BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//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:20230210T140000
DTEND;TZID=Asia/Seoul:20230210T160000
DTSTAMP:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
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:20260423T031132
CREATED:20221117T034958Z
LAST-MODIFIED:20221117T034958Z
UID:6871-1668783600-1668790800@www.ibs.re.kr
SUMMARY:Detecting critical state before phase transition of complex biological systems by hidden Markov model
DESCRIPTION:We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”\, Chen\, Pei\, et al. Bioinformatics 32.14 (2016): 2143-2150. \n  \nAbstract \nMotivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task\, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages\, i.e. before-transition state\, pre-transition state and after-transition state\, which can be considered as three different Markov processes. \nResults: By exploring the rich dynamical information provided by high-throughput data\, we present a novel computational method\, i.e. hidden Markov model (HMM) based approach\, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process)\, thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness\, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets\, and further identify the pre-transition states as well as their critical modules for three real datasets\, i.e. the acute lung injury triggered by phosgene inhalation\, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-jc-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221111T150000
DTEND;TZID=Asia/Seoul:20221111T170000
DTSTAMP:20260423T031132
CREATED:20221028T015855Z
LAST-MODIFIED:20221107T064232Z
UID:6740-1668178800-1668186000@www.ibs.re.kr
SUMMARY:PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
DESCRIPTION:We will discuss about “PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations”\,\nZhong\, Weiheng\, and Hadi Meidani\, Computer Methods in Applied Mechanics and Engineering 403 (2023): 115664. \nAbstract\nWe propose a new class of physics-informed neural networks\, called the Physics-Informed Variational Auto-Encoder (PI-VAE)\, to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In these problems the governing equations are known but only a limited number of measurements of system parameters are available. PI-VAE consists of a variational autoencoder (VAE)\, which generates samples of system variables and parameters. This generative model is integrated with the governing equations. In this integration\, the derivatives of VAE outputs are readily calculated using automatic differentiation\, and used in the physics-based loss term. In this work\, the loss function is chosen to be the Maximum Mean Discrepancy (MMD) for improved performance\, and neural network parameters are updated iteratively using the stochastic gradient descent algorithm. We first test the proposed method on approximating stochastic processes. Then we study three types of problems related to SDEs: forward and inverse problems together with mixed problems where system parameters and solutions are simultaneously calculated. The satisfactory accuracy and efficiency of the proposed method are numerically demonstrated in comparison with physics-informed Wasserstein generative adversarial network (PI-WGAN).
URL:https://www.ibs.re.kr/bimag/event/2022-11-11-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221104T150000
DTEND;TZID=Asia/Seoul:20221104T170000
DTSTAMP:20260423T031132
CREATED:20220930T035218Z
LAST-MODIFIED:20221030T231656Z
UID:6648-1667574000-1667581200@www.ibs.re.kr
SUMMARY:Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach
DESCRIPTION:We will discuss about “Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach”\, Öcal\, Kaan\, Guido Sanguinetti\, and Ramon Grima.\, arXiv preprint arXiv:2210.05329 (2022). \nAbstract: \nThe complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist’s toolkit. For stochastic reaction networks described using the Chemical Master Equation\, commonly used methods include time-scale separation\, the Linear Mapping Approximation and state-space lumping. Despite the success of these techniques\, they appear to be rather disparate and at present no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimising a well-known information-theoretic quantity between the full model and its reduction\, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimisation approaches. In addition we derive general expressions for the propensities of a reduced system that generalise those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop\, the Michaelis-Menten enzyme system and a genetic oscillator.
URL:https://www.ibs.re.kr/bimag/event/2022-11-04-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221028T140000
DTEND;TZID=Asia/Seoul:20221028T160000
DTSTAMP:20260423T031132
CREATED:20220930T035148Z
LAST-MODIFIED:20221027T083230Z
UID:6646-1666965600-1666972800@www.ibs.re.kr
SUMMARY:Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19
DESCRIPTION:We will discuss about “Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19”\, Cheng\, Jinyu\, et al.\, Briefings in bioinformatics 22.2 (2021): 988-1005. \nAbstract: \nInferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study\, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF\, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression\, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition\, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.
URL:https://www.ibs.re.kr/bimag/event/2022-10-28-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221021T150000
DTEND;TZID=Asia/Seoul:20221021T170000
DTSTAMP:20260423T031132
CREATED:20220930T035045Z
LAST-MODIFIED:20221019T070546Z
UID:6641-1666364400-1666371600@www.ibs.re.kr
SUMMARY:Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level
DESCRIPTION:We will discuss about “Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level”\, David Laloum\, and Marc Robinson-Rechavi\, PLoS computational biology 18.9 (2022): e1010399. \nAbstract: \nMany genes have nycthemeral rhythms of expression\, i.e. a 24-hours periodic variation\, at either mRNA or protein level or both\, and most rhythmic genes are tissue-specific. Here\, we investigate and discuss the evolutionary origins of rhythms in gene expression. Our results suggest that rhythmicity of protein expression could have been favored by selection to minimize costs. Trends are consistent in bacteria\, plants and animals\, and are also supported by tissue-specific patterns in mouse. Unlike for protein level\, cost cannot explain rhythm at the RNA level. We suggest that instead it allows to periodically reduce expression noise. Noise control had the strongest support in mouse\, with limited evidence in other species. We have also found that genes under stronger purifying selection are rhythmically expressed at the mRNA level\, and we propose that this is because they are noise sensitive genes. Finally\, the adaptive role of rhythmic expression is supported by rhythmic genes being highly expressed yet tissue-specific. This provides a good evolutionary explanation for the observation that nycthemeral rhythms are often tissue-specific.
URL:https://www.ibs.re.kr/bimag/event/2022-10-21-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:20220930T150000
DTEND;TZID=Asia/Seoul:20220930T160000
DTSTAMP:20260423T031132
CREATED:20220830T012122Z
LAST-MODIFIED:20220830T012141Z
UID:6531-1664550000-1664553600@www.ibs.re.kr
SUMMARY:Absolute concentration robustness in networks with low-dimensional stoichiometric subspace
DESCRIPTION:We will discuss about “Absolute concentration robustness in networks with low-dimensional stoichiometric subspace”\, Meshkat\, Nicolette\, Anne Shiu\, and Angelica Torres.\, Vietnam Journal of Mathematics 50.3 (2022): 623-651. \nAbstract: \nA reaction system exhibits “absolute concentration robustness” (ACR) in some species if the positive steady-state value of that species does not depend on initial conditions. Mathematically\, this means that the positive part of the variety of the steady-state ideal lies entirely in a hyperplane of the form xi = c\, for some c > 0. Deciding whether a given reaction system – or those arising from some reaction network – exhibits ACR is difficult in general\, but here we show that for many simple networks\, assessing ACR is straightforward. Indeed\, our criteria for ACR can be performed by simply inspecting a network or its standard embedding into Euclidean space. Our main results pertain to networks with many conservation laws\, so that all reactions are parallel to one other. Such “one-dimensional” networks include those networks having only one species. We also consider networks with only two reactions\, and show that ACR is characterized by a well-known criterion of Shinar and Feinberg. Finally\, up to some natural ACR-preserving operations – relabeling species\, lengthening a reaction\, and so on – only three families of networks with two reactions and two species have ACR. Our results are proven using algebraic and combinatorial techniques. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-09-30-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:20220923T150000
DTEND;TZID=Asia/Seoul:20220923T160000
DTSTAMP:20260423T031132
CREATED:20220830T011634Z
LAST-MODIFIED:20220922T011820Z
UID:6529-1663945200-1663948800@www.ibs.re.kr
SUMMARY:Cell clustering for spatial transcriptomics data with graph neural networks
DESCRIPTION:We will discuss about “Cell clustering for spatial transcriptomics data with graph neural networks”\, Li\, J.\, Chen\, S.\, Pan\, X. et al.\, Nat Comput Sci 2\, 399–408 (2022) \nAbstract: \nSpatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficiently. Taking advantage of spatial transcriptomics and graph neural networks\, we introduce cell clustering for spatial transcriptomics data with graph neural networks\, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category annotation. On the basis of its application to five in vitro and in vivo spatial datasets\, we show that cell clustering for spatial transcriptomics outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells. From enhanced sequential fluorescence in situ hybridization data of brain\, cell clustering for spatial transcriptomics finds functional cell subtypes with different micro-environments\, which are all validated experimentally\, inspiring biological hypotheses about the underlying interactions among the cell state\, cell type and micro-environment. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2022-09-23/
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:20220916T110000
DTEND;TZID=Asia/Seoul:20220916T120000
DTSTAMP:20260423T031132
CREATED:20220825T190000Z
LAST-MODIFIED:20220905T053032Z
UID:6351-1663326000-1663329600@www.ibs.re.kr
SUMMARY:Physics-informed neural networks for PDE-constrained optimization and control
DESCRIPTION:We will discuss about “Physics-informed neural networks for PDE-constrained optimization and control”\, Barry-Straume\, Jostein\, et al.\, arXiv preprint arXiv:2205.03377 (2022). \nAbstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control PhysicsInformed Neural Networks simultaneously solve a given system state\, and its respective optimal control\, in a one-stage framework that conforms to physical laws of the system. Prior approaches use a two-stage framework that models and controls a system sequentially\, whereas Control PINNs incorporates the required optimality conditions in its architecture and loss function. The success of Control PINNs is demonstrated by solving the following open-loop optimal control problems: (i) an analytical problem (ii) a one-dimensional heat equation\, and (iii) a two-dimensional predator-prey problem.
URL:https://www.ibs.re.kr/bimag/event/2022-09-16-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:20220902T150000
DTEND;TZID=Asia/Seoul:20220902T160000
DTSTAMP:20260423T031132
CREATED:20220817T042800Z
LAST-MODIFIED:20220828T171528Z
UID:6398-1662130800-1662134400@www.ibs.re.kr
SUMMARY:Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data
DESCRIPTION:We will discuss about “Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data”\, Huang\, Qi\, Journal of The Royal Society Interface 15.139 (2018): 20170885. \nAbstract: Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity\, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates\, based on probabilities of transitions between rest and activity\, that are interpretable and of interest to circadian research.
URL:https://www.ibs.re.kr/bimag/event/2022-09-02-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:20220826T130000
DTEND;TZID=Asia/Seoul:20220826T140000
DTSTAMP:20260423T031132
CREATED:20220825T190000Z
LAST-MODIFIED:20220825T155707Z
UID:6348-1661518800-1661522400@www.ibs.re.kr
SUMMARY:Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
DESCRIPTION:We will discuss about “Inferring Regulatory Networks from Expression Data Using Tree-Based Methods\,” Huynh-Thu et al.\, PLoS ONE (2010). \nAbstract: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data\, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article\, we present GENIE3\, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems\, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes)\, using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data\, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn’t make any assumption about the nature of gene regulation\, can deal with combinatorial and non-linear interactions\, produces directed GRNs\, and is fast and scalable. In conclusion\, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm\, based on feature selection with tree-based ensemble methods\, is simple and generic\, making it adaptable to other types of genomic data and interactions.
URL:https://www.ibs.re.kr/bimag/event/2022-08-26-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:20220812T130000
DTEND;TZID=Asia/Seoul:20220812T140000
DTSTAMP:20260423T031132
CREATED:20220811T190000Z
LAST-MODIFIED:20220728T092951Z
UID:6338-1660309200-1660312800@www.ibs.re.kr
SUMMARY:Molecular convolutional neural networks with DNA regulatory circuits
DESCRIPTION:We will discuss about “Molecular convolutional neural networks with DNA regulatory circuits”\, Pei\, Hao\, et al.\, Nature Machine Intelligence (2022): 1-11. \nAbstract: Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s\, synthetic DNA circuits in liquid phase have been developed as computational hardware to perform neural network-like computations that harness the collective properties of complex biochemical systems. However\, scaling up such DNA-based neural networks to support more powerful computation remains challenging. Here we present a systematic molecular implementation of a convolutional neural network algorithm with synthetic DNA regulatory circuits based on a simple switching gate architecture. Our DNA-based weight-sharing convolutional neural network can simultaneously implement parallel multiply–accumulate operations for 144-bit inputs and recognize patterns in up to eight categories autonomously. Further\, this system can be connected with other DNA circuits to construct hierarchical networks to recognize patterns in up to 32 categories with a two-step approach: coarse classification on language (Arabic numerals\, Chinese oracles\, English alphabets and Greek alphabets) followed by classification into specific handwritten symbols. We also reduced the computation time from hours to minutes by using a simple cyclic freeze–thaw approach. Our DNA-based regulatory circuits are a step towards the realization of a molecular computer with high computing power and the ability to classify complex and noisy information.
URL:https://www.ibs.re.kr/bimag/event/2022-08-12-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:20220805T130000
DTEND;TZID=Asia/Seoul:20220805T140000
DTSTAMP:20260423T031132
CREATED:20220804T190000Z
LAST-MODIFIED:20220729T014246Z
UID:6341-1659704400-1659708000@www.ibs.re.kr
SUMMARY:Neural Ordinary Differential Equations
DESCRIPTION:We will discuss about “Neural Ordinary Differential Equations”\, Chen\, Ricky TQ\, et al.\, Advances in neural information processing systems 31 (2018). \nAbstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers\, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost\, adapt their evaluation strategy to each input\, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows\, a generative model that can train by maximum likelihood\, without partitioning or ordering the data dimensions. For training\, we show how to scalably backpropagate through any ODE solver\, without access to its internal operations. This allows end-to-end training of ODEs within larger models.
URL:https://www.ibs.re.kr/bimag/event/2022-08-05-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:20220729T130000
DTEND;TZID=Asia/Seoul:20220729T140000
DTSTAMP:20260423T031132
CREATED:20220728T190000Z
LAST-MODIFIED:20220728T085252Z
UID:6250-1659099600-1659103200@www.ibs.re.kr
SUMMARY:Learning stable and predictive structures in kinetic systems
DESCRIPTION:We will discuss about “Learning stable and predictive structures in kinetic systems”\, Niklas Pfister \, Stefan Bauer\, and Jonas Peters. PNAS\, 2019 \nAbstract: Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework\, called CausalKinetiX\, that identifies structure from discrete time\, noisy observations\, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying\, invariant kinetic model\, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well-established approaches focusing solely on predictive performance\, especially for out-of-sample generalization.
URL:https://www.ibs.re.kr/bimag/event/2022-07-29-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:20220722T130000
DTEND;TZID=Asia/Seoul:20220722T140000
DTSTAMP:20260423T031132
CREATED:20220629T010032Z
LAST-MODIFIED:20220629T010032Z
UID:6248-1658494800-1658498400@www.ibs.re.kr
SUMMARY:Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells
DESCRIPTION:We will discuss about “Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells”\, Jia\, Chen\, and Ramon Grima\, bioRxiv (2022). \nAbstract: The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA\, and switching of the gene between active and inactive states. While commonly used\, this model does not describe how fluctuations are influenced by the cell cycle phase\, cellular growth and division\, and other crucial aspects of cellular biology. Here we derive the analytical time-dependent solution of a stochastic model that explicitly considers various sources of intrinsic and extrinsic noise: switching between inactive and active states\, doubling of gene copy numbers upon DNA replication\, dependence of the mRNA synthesis rate on cellular volume\, gene dosage compensation\, partitioning of molecules during cell division\, cell-cycle duration variability\, and cell-size control strategies. We show that generally the analytical distribution of transcript numbers in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models\, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak\, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.
URL:https://www.ibs.re.kr/bimag/event/2022-07-22-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:20220708T130000
DTEND;TZID=Asia/Seoul:20220708T140000
DTSTAMP:20260423T031132
CREATED:20220707T190000Z
LAST-MODIFIED:20220629T005506Z
UID:6246-1657285200-1657288800@www.ibs.re.kr
SUMMARY:Chemical Organisation Theory
DESCRIPTION:We will discuss about “Chemical Organisation Theory\n“\, Dittrich\, Peter\, and Pietro Speroni Di Fenizio\, Bulletin of mathematical biology 69.4 (2007): 1199-1231. \nAbstract: Complex dynamical reaction networks consisting of many components that interact and produce each other are difficult to understand\, especially\, when new component types may appear and present component types may vanish completely. Inspired by Fontana and Buss (Bull. Math. Biol.\, 56\, 1–64) we outline a theory to deal with such systems. The theory consists of two parts. The first part introduces the concept of a chemical organisation as a closed and self-maintaining set of components. This concept allows to map a complex (reaction) network to the set of organisations\, providing a new view on the system’s structure. The second part connects dynamics with the set of organisations\, which allows to map a movement of the system in state space to a movement in the set of organisations. The relevancy of our theory is underlined by a theorem that says that given a differential equation describing the chemical dynamics of the network\, then every stationary state is an instance of an organisation. For demonstration\, the theory is applied to a small model of HIV-immune system interaction by Wodarz and Nowak (Proc. Natl. Acad. USA\, 96\, 14464–14469) and to a large model of the sugar metabolism of E. Coli by Puchalka and Kierzek (Biophys. J.\, 86\, 1357–1372). In both cases organisations where uncovered\, which could be related to functions.
URL:https://www.ibs.re.kr/bimag/event/2022-07-08-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:20220705T100000
DTEND;TZID=Asia/Seoul:20220705T110000
DTSTAMP:20260423T031132
CREATED:20220704T160000Z
LAST-MODIFIED:20220704T035619Z
UID:6122-1657015200-1657018800@www.ibs.re.kr
SUMMARY:AI Pontryagin or how artificial neural networks learn to control dynamical systems
DESCRIPTION:We will discuss about “AI Pontryagin or how artificial neural networks learn to control dynamical systems”\, Böttcher\, L.\, Antulov-Fantulin\, N. & Asikis\, T.\, Nat Commun 13\, 333 (2022). \nAbstract: The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems\, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus\, their solution for complex systems is often analytically and computationally intractable. To overcome this outstanding challenge\, we present AI Pontryagin\, a versatile control framework based on neural ordinary differential equations that automatically learns control signals that steer high-dimensional dynamical systems towards a desired target state within a specified time interval. We demonstrate the ability of AI Pontryagin to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that AI Pontryagin is capable of solving a wide range of control and optimization problems\, including those that are analytically intractable
URL:https://www.ibs.re.kr/bimag/event/2022-07-05-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:20220623T123000
DTEND;TZID=Asia/Seoul:20220623T133000
DTSTAMP:20260423T031132
CREATED:20220622T183000Z
LAST-MODIFIED:20220623T060141Z
UID:6104-1655987400-1655991000@www.ibs.re.kr
SUMMARY:Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE\, UMAP\, TriMAP\, and PaCMAP for Data Visualization
DESCRIPTION:We will discuss about “Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE\, UMAP\, TriMAP\, and PaCMAP for Data Visualization”\, Wang\, Yingfan\, et al.\, J. Mach. Learn. Res.\, 2021. \nAbstract: Dimension reduction (DR) techniques such as t-SNE\, UMAP\, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension that has always faced these methods is the trade-off between preservation of global structure and preservation of local structure: these methods can either handle one or the other\, but not both. In this work\, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce. Towards the goal of local structure preservation\, we provide several useful design principles for DR loss functions based on our new understanding of the mechanisms behind successful DR methods. Towards the goal of global structure preservation\, our analysis illuminates that the choice of which components to preserve is important. We leverage these insights to design a new algorithm for DR\, called Pairwise Controlled Manifold Approximation Projection (PaCMAP)\, which preserves both local and global structure. Our work provides several unexpected insights into what design choices both to make and avoid when constructing DR algorithms.
URL:https://www.ibs.re.kr/bimag/event/2022-06-23-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:20220616T130000
DTEND;TZID=Asia/Seoul:20220616T140000
DTSTAMP:20260423T031132
CREATED:20220615T190000Z
LAST-MODIFIED:20220623T060231Z
UID:6124-1655384400-1655388000@www.ibs.re.kr
SUMMARY:Identifying the critical states of complex diseases by the dynamic change of multivariate distribution
DESCRIPTION:We will discuss about “Identifying the critical states of complex diseases by the dynamic change of multivariate distribution”\, Peng\, Hao\, et al.\, Briefings in Bioinformatics\, 2022. \nAbstract: The dynamics of complex diseases are not always smooth; they are occasionally abrupt\, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements\, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study\, we propose a computational method\, the direct interaction network-based divergence\, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets\, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.
URL:https://www.ibs.re.kr/bimag/event/2022-06-16-jc-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220603T130000
DTEND;TZID=Asia/Seoul:20220603T140000
DTSTAMP:20260423T031132
CREATED:20220525T170000Z
LAST-MODIFIED:20220529T181413Z
UID:5986-1654261200-1654264800@www.ibs.re.kr
SUMMARY:Approximating Solutions of the Chemical Master Equation using Neural Networks
DESCRIPTION:We will discuss about “Approximating Solutions of the Chemical Master Equation using Neural Networks”\, Sukys et al.\, bioRxiv\, 2022 \nAbstract: The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions\, but it cannot be solved analytically for most systems of practical interest. While Monte Carlo methods provide a principled means to probe the system dy- namics\, their high computational cost can render the estimation of molecule number distributions and other numerical tasks infeasible due to the large number of repeated simulations typically required. In this paper we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training a neural network to learn the distributions predicted by the CME from a relatively small number of stochastic simulations\, thereby accelerating computationally intensive tasks such as parameter exploration and inference. We show on biologically relevant examples that simple neural networks with one hidden layer are able to cap- ture highly complex distributions across parameter space. We provide a detailed discussion of the neural network implementation and code for easy reproducibility.
URL:https://www.ibs.re.kr/bimag/event/2022-06-03-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:20220512T150000
DTEND;TZID=Asia/Seoul:20220512T160000
DTSTAMP:20260423T031132
CREATED:20220511T210000Z
LAST-MODIFIED:20220509T084329Z
UID:5983-1652367600-1652371200@www.ibs.re.kr
SUMMARY:Optimizing Oscillators for Specific Tasks Predicts Preferred Biochemical Implementations
DESCRIPTION:We will discuss about “Optimizing Oscillators for Specific Tasks Predicts Preferred Biochemical Implementations”\, Agrahar and  Rust.\, bioRxiv\, 2022. \nAbstract: Oscillatory processes are used throughout cell biology to control time-varying physiology including the cell cycle\, circadian rhythms\, and developmental patterning. It has long been understood that free-running oscillations require feedback loops where the activity of one component depends on the concentration of another. Oscillator motifs have been classified by the positive or negative net logic of these loops. However\, each feedback loop can be implemented by regulation of either the production step or the removal step. These possibilities are not equivalent because of the underlying structure of biochemical kinetics. By computationally searching over these possibilities\, we find that certain molecular implementations are much more likely to produce stable oscillations. These preferred molecular implementations are found in many natural systems\, but not typically in artificial oscillators\, suggesting a design principle for future synthetic biology. Finally\, we develop an approach to oscillator function across different reaction networks by evaluating the biosynthetic cost needed to achieve a given phase coherence. This analysis predicts that phase drift is most efficiently suppressed by delayed negative feedback lo op architectures that operate without positive feedback.
URL:https://www.ibs.re.kr/bimag/event/2022-05-12-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
END:VCALENDAR