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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211215T143000
DTEND;TZID=Asia/Seoul:20211215T160000
DTSTAMP:20260423T082902
CREATED:20211214T190000Z
LAST-MODIFIED:20211214T070933Z
UID:5299-1639578600-1639584000@www.ibs.re.kr
SUMMARY:Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
DESCRIPTION:We will discuss about “Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics”\, Ji et al.\, The Journal of Physical Chemistry A\, 2020 \nThe recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not only conforms to the measurements and initial and boundary conditions but also satisfies the governing equations. This work first investigates the performance of the PINN in solving stiff chemical kinetic problems with governing equations of stiff ordinary differential equations (ODEs). The results elucidate the challenges of utilizing the PINN in stiff ODE systems. Consequently\, we employ quasi-steady-state assumption (QSSA) to reduce the stiffness of the ODE systems\, and the PINN then can be successfully applied to the converted non-/mild-stiff systems. Therefore\, the results suggest that stiffness could be the major reason for the failure of the regular PINN in the studied stiff chemical kinetic systems. The developed stiff-PINN approach that utilizes QSSA to enable the PINN to solve stiff chemical kinetics shall open the possibility of applying the PINN to various reaction-diffusion systems involving stiff dynamics.
URL:https://www.ibs.re.kr/bimag/event/2021-12-15/
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:20211126T100000
DTEND;TZID=Asia/Seoul:20211126T110000
DTSTAMP:20260423T082902
CREATED:20211124T190000Z
LAST-MODIFIED:20211122T014405Z
UID:5190-1637920800-1637924400@www.ibs.re.kr
SUMMARY:A Random Matrix Theory Approach to Denoise Single-Cell Data
DESCRIPTION:We will discuss about “A Random Matrix Theory Approach to Denoise Single-Cell Data”\, Aparicio et al.\, Patterns\, 2020 \nSingle-cell technologies provide the opportunity to identify new cellular states. However\, a major obstacle to the identification of biological signals is noise in single-cell data. In addition\, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition\, we explain how sparsity can cause spurious eigenvector localization\, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory\, about 3% is spurious signal induced by sparsity\, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations.
URL:https://www.ibs.re.kr/bimag/event/a-random-matrix-theory-approach-to-denoise-single-cell-data/
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:20211118T130000
DTEND;TZID=Asia/Seoul:20211118T140000
DTSTAMP:20260423T082902
CREATED:20211117T190000Z
LAST-MODIFIED:20211101T080821Z
UID:5187-1637240400-1637244000@www.ibs.re.kr
SUMMARY:Solving Singular Control Problems in Mathematical Biology\, Using PASA
DESCRIPTION:We will discuss about “Solving Singular Control Problems in Mathematical Biology\, Using PASA”\, Atkins et al.\, arXiv\, 2020 \nIn this paper\, we will demonstrate how to use a nonlinear polyhedral constrained optimization solver called the Polyhedral Active Set Algorithm (PASA) for solving a general singular control problem. We present methods of discretizing a general optimal control problem that involves the use of the gradient of the Lagrangian for computing the gradient of the cost functional so that PASA can be applied. When a numerical solution contains artifacts that resemble “chattering”\, a phenomenon where the control oscillates wildly along the singular region\, we recommend a method of regularizing the singular control problem by adding a term to the cost functional that measures a scalar multiple of the total variation of the control\, where the scalar is viewed as a tuning parameter. We then demonstrate PASA’s performance on three singular control problems that give rise to different applications of mathematical biology. We also provide some exposition on the heuristics that we use in determining an appropriate size for the tuning parameter.
URL:https://www.ibs.re.kr/bimag/event/2021-11-18-2/
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:20211112T110000
DTEND;TZID=Asia/Seoul:20211112T120000
DTSTAMP:20260423T082902
CREATED:20211111T170000Z
LAST-MODIFIED:20211111T084242Z
UID:5185-1636714800-1636718400@www.ibs.re.kr
SUMMARY:Detecting and quantifying causal associations in large nonlinear time series datasets
DESCRIPTION:We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”\, Runge et al.\, Science Advances\, 2019 \nIdentifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here\, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power\, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.
URL:https://www.ibs.re.kr/bimag/event/2021-11-12-2/
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:20211022T130000
DTEND;TZID=Asia/Seoul:20211022T140000
DTSTAMP:20260423T082902
CREATED:20211021T190000Z
LAST-MODIFIED:20211001T062513Z
UID:5061-1634907600-1634911200@www.ibs.re.kr
SUMMARY:Filtering and inference for stochastic oscillators with distributed delays
DESCRIPTION:We will discuss about “Filtering and inference for stochastic oscillators with distributed delays”\, Calderazzo et al.\, Bioinformatics\, 2018 at the Journal Club \n\n\n\n\nMotivation\nThe time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data\, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model\, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here. \n\n\nResults\nWe develop a novel filtering approach for the LNA in stochastic systems with distributed delays\, which allows the parameter values and unobserved states of a stochastic negative feedback model to be inferred from univariate time-series data. The performance of the methods is tested for simulated data. Results are obtained for real data when the model is fitted to imaging data on Cry1\, a key gene involved in the mammalian central circadian clock\, observed via a luciferase reporter construct in a mouse suprachiasmatic nucleus. \n\n\nAvailability and implementation\nProgrammes are written in MATLAB and Statistics Toolbox Release 2016 b\, The MathWorks\, Inc.\, Natick\, Massachusetts\, USA. Sample code and Cry1 data are available on GitHub https://github.com/scalderazzo/FLNADD.
URL:https://www.ibs.re.kr/bimag/event/2021-10-22-2/
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:20211008T140000
DTEND;TZID=Asia/Seoul:20211008T150000
DTSTAMP:20260423T082902
CREATED:20211007T190000Z
LAST-MODIFIED:20211006T081805Z
UID:4912-1633701600-1633705200@www.ibs.re.kr
SUMMARY:Balanced truncation for model reduction of biological oscillators
DESCRIPTION:We will discuss about “Balanced truncation for model reduction of biological oscillators”\, Padoan et al.\, Biological Cybernetics\, 2021 \nModel reduction is a central problem in mathematical biology. Reduced order models enable modeling of a biological system at different levels of complexity and the quantitative analysis of its properties\, like sensitivity to parameter variations and resilience to exogenous perturbations. However\, available model reduction methods often fail to capture a diverse range of nonlinear behaviors observed in biology\, such as multistability and limit cycle oscillations. The paper addresses this need using differential analysis. This approach leads to a nonlinear enhancement of classical balanced truncation for biological systems whose behavior is not restricted to the stability of a single equilibrium. Numerical results suggest that the proposed framework may be relevant to the approximation of classical models of biological systems.
URL:https://www.ibs.re.kr/bimag/event/2021-10-8/
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:20210924T130000
DTEND;TZID=Asia/Seoul:20210924T140000
DTSTAMP:20260423T082902
CREATED:20210922T190000Z
LAST-MODIFIED:20210831T052818Z
UID:4910-1632488400-1632492000@www.ibs.re.kr
SUMMARY:A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells
DESCRIPTION:We will discuss about “A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells”\, Unosson et al.\, bioRxiv\, 2021 \nWe propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatiotemporal gene expression in intact networks of cells. The model describes the oscillating time evolution of molecular mRNA counts through a negative transcriptional-translational feedback loop encoded in a chemical Langevin equation with a probabilistic delay distribution. The model is extended spatially by means of a multiplicative random effects model with a first order Markov random field prior distribution. Our methodology effectively separates intrinsic molecular noise\, measurement noise\, and extrinsic noise and phenotypic variation driving cell heterogeneity\, while being amenable to parameter identification and inference. Based on the single-cell model we propose a novel computational stability analysis that allows us to infer two key characteristics\, namely the robustness of the oscillations\, i.e. whether the reaction network exhibits sustained or damped oscillations\, and the profile of the regulation\, i.e. whether the inhibition occurs over time in a more distributed versus a more direct manner\, which affects the cells’ ability to phase-shift to new schedules. We show how insight into the spatio-temporal characteristics of the circadian feedback loop in the suprachiasmatic nucleus (SCN) can be gained by applying the methodology to bioluminescence-reported expression of the circadian core clock gene Cry1 across mouse SCN tissue. We find that while (almost) all SCN neurons exhibit robust cell-autonomous oscillations\, the parameters that are associated with the regulatory transcription profile give rise to a spatial division of the tissue between the central region whose oscillations are resilient to perturbation in the sense that they maintain a high degree of synchronicity\, and the dorsal region which appears to phase shift in a more diversified way as a response to large perturbations and thus could be more amenable to entrainment.
URL:https://www.ibs.re.kr/bimag/event/2021-09-24/
LOCATION:B305 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:20210917T130000
DTEND;TZID=Asia/Seoul:20210917T140000
DTSTAMP:20260423T082902
CREATED:20210915T190000Z
LAST-MODIFIED:20210831T052758Z
UID:4908-1631883600-1631887200@www.ibs.re.kr
SUMMARY:The Oscillation Amplitude\, Not the Frequency of Cytosolic Calcium\, Regulates Apoptosis Induction
DESCRIPTION:We will discuss about “The Oscillation Amplitude\, Not the Frequency of Cytosolic Calcium\, Regulates Apoptosis Induction ”\, Qi et al.\, iScience\, 2020 \nAbstract: \nAlthough a rising concentration of cytosolic Ca2+ has long been recognized as an essential signal for apoptosis\, the dynamical mechanisms by which Ca2+ regulates apoptosis are not clear yet. To address this\, we constructed a computational model that integrates known biochemical reactions and can reproduce the dynamical behaviors of Ca2+-induced apoptosis as observed in experiments. Model analysis shows that oscillating Ca2+ signals first convert into gradual signals and eventually transform into a switch-like apoptotic response. Via the two processes\, the apoptotic signaling pathway filters the frequency of Ca2+ oscillations effectively but instead responds acutely to their amplitude. Collectively\, our results suggest that Ca2+ regulates apoptosis mainly via oscillation amplitude\, rather than frequency\, modulation. This study not only provides a comprehensive understanding of how oscillatory Ca2+ dynamically regulates the complex apoptotic signaling network but also presents a typical example of how Ca2+ controls cellular responses through amplitude modulation.
URL:https://www.ibs.re.kr/bimag/event/2021-09-17/
LOCATION:B305 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:20210909T090000
DTEND;TZID=Asia/Seoul:20210909T100000
DTSTAMP:20260423T082902
CREATED:20210908T190000Z
LAST-MODIFIED:20210903T055048Z
UID:4906-1631178000-1631181600@www.ibs.re.kr
SUMMARY:Nonlinear delay differential equations and their application to modeling biological network motifs
DESCRIPTION:We will discuss about “Nonlinear delay differential equations and their application to modeling biological network motifs”\, Glass et al.\, Nature Communications\, 2021 \nAbstract: \nBiological regulatory systems\, such as cell signaling networks\, nervous systems and ecological webs\, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However\, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models\, both analytically and numerically. We find many broadly applicable results\, including parameter reduction versus canonical ordinary differential equation (ODE) models\, analytical relations for converting between ODE and DDE models\, criteria for when delays may be ignored\, a complete phase space for autoregulation\, universal behaviors of feedforward loops\, a unified Hill-function logic framework\, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs.
URL:https://www.ibs.re.kr/bimag/event/2021-09-09/
LOCATION:B305 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:20210902T130000
DTEND;TZID=Asia/Seoul:20210902T140000
DTSTAMP:20260423T082902
CREATED:20210902T190000Z
LAST-MODIFIED:20210831T052727Z
UID:4841-1630587600-1630591200@www.ibs.re.kr
SUMMARY:Machine learning of stochastic gene network phenotypes
DESCRIPTION:We will discuss about “Machine learning of stochastic gene network phenotypes”\, Park et al.\, bioRxiv\, 2019 \nAbstract: \nA recurrent challenge in biology is the development of predictive quantitative models because most molecular and cellular parameters have unknown values and realistic models are analytically intractable. While the dynamics of the system can be analyzed via computer simulations\, substantial computational resources are often required given uncertain parameter values resulting in large numbers of parameter combinations\, especially when realistic biological features are included. Simulation alone also often does not yield the kinds of intuitive insights from analytical solutions. Here we introduce a general framework combining stochastic/mechanistic simulation of reaction systems and machine learning of the simulation data to generate computationally efficient predictive models and interpretable parameter-phenotype maps. We applied our approach to investigate stochastic gene expression propagation in biological networks\, which is a contemporary challenge in the quantitative modeling of single-cell heterogeneity. We found that accurate\, predictive machine-learning models of stochastic simulation results can be constructed. Even in the simplest networks existing analytical schemes generated significantly less accurate predictions than our approach\, which revealed interesting insights when applied to more complex circuits\, including the extensive tunability of information propagation enabled by feedforward circuits and how even single negative feedbacks can utilize stochastic fluctuations to generate robust oscillations. Our approach is applicable beyond biology and opens up a new avenue for exploring complex dynamical systems.
URL:https://www.ibs.re.kr/bimag/event/2021-09-02-2/
LOCATION:B305 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:20210819T130000
DTEND;TZID=Asia/Seoul:20210819T140000
DTSTAMP:20260423T082902
CREATED:20210819T190000Z
LAST-MODIFIED:20210812T095509Z
UID:4839-1629378000-1629381600@www.ibs.re.kr
SUMMARY:Cellular signaling beyond the Wiener-Kolmogorov limit
DESCRIPTION:We will discuss about “Cellular signaling beyond the Wiener-Kolmogorov limit”\, Weisenberger et al.\, bioRxiv\, 2021 \nAbstract: \nAccurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory\, originally developed for engineering problems\, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However\, WK theory has one assumption that potentially limits its applicability: it relies on a linear\, continuum description of the reaction dynamics. Despite this\, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence the bound being broken. To accomplish this\, we develop a theoretical framework for multi-level signaling cascades\, including the possibility of feedback interactions between input and output. In the absence of feedback\, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback\, we rely on numerical solutions of the system’s master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However the magnitude of the violation is biologically negligible\, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds\, its predictions for performance limits are excellent approximations\, even for nonlinear systems. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-08-19/
LOCATION:B305 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:20210813T090000
DTEND;TZID=Asia/Seoul:20210813T100000
DTSTAMP:20260423T082902
CREATED:20210810T220000Z
LAST-MODIFIED:20210811T064522Z
UID:4837-1628845200-1628848800@www.ibs.re.kr
SUMMARY:TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data
DESCRIPTION:We will discuss about “TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data”\, Ness-Cohn and Braun\, Bioinformatics\, 2021 \nAbstract \nMotivation: The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics\, coupled with the significant implicatins of the circadian clock for human health\, has sparked an interest in circadian profiling studies to discover genes under circadian control.\nResult: We present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series\, the method reconstructs the state space using time-delay embedding\, a data transformation technique from dynamical systems theory. In the embedded space\, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology\, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution\, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes\, number of replicates\, and missing data. Comparison to competing methods highlights their relative strengths\, providing guidance as to the optimal choice of cycling detection method.\nAvailability: A fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/ .
URL:https://www.ibs.re.kr/bimag/event/2021-08-13-2/
LOCATION:B305 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:20210806T130000
DTEND;TZID=Asia/Seoul:20210806T140000
DTSTAMP:20260423T082902
CREATED:20210801T140652Z
LAST-MODIFIED:20210801T140652Z
UID:4835-1628254800-1628258400@www.ibs.re.kr
SUMMARY:Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation
DESCRIPTION:We will discuss about “Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation”\, Li et. al.\, Cell Systems\, 2018 \nAbstract \nGene regulation is a complex non-equilibrium process. Here\, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive\, active\, and refractory) provides a parsimonious framework for analyzing gene regulation. Our theory makes two non-intuitive predictions. First\, for transcription factors (TFs) that regulate transcription burst frequency\, as opposed to amplitude or duration\, weak TF binding is sufficient to elicit strong transcriptional responses. Second\, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural\, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further\, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates\, not different TF occupancy\, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total\, our work demonstrates the relevance of a kinetic\, non-equilibrium framework for understanding transcriptional regulation.
URL:https://www.ibs.re.kr/bimag/event/2021-08-06/
LOCATION:B305 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:20210730T130000
DTEND;TZID=Asia/Seoul:20210730T140000
DTSTAMP:20260423T082902
CREATED:20210726T125859Z
LAST-MODIFIED:20210726T125859Z
UID:4785-1627650000-1627653600@www.ibs.re.kr
SUMMARY:Stochastic reaction networks in dynamic compartment populations
DESCRIPTION:We will discuss about “Stochastic reaction networks in dynamic compartment populations”\, Duso and Zechner\, PNAS\, 2020 \nAbstract: Compartmentalization of biochemical processes underlies all biological systems\, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse\, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems\, but a general and sufficiently effective approach remains lacking. In this work\, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes\, including subcellular compartmentalization and tissue homeostasis.
URL:https://www.ibs.re.kr/bimag/event/2021-07-30/
LOCATION:B305 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:20210722T130000
DTEND;TZID=Asia/Seoul:20210722T140000
DTSTAMP:20260423T082902
CREATED:20210721T190000Z
LAST-MODIFIED:20210726T125353Z
UID:4754-1626958800-1626962400@www.ibs.re.kr
SUMMARY:Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter
DESCRIPTION:We will discuss about “Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter”\, Bonarius et. al.\, IEEE Trans. Biomed. Eng.\, 2021 \nAbstract \nObjective: In the near future\, real-time estimation of peoples unique\, precise circadian clock state has the potential to improve the efficacy of medical treatments and improve human performance on a broad scale. Humancentric lighting can bring circadian-rhythm support using biodynamic lighting solutions that sync light with the time of day. We investigate a method to improve the tracking of individual’s circadian processes. Methods: In literature\, the human circadian physiology has been mathematically modeled using ordinary differential equations\, the state of which can be tracked via the signal processing concept of a Particle Filter. We show that substantial improvements can be made if the estimator not only tracks state variables\, such as the phase and amplitude of the circadian pacemaker\, but also fits specific model parameters to the individual. In particular\, we optimize model parameter τx\, which reflects the intrinsic period of the circadian pacemaker (τ). We show that both state and model parameters can be estimated based on minimally-invasive light exposure measurements and sleep-wake state observations. We also quantify the effect of inaccuracies in sensing. Results: We demonstrate improved performance by estimating τx for every individual\, both with artificially created and human subject data. Prediction accuracy improves with every newly available observation. The estimated τx-s correlate well with the subjects’ chronotypes\, in a similar way as τ correlates. Conclusion: Our results show that individualizing the estimation of model parameters can improve circadian state estimation accuracy. Significance: These findings underscore the potential improvements in personalized models over one-size fits all approaches.
URL:https://www.ibs.re.kr/bimag/event/2021-07-22/
LOCATION:B305 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:20210715T130000
DTEND;TZID=Asia/Seoul:20210715T140000
DTSTAMP:20260423T082902
CREATED:20210713T071946Z
LAST-MODIFIED:20210715T002734Z
UID:4721-1626354000-1626357600@www.ibs.re.kr
SUMMARY:Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions
DESCRIPTION:We will discuss about “Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions”\, Thurley et al.\, Cell Systems\, 2021 \nAbstract: \nCell-to-cell communication networks have critical roles in coordinating diverse organismal processes\, such as tissue development or immune cell response. However\, compared with intracellular signal transduction networks\, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here\, we make use of a framework that treats intracellular signal transduction networks as “black boxes” with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time\, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.
URL:https://www.ibs.re.kr/bimag/event/2021-07-15/
LOCATION:B305 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:20210709T130000
DTEND;TZID=Asia/Seoul:20210709T140000
DTSTAMP:20260423T082902
CREATED:20210705T061640Z
LAST-MODIFIED:20210705T131643Z
UID:4710-1625835600-1625839200@www.ibs.re.kr
SUMMARY:DeepCME: A deep learning framework for solving the Chemical Master Equation
DESCRIPTION:We will discuss about “DeepCME: A deep learning framework for solving the Chemical Master Equation\,” Gupta et al.\, bioRxiv\, 2021 \nStochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models\, the Kolmogorov’s forward equation is called the chemical master equation (CME)\, and it is a fundamental system of linear ordinary differential equations (ODEs) that describes the evolution of the probability distribution of the random state-vector representing the copy-numbers of all the reacting species. The size of this system is given by the number of states that are accessible by the chemical system\, and for most examples of interest this number is either very large or infinite. Moreover\, approximations that reduce the size of the system by retaining only a finite number of important chemical states (e.g. those with non-negligible probability) result in high-dimensional ODE systems\, even when the number of reacting species is small. Consequently\, accurate numerical solution of the CME is very challenging\, despite the linear nature of the underlying ODEs. One often resorts to estimating the solutions via computationally intensive stochastic simulations. The goal of the present paper is to develop a novel deep-learning approach for solving high-dimensional CMEs by reformulating the stochastic dynamics using Kolmogorov’s backward equation. The proposed method leverages superior approximation properties of Deep Neural Networks (DNNs) and is algorithmically based on reinforcement learning. It only requires a moderate number of stochastic simulations (in comparison to typical simulation-based approaches) to train the “policy function”. This allows not just the numerical approximation of the CME solution but also of its sensitivities to all the reaction network parameters (e.g. rate constants). We provide four examples to illustrate our methodology and provide several directions for future research.
URL:https://www.ibs.re.kr/bimag/event/2021-07-09/
LOCATION:B305 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:20210702T120000
DTEND;TZID=Asia/Seoul:20210702T130000
DTSTAMP:20260423T082902
CREATED:20210507T124154Z
LAST-MODIFIED:20210622T234621Z
UID:4550-1625227200-1625230800@www.ibs.re.kr
SUMMARY:Collective Oscillations in coupled cell systems
DESCRIPTION:We will discuss about “Collective Oscillations in coupled cell systems”\, Chen and Sinh\, Bulletin of Mathematical Biology\, 2021 \nWe investigate oscillations in coupled systems. The methodology is based on the Hopf bifurcation theorem and a condition extended from the Routh–Hurwitz criterion. Such a condition leads to locating the bifurcation values of the parameters. With such an approach\, we analyze a single-cell system modeling the minimal genetic negative feedback loop and the coupled-cell system composed by these single-cell systems. We study the oscillatory properties for these systems and compare these properties between the model with Hill-type repression and the one with protein-sequestration-based repression. As the parameters move from the Hopf bifurcation value for single cells to the one for coupled cells\, we compute the eigenvalues of the linearized systems to obtain the magnitude of the collective frequency when the periodic solution of the coupled-cell system is generated. Extending from this information on the parameter values\, we further compute and compare the collective frequency for the coupled-cell system and the average frequency of the decoupled individual cells. To compare these scenarios with other biological oscillators\, we perform parallel analysis and computations on a segmentation clock model.
URL:https://www.ibs.re.kr/bimag/event/2021-07-02/
LOCATION:B305 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:20210611T123000
DTEND;TZID=Asia/Seoul:20210611T133000
DTSTAMP:20260423T082902
CREATED:20210507T123416Z
LAST-MODIFIED:20210601T035036Z
UID:4545-1623414600-1623418200@www.ibs.re.kr
SUMMARY:DNA as a universal substrate for chemical kinetics
DESCRIPTION:We will discuss about “DNA as a universal substrate for chemical kinetics “\, Soloveichik et al.\, PNAS (2009) \nMolecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive\, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously\, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator\, a limit-cycle oscillator\, a chaotic system\, and systems implementing feedback digital logic and algorithmic behavior.
URL:https://www.ibs.re.kr/bimag/event/2021-05-27/
LOCATION:B305 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:20210520T123000
DTEND;TZID=Asia/Seoul:20210520T133000
DTSTAMP:20260423T082902
CREATED:20210507T123654Z
LAST-MODIFIED:20210507T123746Z
UID:4547-1621513800-1621517400@www.ibs.re.kr
SUMMARY:Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes
DESCRIPTION:We will discuss about “Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes”\, Hempel et. al.\, bioRxiv\, 2021 \nIn order to advance the mission of in silico cell biology\, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success\, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes\, the number of independent or weakly coupled subsystems increases\, and the number of global system states increase exponentially\, making the sampling of all distinct global states unfeasible. In this work\, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology\, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.
URL:https://www.ibs.re.kr/bimag/event/2021-05-20/
LOCATION:B305 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:20210514T110000
DTEND;TZID=Asia/Seoul:20210514T120000
DTSTAMP:20260423T082902
CREATED:20210507T124508Z
LAST-MODIFIED:20210507T124508Z
UID:4555-1620990000-1620993600@www.ibs.re.kr
SUMMARY:Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model
DESCRIPTION:We will discuss about “Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model”\, Ito et. al.\, PloS ONE\, 2011 \nTransfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive\, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic\, as synaptic delays between cortical neurons\, for example\, range from one to tens of milliseconds. In addition\, neurons produce bursts of spikes spanning multiple time bins. To address these issues\, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance\, we applied these extensions of TE to a spiking cortical network model (Izhikevich\, 2006) with known connectivity and a range of synaptic delays. For comparison\, we also investigated single-delay TE\, at a message length of one bin (D1TE)\, and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE\, this dramatically improved to 73% of true connections. In addition\, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE\, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE\, when used on currently available desktop computers\, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
URL:https://www.ibs.re.kr/bimag/event/2021-05-14/
LOCATION:B305 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:20210507T123000
DTEND;TZID=Asia/Seoul:20210507T133000
DTSTAMP:20260423T082902
CREATED:20210503T075749Z
LAST-MODIFIED:20210503T075749Z
UID:4525-1620390600-1620394200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 2
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nThe problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics\, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors. However\, if the underlying graphical model is too complex or the data is in very high dimensions\, then such sampling-based methodologies run into several problems. Variational inference (Jordan et al.\, 1999; Wainwright and Jordan\, 2008) is a family of machine learning methodologies that transforms the problem of approximating posterior densities to an optimization\, which lets us circumvent all such problems. In the first part\, I will introduce the general framework of variational inference and some underlying theory\, accompanied by an illustrative example of LDA (Blei et al.\, 2003). In the second part\, I will introduce some recent works on applying variational inference to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-2/
LOCATION:B305 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:20210429T120000
DTEND;TZID=Asia/Seoul:20210429T130000
DTSTAMP:20260423T082902
CREATED:20210425T180554Z
LAST-MODIFIED:20210425T180554Z
UID:4499-1619697600-1619701200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 1
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nGaussian process(GP) is a stochastic process such that the joint distribution of an arbitrary finite subset of the random variables is a multivariate normal. It plays a fundamental role in Bayesian machine learning as it can be interpreted as a prior over functions (Rasmussen and Williams\, 2006)\, hence providing a nonparametric approach to various tasks. In the first part\, I will introduce the general framework of GP and some underlying theory\, accompanied by an illustrative example of GP regression\, also known as Kringing. In the second part\, I will introduce some recent works on applying GP to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-1/
LOCATION:B305 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:20210422T120000
DTEND;TZID=Asia/Seoul:20210422T130000
DTSTAMP:20260423T082902
CREATED:20210417T101617Z
LAST-MODIFIED:20210419T021327Z
UID:4477-1619092800-1619096400@www.ibs.re.kr
SUMMARY:A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics
DESCRIPTION:We will discuss about “A Simple and Flexible Computational Framework for\nInferring Sources of Heterogeneity from Single-Cell\nDynamics”\, Dharmarajan et al.\, Cell Systems (2019) \nSingle-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability\, a key step for understanding heterogeneity in cell populations. However\, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple\, flexible\, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks\, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics
URL:https://www.ibs.re.kr/bimag/event/2021-04-22/
LOCATION:B305 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:20210416T120000
DTEND;TZID=Asia/Seoul:20210416T130000
DTSTAMP:20260423T082902
CREATED:20210412T110458Z
LAST-MODIFIED:20210412T110458Z
UID:4423-1618574400-1618578000@www.ibs.re.kr
SUMMARY:Synthetic multistability in mammalian cells
DESCRIPTION:We will discuss about “Synthetic multistability in mammalian cells”\, Zhu et al.\, bioRxiv (2021) \nIn multicellular organisms\, gene regulatory circuits generate thousands of molecularly distinct\, mitotically heritable states\, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require interactions among cells in distinct states. Here we introduce MultiFate\, a naturally-inspired\, synthetic circuit that supports long-term\, controllable\, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design\, we engineered MultiFate circuits that generate up to seven states\, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs\, and can be easily expanded with additional transcription factors. Together\, these results provide a foundation for engineering multicellular behaviors in mammalian cells. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-16/
LOCATION:B305 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:20210409T120000
DTEND;TZID=Asia/Seoul:20210409T130000
DTSTAMP:20260423T082902
CREATED:20210323T105030Z
LAST-MODIFIED:20210407T041048Z
UID:4304-1617969600-1617973200@www.ibs.re.kr
SUMMARY:Highly accurate fluorogenic DNA sequencing with information theory–based error correction
DESCRIPTION:We will discuss about “Highly accurate fluorogenic DNA sequencing with information theory–based error correction”\, Chen et al.\, Nature Biotechnology (2017) \nEliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing\, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory–based error-correction algorithm. ECC embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences\, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that\, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%\, ECC sequencing provides single-end\, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-09/
LOCATION:B305 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:20210401T120000
DTEND;TZID=Asia/Seoul:20210401T130000
DTSTAMP:20260423T082902
CREATED:20210331T003338Z
LAST-MODIFIED:20210406T075108Z
UID:4352-1617278400-1617282000@www.ibs.re.kr
SUMMARY:Yun Min Song\, A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light
DESCRIPTION:We will discuss about “A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light”\, Kumpost et al.\, bioRxiv (2021) \nThe circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock\, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light-dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable\, thus providing an attractive laboratory model for circadian entrainment. Here\, we have developed a stochastic oscillator model of the zebrafish circadian clock\, which accounts for the core clock negative feedback loop\, light input\, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore\, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based\, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function.
URL:https://www.ibs.re.kr/bimag/event/2021-04-02/
LOCATION:B305 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:20210319T113000
DTEND;TZID=Asia/Seoul:20210319T130000
DTSTAMP:20260423T082902
CREATED:20210312T062049Z
LAST-MODIFIED:20210406T075219Z
UID:4254-1616153400-1616158800@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Unified rational protein engineering with sequence-based deep representation learning
DESCRIPTION:In this presentation\, we are going to discuss the paper\, “Unified rational protein engineering with sequence-based deep representation learning” \nAbstract\nRational protein engineering requires a holistic understanding of protein function. Here\, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally\, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins\, and the quantitative function of molecularly diverse mutants\, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
URL:https://www.ibs.re.kr/bimag/event/2021-03-19/
LOCATION:Tea Room\, IBS\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210312T113000
DTEND;TZID=Asia/Seoul:20210312T130000
DTSTAMP:20260423T082902
CREATED:20210305T084406Z
LAST-MODIFIED:20210406T075224Z
UID:4227-1615548600-1615554000@www.ibs.re.kr
SUMMARY:Dae Wook Kim\, Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks
DESCRIPTION:We will discuss about “Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks”\, Dixit et al.\, Cell Systems (2020) \nPredictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However\, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here\, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-03-12/
LOCATION:Tea Room\, IBS\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210305T130000
DTEND;TZID=Asia/Seoul:20210305T140000
DTSTAMP:20260423T082902
CREATED:20210228T074756Z
LAST-MODIFIED:20210406T075234Z
UID:4157-1614949200-1614952800@www.ibs.re.kr
SUMMARY:Eui Min Jeong\, Pairing of segmentation clock genes drives robust pattern formation
DESCRIPTION:We will discuss about “Pairing of segmentation clock genes drives robust pattern formation”\, Zinani et al.\, Nature (2021) \nGene expression is an inherently stochastic process; however\, organismal development and homeostasis require cells to coordinate the spatiotemporal expression of large sets of genes. In metazoans\, pairs of co-expressed genes often reside in the same chromosomal neighbourhood\, with gene pairs representing 10 to 50% of all genes\, depending on the species. Because shared upstream regulators can ensure correlated gene expression\, the selective advantage of maintaining adjacent gene pairs remains unknown6. Here\, using two linked zebrafish segmentation clock genes\, her1 and her7\, and combining single-cell transcript counting\, genetic engineering\, real-time imaging and computational modelling\, we show that gene pairing boosts correlated transcription and provides phenotypic robustness for the formation of developmental patterns. Our results demonstrate that the prevention of gene pairing disrupts oscillations and segmentation\, and the linkage of her1 and her7 is essential for the development of the body axis in zebrafish embryos. We predict that gene pairing may be similarly advantageous in other organisms\, and our findings could lead to the engineering of precise synthetic clocks in embryos and organoids \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-03-05/
LOCATION:Tea Room\, IBS\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR