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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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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220512T150000
DTEND;TZID=Asia/Seoul:20220512T160000
DTSTAMP:20220509T084329Z
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220506T130000
DTEND;TZID=Asia/Seoul:20220506T140000
DTSTAMP:20220425T061007Z
CREATED:20220505T190000Z
LAST-MODIFIED:20220425T061007Z
UID:5980-1651842000-1651845600@www.ibs.re.kr
SUMMARY:The 103\,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes
DESCRIPTION:We will discuss about “The 103\,200-arm acceleration dataset in the UK Biobank revealed a landscape of human sleep phenotypes”\, Katori et al.\, PNAS\, 2022. \nAbstract: Human sleep phenotypes can be defined and diversified by both genetic and environmental factors. However\, some sleep phenotypes are difficult to evaluate without long-term\, precise sleep monitoring\, for which simple yet accurate sleep measurement is required. To solve this problem\, we recently developed a state-of-the-art sleep/wake classification algorithm based on wristband-type accelerometers\, termed ACCEL (acceleration-based classification and estimation of long-term sleep-wake cycles). In this study\, we optimized and applied ACCEL to large-scale analysis of human sleep phenotypes. The clustering of an about 100\,000-arm acceleration dataset in the UK Biobank using uniform manifold approximation and projection (UMAP) dimension reduction and density-based spatial clustering of applications with noise (DBSCAN) clustering methods identified 16 sleep phenotypes\, including those related to social jet lag\, chronotypes (“morning/night person”)\, and seven different insomnia-like phenotypes. Considering the complex relationship between sleep disorders and other psychiatric disorders\, these unbiased and comprehensive analyses of sleep phenotypes in humans will not only contribute to the advancement of biomedical research on genetic and environmental factors underlying human sleep patterns but also\, allow for the development of better digital biomarkers for psychiatric disorders.
URL:https://www.ibs.re.kr/bimag/event/2022-05-06-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220429T130000
DTEND;TZID=Asia/Seoul:20220429T140000
DTSTAMP:20220329T103359Z
CREATED:20220329T103359Z
LAST-MODIFIED:20220329T103359Z
UID:5877-1651237200-1651240800@www.ibs.re.kr
SUMMARY:Toroidal topology of population activity in grid cells
DESCRIPTION:We will discuss about “Toroidal topology of population activity in grid cells”\, Gardner et al.\, Nature\, 2021. \nAbstract: The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment. Grid cells\, a key component of this system\, fire in a characteristic hexagonal pattern of locations\, and are organized in modules that collectively form a population code for the animal’s allocentric position. The invariance of the correlation structure of this population code across environments and behavioral states\, independent of specific sensory inputs\, has pointed to intrinsic\, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern. However\, whether grid cell networks show continuous attractor dynamics\, and how they interface with inputs from the environment\, has remained unclear owing to the small samples of cells obtained so far. Here\, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis\, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold\, as expected in a two-dimensional CAN. Positions on the torus correspond to the positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep\, as predicted by CAN models for grid cells but not by alternative feedforward models. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.
URL:https://www.ibs.re.kr/bimag/event/2022-04-29-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:20220422T130000
DTEND;TZID=Asia/Seoul:20220422T140000
DTSTAMP:20220329T005550Z
CREATED:20220421T190000Z
LAST-MODIFIED:20220329T005550Z
UID:5874-1650632400-1650636000@www.ibs.re.kr
SUMMARY:An Efficient Characterization of Complex-Balanced\, Detailed-Balanced\, and Weakly Reversible Systems
DESCRIPTION:We will discuss about “An Efficient Characterization of Complex-Balanced\, Detailed-Balanced\, and Weakly Reversible Systems”\, Craciun et al.\, SIAM Journal on Applied Mathematics\, 2020 \nAbstract: Very often\, models in biology\, chemistry\, physics\, and engineering are systems of polynomial or power-law ordinary differential equations\, arising from a reaction network. Such dynamical systems can be generated by many different reaction networks. On the other hand\, networks with special properties (such as reversibility or weak reversibility) are known or conjectured to give rise to dynamical systems that have special properties: existence of positive steady states\, persistence\, permanence\, and (for well-chosen parameters) complex balancing or detailed balancing. These last two are related to thermodynamic equilibrium\, and therefore the positive steady states are unique and stable. We describe a computationally efficient characterization of polynomial or power-law dynamical systems that can be obtained as complex-balanced\, detailed-balanced\, weakly reversible\, and reversible mass-action systems.
URL:https://www.ibs.re.kr/bimag/event/2022-04-22-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:20220415T110000
DTEND;TZID=Asia/Seoul:20220415T130000
DTSTAMP:20220414T012030Z
CREATED:20220414T182000Z
LAST-MODIFIED:20220414T012030Z
UID:5868-1650020400-1650027600@www.ibs.re.kr
SUMMARY:A topological data analysis based classifier
DESCRIPTION:We will discuss about “A topological data analysis based classifier”\, Kindelan et al.\, arXiv\, 2022 \nAbstract: Topological Data Analysis is an emergent field that aims to discover the underlying dataset’s topological information. Topological Data Analysis tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes a different Topological Data Analysis pipeline to classify balanced and imbalanced multi-class datasets without additional ML methods. Our proposed method was designed to solve multi-class problems. It resolves multi-class imbalanced classification problems with no data resampling preprocessing stage. The proposed Topological Data Analysis-based classifier builds a filtered simplicial complex on the dataset representing high-order data relationships. Following the assumption that a meaningful sub-complex exists in the filtration that approximates the data topology\, we apply Persistent Homology to guide the selection of that sub-complex by considering detected topological features. We use each unlabeled point’s link and star operators to provide different sized and multi-dimensional neighborhoods to propagate labels from labeled to unlabeled points. The labeling function depends on the filtration entire history of the filtered simplicial complex and is encoded within the persistent diagrams at various dimensions. We select eight datasets with different dimensions\, degrees of class overlap\, and imbalanced samples per class. The TDABC outperforms all baseline methods classifying multi-class imbalanced data with high imbalanced ratios and data with overlapped classes. Also\, on average\, the proposed method was better than KNN and weighted-KNN and behaved competitively with SVM and Random Forest baseline classifiers in balanced datasets.
URL:https://www.ibs.re.kr/bimag/event/2022-04-15-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220408T130000
DTEND;TZID=Asia/Seoul:20220408T140000
DTSTAMP:20220405T042614Z
CREATED:20220407T190000Z
LAST-MODIFIED:20220405T042614Z
UID:5870-1649422800-1649426400@www.ibs.re.kr
SUMMARY:RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy
DESCRIPTION:We will discuss about “RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy”\, Behrendt et al.\, SoftwareX\, 2019 \nAbstract: This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the R package RTransferEntropy. We discuss the methodology\, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore\, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series.
URL:https://www.ibs.re.kr/bimag/event/2022-04-08-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:20220401T130000
DTEND;TZID=Asia/Seoul:20220401T140000
DTSTAMP:20220320T093117Z
CREATED:20220331T190000Z
LAST-MODIFIED:20220320T093117Z
UID:5564-1648818000-1648821600@www.ibs.re.kr
SUMMARY:Physics-informed learning of governing equations from scarce data
DESCRIPTION:We will discuss about “Physics-informed learning of governing equations from scarce data”\, Chen et al.\, Nature Communications\, 2021 \nAbstract: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive\, as in climate science\, neuroscience\, ecology\, finance\, and epidemiology\, to name only a few examples. In this work\, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics\, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular\, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems\, from simple canonical systems\, including linear and nonlinear oscillators and the chaotic Lorenz system\, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.
URL:https://www.ibs.re.kr/bimag/event/2022-04-01/
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:20220325T130000
DTEND;TZID=Asia/Seoul:20220325T140000
DTSTAMP:20220224T015444Z
CREATED:20220317T190000Z
LAST-MODIFIED:20220224T015444Z
UID:5562-1648213200-1648216800@www.ibs.re.kr
SUMMARY:Universal structural requirements for maximal robust perfect adaptation in biomolecular networks
DESCRIPTION:Abstract: Consider a biomolecular reaction network that exhibits robust perfect adaptation to disturbances from several parallel sources. The well-known Internal Model Principle of control theory suggests that such systems must include a subsystem (called the “internal model”) that is able to recreate the dynamic structure of the disturbances. This requirement poses certain structural constraints on the network which we elaborate in this paper for the scenario where constant-in-time disturbances maximally affect network interactions and there is model uncertainty and possible stochasticity in the dynamics. We prove that these structural constraints are primarily characterized by a simple linear-algebraic stoichiometric condition which remains the same for both deterministic and stochastic descriptions of the dynamics. Our results reveal the essential requirements for maximal robust perfect adaptation in biology\, with important implications for both systems and synthetic biology. We exemplify our results through many known examples of robustly adapting networks and we construct new examples of such networks with the aid of our linear-algebraic characterization.
URL:https://www.ibs.re.kr/bimag/event/2022-03-18/
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:20220318T130000
DTEND;TZID=Asia/Seoul:20220318T140000
DTSTAMP:20220224T015419Z
CREATED:20220310T190000Z
LAST-MODIFIED:20220224T015419Z
UID:5560-1647608400-1647612000@www.ibs.re.kr
SUMMARY:Data-driven discovery of coordinates and governing equations
DESCRIPTION:Abstract: The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics\, balancing model complexity with descriptive ability\, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However\, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work\, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus\, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.
URL:https://www.ibs.re.kr/bimag/event/2022-03-11/
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:20220311T130000
DTEND;TZID=Asia/Seoul:20220311T140000
DTSTAMP:20220224T015356Z
CREATED:20220303T190000Z
LAST-MODIFIED:20220224T015356Z
UID:5558-1647003600-1647007200@www.ibs.re.kr
SUMMARY:Transcription factor competition facilitates self-sustained oscillations in single gene genetic circuits
DESCRIPTION:Abstract: Genetic feedback loops can be used by cells as a means to regulate internal processes or keep track of time. It is often thought that\, for a genetic circuit to display self-sustained oscillations\, a degree of cooperativity is needed in the binding and unbinding of actor species. This cooperativity is usually modeled using a Hill function\, regardless of the actual promoter architecture. Moreover\, genetic circuits do not operate in isolation and often transcription factors are shared between different promoters. In this work we show how mathematical modelling of genetic feedback loops can be facilitated with a mechanistic fold-change function that takes into account the titration effect caused by competing binding sites for transcription factors. The model shows how the titration effect aids self-sustained oscillations in a minimal genetic feedback loop: a gene that produces its own repressor directly — without cooperative transcription factor binding. The use of delay differential equations leads to a stability contour that predicts whether a genetic feedback loop will show self-sustained oscillations\, even when taking the bursty nature of transcription into account. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-03-04/
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:20220304T130000
DTEND;TZID=Asia/Seoul:20220304T140000
DTSTAMP:20220224T015333Z
CREATED:20220224T190000Z
LAST-MODIFIED:20220224T015333Z
UID:5556-1646398800-1646402400@www.ibs.re.kr
SUMMARY:Modeling polypharmacy side effects with graph convolutional networks
DESCRIPTION:We will discuss about “Modeling polypharmacy side effects with graph convolutional networks”\, Zitnik\, Agrawal\, and Leskovec\, Bioinformatics\, 2018 \nMotivation\nThe use of drug combinations\, termed polypharmacy\, is common to treat patients with complex diseases or co-existing conditions. However\, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions\, in which activity of one drug may change\, favorably or unfavorably\, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare\, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. \nResults\nHere\, we present Decagon\, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions\, drug-protein target interactions and the polypharmacy side effects\, which are represented as drug-drug interactions\, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values\, Decagon can predict the exact side effect\, if any\, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects\, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore\, Decagon models particularly well polypharmacy side effects that have a strong molecular basis\, while on predominantly non-molecular side effects\, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.
URL:https://www.ibs.re.kr/bimag/event/2022-02-25/
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:20220218T130000
DTEND;TZID=Asia/Seoul:20220218T140000
DTSTAMP:20220130T031904Z
CREATED:20220130T031904Z
LAST-MODIFIED:20220130T031904Z
UID:5554-1645189200-1645192800@www.ibs.re.kr
SUMMARY:A Deficiency-Based Approach to Parametrizing Positive Equilibria of Biochemical Reaction Systems
DESCRIPTION:We will discuss about “A Deficiency-Based Approach to Parametrizing Positive Equilibria of Biochemical Reaction Systems”\, Johnston\, Müller\, and Pantea\, Bulletin of Mathematical Biology\, 2019 \nWe present conditions which guarantee a parametrization of the set of positive equilibria of a generalized mass-action system. Our main results state that (1) if the underlying generalized chemical reaction network has an effective deficiency of zero\, then the set of positive equilibria coincides with the parametrized set of complex-balanced equilibria and (2) if the network is weakly reversible and has a kinetic deficiency of zero\, then the equilibrium set is nonempty and has a positive\, typically rational\, parametrization. Via the method of network translation\, we apply our results to classical mass-action systems studied in the biochemical literature\, including the EnvZ–OmpR and shuttled WNT signaling pathways. A parametrization of the set of positive equilibria of a (generalized) mass-action system is often a prerequisite for the study of multistationarity and allows an easy check for the occurrence of absolute concentration robustness\, as we demonstrate for the EnvZ–OmpR pathway.
URL:https://www.ibs.re.kr/bimag/event/2022-02-18/
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:20220211T130000
DTEND;TZID=Asia/Seoul:20220211T140000
DTSTAMP:20220208T054847Z
CREATED:20220210T190000Z
LAST-MODIFIED:20220208T054847Z
UID:5552-1644584400-1644588000@www.ibs.re.kr
SUMMARY:Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
DESCRIPTION:We will discuss about “Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations”\, Mircea et al.\, 2022\, Genome Biology \nThe ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently\, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here\, we present phiclust (ϕ_clust)\, a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure\, testably leading to the discovery of previously overlooked phenotypes.
URL:https://www.ibs.re.kr/bimag/event/2022-02-11/
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:20220204T130000
DTEND;TZID=Asia/Seoul:20220204T140000
DTSTAMP:20220125T115800Z
CREATED:20220126T170000Z
LAST-MODIFIED:20220125T115800Z
UID:5397-1643979600-1643983200@www.ibs.re.kr
SUMMARY:Mechanisms for the generation of robust circadian oscillations through ultrasensitivity and differential binding affinity
DESCRIPTION:We will discuss about “Mechanisms for the generation of robust circadian oscillations through ultrasensitivity and differential binding affinity”\, Behera\, Junco\, and Vaikuntanathan\, The Journal of Physical Chemistry B\, 2021 \nBiochemical circadian rhythm oscillations play an important role in many signaling mechanisms. In this work\, we explore some of the biophysical mechanisms responsible for sustaining robust oscillations by constructing a minimal but analytically tractable model of the circadian oscillations in the KaiABC protein system found in the cyanobacteria S. elongatus. In particular\, our minimal model explicitly accounts for two experimentally characterized biophysical features of the KaiABC protein system\, namely\, a differential binding affinity and an ultrasensitive response. Our analytical work shows how these mechanisms might be crucial for promoting robust oscillations even in suboptimal nutrient conditions. Our analytical and numerical work also identifies mechanisms by which biological clocks can stably maintain a constant time period under a variety of nutrient conditions. Finally\, our work also explores the thermodynamic costs associated with the generation of robust sustained oscillations and shows that the net rate of entropy production alone might not be a good figure of merit to asses the quality of oscillations. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-02-04/
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:20220119T110000
DTEND;TZID=Asia/Seoul:20220119T120000
DTSTAMP:20220115T115214Z
CREATED:20220118T170000Z
LAST-MODIFIED:20220115T115214Z
UID:5400-1642590000-1642593600@www.ibs.re.kr
SUMMARY:Network design principle for robust oscillatory behaviors with respect to biological noise
DESCRIPTION:We will discuss about “Network design principle for robust oscillatory behaviors with respect to biological noise”\, Qiao et al\, bioRxiv\, 2021 \nOscillatory behaviors\, which are ubiquitous in transcriptional regulatory networks\, are often subject to inevitable biological noise. Thus a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here\, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that\, no matter what source of the noise is applied\, the topologies containing the repressilator with positive auto-regulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator\, and additional positive auto-regulation enhances the robustness against noise. Nevertheless\, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period\, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore\, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies\, and verify that the addition of a repressilator to the activator-inhibitor oscillator\, which leads to the emergence of high-robustness motif—the repressilator with positive auto-regulation\, improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.
URL:https://www.ibs.re.kr/bimag/event/2022-01-19/
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:20220113T130000
DTEND;TZID=Asia/Seoul:20220113T140000
DTSTAMP:20220112T070151Z
CREATED:20220112T190000Z
LAST-MODIFIED:20220112T070151Z
UID:5395-1642078800-1642082400@www.ibs.re.kr
SUMMARY:Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation
DESCRIPTION:We will discuss about “Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation”\, Wagner et al\, bioRxiv\, 2021 \nMotivation: The Chemical Master Equation is the most comprehensive stochastic approach to describe the evolution of a (bio-)chemical reaction system. Its solution is a time-dependent probability distribution on all possible configurations of the system. As the number of possible configurations is typically very large\, the Master Equation is often practically unsolvable. The Method of Moments reduces the system to the evolution of a few moments of this distribution\, which are described by a system of ordinary differential equations. Those equations are not closed\, since lower order moments generally depend on higher order moments. Various closure schemes have been suggested to solve this problem\, with different advantages and limitations. Two major problems with these approaches are first that they are open loop systems\, which can diverge from the true solution\, and second\, some of them are computationally expensive. \nResults: Here we introduce Quasi-Entropy Closure\, a moment closure scheme for the Method of Moments which estimates higher order moments by reconstructing the distribution that minimizes the distance to a uniform distribution subject to lower order moment constraints. Quasi-Entropy closure is similar to Zero-Information closure\, which maximizes the information entropy. Results show that both approaches outperform truncation schemes. Moreover\, Quasi-Entropy Closure is computationally much faster than Zero-Information Closure. Finally\, our scheme includes a plausibility check for the existence of a distribution satisfying a given set of moments on the feasible set of configurations. Results are evaluated on different benchmark problems.
URL:https://www.ibs.re.kr/bimag/event/2022-01-13/
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:20220107T130000
DTEND;TZID=Asia/Seoul:20220107T140000
DTSTAMP:20211224T001535Z
CREATED:20220106T190000Z
LAST-MODIFIED:20211224T001535Z
UID:5363-1641560400-1641564000@www.ibs.re.kr
SUMMARY:Fundamental limits on the suppression of molecular fluctuations
DESCRIPTION:We will discuss about “Fundamental limits on the suppression of molecular fluctuations”\, Lestas et al\, Nature\, 2010 \nAbstract: Negative feedback is common in biological processes and can increase a system’s stability to internal and external perturbations. But at the molecular level\, control loops always involve signalling steps with finite rates for random births and deaths of individual molecules. Here we show\, by developing mathematical tools that merge control and information theory with physical chemistry\, that seemingly mild constraints on these rates place severe limits on the ability to suppress molecular fluctuations. Specifically\, the minimum standard deviation in abundances decreases with the quartic root of the number of signalling events\, making it extremely expensive to increase accuracy. Our results are formulated in terms of experimental observables\, and existing data show that cells use brute force when noise suppression is essential; for example\, regulatory genes are transcribed tens of thousands of times per cell cycle. The theory challenges conventional beliefs about biochemical accuracy and presents an approach to the rigorous analysis of poorly characterized biological systems.
URL:https://www.ibs.re.kr/bimag/event/2022-01-07/
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:20211231T130000
DTEND;TZID=Asia/Seoul:20211231T140000
DTSTAMP:20211227T004211Z
CREATED:20211230T190000Z
LAST-MODIFIED:20211227T004211Z
UID:5306-1640955600-1640959200@www.ibs.re.kr
SUMMARY:The Generalized Multiset Sampler
DESCRIPTION:We will discuss about “The Generalized Multiset Sampler”\, Kim and MacEachern\, The Journal of Computation and Graphical Statistics\, 2021 \nAbstract: The multiset sampler\, an MCMC algorithm recently proposed by Leman and coauthors\, is an easy-to-implement algorithm which is especially well-suited to drawing samples from a multimodal distribution. We generalize the algorithm by redefining the multiset sampler with an explicit link between target distribution and sampling distribution. The generalized formulation replaces the multiset with a K-tuple\, which allows us to use the algorithm on unbounded parameter spaces\, improves estimation\, and sets up further extensions to adaptive MCMC techniques. Theoretical properties of the algorithm are provided and guidance is given on its implementation. Examples\, both simulated and real\, confirm that the generalized multiset sampler provides a simple\, general and effective approach to sampling from multimodal distributions. Supplementary materials for this article are available online.
URL:https://www.ibs.re.kr/bimag/event/2021-12-31/
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:20211224T130000
DTEND;TZID=Asia/Seoul:20211224T140000
DTSTAMP:20211221T043551Z
CREATED:20211223T190000Z
LAST-MODIFIED:20211221T043551Z
UID:5302-1640350800-1640354400@www.ibs.re.kr
SUMMARY:Information Integration and Energy Expenditure in Gene Regulation
DESCRIPTION:We will discuss about “Information Integration and Energy Expenditure in Gene Regulation”\, Estrada et al.\, Cell\, 2016 \nAbstract: The quantitative concepts used to reason about gene regulation largely derive from bacterial studies. We show that this bacterial paradigm cannot explain the sharp expression of a canonical developmental gene in response to a regulating transcription factor (TF). In the absence of energy expenditure\, with regulatory DNA at thermodynamic equilibrium\, information integration across multiple TF binding sites can generate the required sharpness\, but with strong constraints on the resultant “higher-order cooperativities.” Even with such integration\, there is a “Hopfield barrier” to sharpness; for n TF binding sites\, this barrier is represented by the Hill function with the Hill coefficient n. If\, however\, energy is expended to maintain regulatory DNA away from thermodynamic equilibrium\, as in kinetic proofreading\, this barrier can be breached and greater sharpness achieved. Our approach is grounded in fundamental physics\, leads to testable experimental predictions\, and suggests how a quantitative paradigm for eukaryotic gene regulation can be formulated.
URL:https://www.ibs.re.kr/bimag/event/2021-12-24/
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:20211215T143000
DTEND;TZID=Asia/Seoul:20211215T160000
DTSTAMP:20211214T070933Z
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:20211122T014405Z
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:20211101T080821Z
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:20211111T084242Z
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:20211001T062513Z
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:20211006T081805Z
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:20210831T052818Z
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:20210831T052758Z
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:20210903T055048Z
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:20210831T052727Z
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:20210812T095509Z
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
END:VCALENDAR