics informed modeling\, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system\, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics\, robustly solve forward and inverse problems\, and perform well for both interpolation and extrapolation\, even for a small amount of noisy and incomplete data. At only minor additional cost\, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference\, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks\, Bayesian Inference\, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease\, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and\, more broadly\, to a wide variety of nonlinear dynamical systems. Our source code and examples are available at https://github.com/LivingMatterLab/xPINNs.
URL:https://www.ibs.re.kr/bimag/event/2023-01-06-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221230T150000
DTEND;TZID=Asia/Seoul:20221230T170000
DTSTAMP:20221230T060020Z
CREATED:20221222T082525Z
LAST-MODIFIED:20221230T060020Z
UID:7080-1672412400-1672419600@www.ibs.re.kr
SUMMARY:Candan Celik\, Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms
DESCRIPTION:We will discuss about “Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms”\,Jia\, Chen\, and Youming Li\, BioRxiv (2022). \nAbstract \n\n\n\nClassical gene expression models assume exponential switching time distributions between the active and inactive promoter states. However\, recent experiments have shown that many genes in mammalian cells may produce non-exponential switching time distributions\, implying the existence of multiple promoter states and molecular memory in the promoter switching dynamics. Here we analytically solve a gene expression model with random bursting and complex promoter switching\, and derive the time-dependent distributions of the mRNA and protein copy numbers\, generalizing the steady-state solution obtained in [SIAM J. Appl. Math. 72\, 789-818 (2012)] and [SIAM J. Appl. Math. 79\, 1007-1029 (2019)]. Using multiscale simplification techniques\, we find that molecular memory has no influence on the time-dependent distribution when promoter switching is very fast or very slow\, while it significantly affects the distribution when promoter switching is neither too fast nor too slow. By analyzing the dynamical phase diagram of the system\, we also find that molecular memory in the inactive gene state weakens transient and stationary bimodality of the copy number distribution\, while molecular memory in the active gene state enhances such bimodality.
URL:https://www.ibs.re.kr/bimag/event/2022-12-30-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221223T150000
DTEND;TZID=Asia/Seoul:20221223T170000
DTSTAMP:20221222T082248Z
CREATED:20221222T082248Z
LAST-MODIFIED:20221222T082248Z
UID:7075-1671807600-1671814800@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Optimal control of aging in complex networks
DESCRIPTION:We will discuss about “Optimal control of aging in complex networks”\,\nSun\, Eric D.\, Thomas CT Michaels\, and L. Mahadevan\, Proceedings of the National Academy of Sciences 117.34 (2020): 20404-20410. \nAbstract \n\n\n\nMany complex systems experience damage accumulation\, which leads to aging\, manifest as an increasing probability of system collapse with time. This naturally raises the question of how to maximize health and longevity in an aging system at minimal cost of maintenance and intervention. Here\, we pose this question in the context of a simple interdependent network model of aging in complex systems and show that it exhibits cascading failures. We then use both optimal control theory and reinforcement learning alongside a combination of analysis and simulation to determine optimal maintenance protocols. These protocols may motivate the rational design of strategies for promoting longevity in aging complex systems with potential applications in therapeutic schedules and engineered system maintenance.
URL:https://www.ibs.re.kr/bimag/event/2022-12-23-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221216T130000
DTEND;TZID=Asia/Seoul:20221216T150000
DTSTAMP:20221214T122407Z
CREATED:20221214T122407Z
LAST-MODIFIED:20221214T122407Z
UID:7022-1671195600-1671202800@www.ibs.re.kr
SUMMARY:Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators
DESCRIPTION:We will discuss about “Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators”\, Karapetyan\, Sargis\, and Nicolas E. Buchler\,Physical Review E 92.6 (2015): 062712. \nAbstract \n\n\n\nGenetic oscillators\, such as circadian clocks\, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study\, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA\, despite traditionally being considered a fast parameter\, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple\, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics\, which are often omitted in biophysical models of gene circuits\, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.
URL:https://www.ibs.re.kr/bimag/event/2022-12-16-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221202T150000
DTEND;TZID=Asia/Seoul:20221202T170000
DTSTAMP:20221128T010402Z
CREATED:20221128T010402Z
LAST-MODIFIED:20221128T010402Z
UID:6906-1669993200-1670000400@www.ibs.re.kr
SUMMARY:Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors
DESCRIPTION:We will discuss about “Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors”\, Vipond\, Oliver\, et al\, Proceedings of the National Academy of Sciences 118.41 (2021): e2102166118. \nAbstract\nHighly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data—often with outliers\, artifacts\, and mislabeled points—such as those from tissues\, remains a challenge. The mathematical field that extracts information from the shape of data\, topological data analysis (TDA)\, has expanded its capability for analyzing real-world datasets in recent years by extending theory\, statistics\, and computation. An extension to the standard theory to handle heterogeneous data is multiparameter persistent homology (MPH). Here we provide an application of MPH landscapes\, a statistical tool with theoretical underpinnings. MPH landscapes\, computed for (noisy) data from agent-basedMultiparameter persistent homology landscapes identify immune cell spatial patterns in tumors model simulations of immune cells infiltrating into a spheroid\, are shown to surpass existing spatial statistics and one-parameter persistent homology. We then apply MPH landscapes to study immune cell location in digital histology images from head and neck cancer. We quantify intratumoral immune cells and find that infiltrating regulatory T cells have more prominent voids in their spatial patterns than macrophages. Finally\, we consider how TDA can integrate and interrogate data of different types and scales\, e.g.\, immune cell locations and regions with differing levels of oxygenation. This work highlights the power of MPH landscapes for quantifying\, characterizing\, and comparing features within the tumor microenvironment in synthetic and real datasets.
URL:https://www.ibs.re.kr/bimag/event/2022-12-02-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221118T150000
DTEND;TZID=Asia/Seoul:20221118T170000
DTSTAMP:20221117T034958Z
CREATED:20221117T034958Z
LAST-MODIFIED:20221117T034958Z
UID:6871-1668783600-1668790800@www.ibs.re.kr
SUMMARY:Detecting critical state before phase transition of complex biological systems by hidden Markov model
DESCRIPTION:We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”\, Chen\, Pei\, et al. Bioinformatics 32.14 (2016): 2143-2150. \n  \nAbstract \nMotivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task\, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages\, i.e. before-transition state\, pre-transition state and after-transition state\, which can be considered as three different Markov processes. \nResults: By exploring the rich dynamical information provided by high-throughput data\, we present a novel computational method\, i.e. hidden Markov model (HMM) based approach\, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process)\, thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness\, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets\, and further identify the pre-transition states as well as their critical modules for three real datasets\, i.e. the acute lung injury triggered by phosgene inhalation\, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-jc-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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