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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221104T150000
DTEND;TZID=Asia/Seoul:20221104T170000
DTSTAMP:20260423T031620
CREATED:20220930T035218Z
LAST-MODIFIED:20221030T231656Z
UID:6648-1667574000-1667581200@www.ibs.re.kr
SUMMARY:Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach
DESCRIPTION:We will discuss about “Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach”\, Öcal\, Kaan\, Guido Sanguinetti\, and Ramon Grima.\, arXiv preprint arXiv:2210.05329 (2022). \nAbstract: \nThe complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist’s toolkit. For stochastic reaction networks described using the Chemical Master Equation\, commonly used methods include time-scale separation\, the Linear Mapping Approximation and state-space lumping. Despite the success of these techniques\, they appear to be rather disparate and at present no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimising a well-known information-theoretic quantity between the full model and its reduction\, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimisation approaches. In addition we derive general expressions for the propensities of a reduced system that generalise those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop\, the Michaelis-Menten enzyme system and a genetic oscillator.
URL:https://www.ibs.re.kr/bimag/event/2022-11-04-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221028T140000
DTEND;TZID=Asia/Seoul:20221028T160000
DTSTAMP:20260423T031620
CREATED:20220930T035148Z
LAST-MODIFIED:20221027T083230Z
UID:6646-1666965600-1666972800@www.ibs.re.kr
SUMMARY:Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19
DESCRIPTION:We will discuss about “Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19”\, Cheng\, Jinyu\, et al.\, Briefings in bioinformatics 22.2 (2021): 988-1005. \nAbstract: \nInferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study\, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (https://github.com/SunXQlab/scMLnet). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF\, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression\, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition\, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.
URL:https://www.ibs.re.kr/bimag/event/2022-10-28-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221021T150000
DTEND;TZID=Asia/Seoul:20221021T170000
DTSTAMP:20260423T031620
CREATED:20220930T035045Z
LAST-MODIFIED:20221019T070546Z
UID:6641-1666364400-1666371600@www.ibs.re.kr
SUMMARY:Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level
DESCRIPTION:We will discuss about “Rhythmicity is linked to expression cost at the protein level but to expression precision at the mRNA level”\, David Laloum\, and Marc Robinson-Rechavi\, PLoS computational biology 18.9 (2022): e1010399. \nAbstract: \nMany genes have nycthemeral rhythms of expression\, i.e. a 24-hours periodic variation\, at either mRNA or protein level or both\, and most rhythmic genes are tissue-specific. Here\, we investigate and discuss the evolutionary origins of rhythms in gene expression. Our results suggest that rhythmicity of protein expression could have been favored by selection to minimize costs. Trends are consistent in bacteria\, plants and animals\, and are also supported by tissue-specific patterns in mouse. Unlike for protein level\, cost cannot explain rhythm at the RNA level. We suggest that instead it allows to periodically reduce expression noise. Noise control had the strongest support in mouse\, with limited evidence in other species. We have also found that genes under stronger purifying selection are rhythmically expressed at the mRNA level\, and we propose that this is because they are noise sensitive genes. Finally\, the adaptive role of rhythmic expression is supported by rhythmic genes being highly expressed yet tissue-specific. This provides a good evolutionary explanation for the observation that nycthemeral rhythms are often tissue-specific.
URL:https://www.ibs.re.kr/bimag/event/2022-10-21-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220930T150000
DTEND;TZID=Asia/Seoul:20220930T160000
DTSTAMP:20260423T031620
CREATED:20220830T012122Z
LAST-MODIFIED:20220830T012141Z
UID:6531-1664550000-1664553600@www.ibs.re.kr
SUMMARY:Absolute concentration robustness in networks with low-dimensional stoichiometric subspace
DESCRIPTION:We will discuss about “Absolute concentration robustness in networks with low-dimensional stoichiometric subspace”\, Meshkat\, Nicolette\, Anne Shiu\, and Angelica Torres.\, Vietnam Journal of Mathematics 50.3 (2022): 623-651. \nAbstract: \nA reaction system exhibits “absolute concentration robustness” (ACR) in some species if the positive steady-state value of that species does not depend on initial conditions. Mathematically\, this means that the positive part of the variety of the steady-state ideal lies entirely in a hyperplane of the form xi = c\, for some c > 0. Deciding whether a given reaction system – or those arising from some reaction network – exhibits ACR is difficult in general\, but here we show that for many simple networks\, assessing ACR is straightforward. Indeed\, our criteria for ACR can be performed by simply inspecting a network or its standard embedding into Euclidean space. Our main results pertain to networks with many conservation laws\, so that all reactions are parallel to one other. Such “one-dimensional” networks include those networks having only one species. We also consider networks with only two reactions\, and show that ACR is characterized by a well-known criterion of Shinar and Feinberg. Finally\, up to some natural ACR-preserving operations – relabeling species\, lengthening a reaction\, and so on – only three families of networks with two reactions and two species have ACR. Our results are proven using algebraic and combinatorial techniques. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-09-30-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220923T150000
DTEND;TZID=Asia/Seoul:20220923T160000
DTSTAMP:20260423T031620
CREATED:20220830T011634Z
LAST-MODIFIED:20220922T011820Z
UID:6529-1663945200-1663948800@www.ibs.re.kr
SUMMARY:Cell clustering for spatial transcriptomics data with graph neural networks
DESCRIPTION:We will discuss about “Cell clustering for spatial transcriptomics data with graph neural networks”\, Li\, J.\, Chen\, S.\, Pan\, X. et al.\, Nat Comput Sci 2\, 399–408 (2022) \nAbstract: \nSpatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficiently. Taking advantage of spatial transcriptomics and graph neural networks\, we introduce cell clustering for spatial transcriptomics data with graph neural networks\, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category annotation. On the basis of its application to five in vitro and in vivo spatial datasets\, we show that cell clustering for spatial transcriptomics outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells. From enhanced sequential fluorescence in situ hybridization data of brain\, cell clustering for spatial transcriptomics finds functional cell subtypes with different micro-environments\, which are all validated experimentally\, inspiring biological hypotheses about the underlying interactions among the cell state\, cell type and micro-environment. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2022-09-23/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220916T110000
DTEND;TZID=Asia/Seoul:20220916T120000
DTSTAMP:20260423T031620
CREATED:20220825T190000Z
LAST-MODIFIED:20220905T053032Z
UID:6351-1663326000-1663329600@www.ibs.re.kr
SUMMARY:Physics-informed neural networks for PDE-constrained optimization and control
DESCRIPTION:We will discuss about “Physics-informed neural networks for PDE-constrained optimization and control”\, Barry-Straume\, Jostein\, et al.\, arXiv preprint arXiv:2205.03377 (2022). \nAbstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control PhysicsInformed Neural Networks simultaneously solve a given system state\, and its respective optimal control\, in a one-stage framework that conforms to physical laws of the system. Prior approaches use a two-stage framework that models and controls a system sequentially\, whereas Control PINNs incorporates the required optimality conditions in its architecture and loss function. The success of Control PINNs is demonstrated by solving the following open-loop optimal control problems: (i) an analytical problem (ii) a one-dimensional heat equation\, and (iii) a two-dimensional predator-prey problem.
URL:https://www.ibs.re.kr/bimag/event/2022-09-16-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220902T150000
DTEND;TZID=Asia/Seoul:20220902T160000
DTSTAMP:20260423T031620
CREATED:20220817T042800Z
LAST-MODIFIED:20220828T171528Z
UID:6398-1662130800-1662134400@www.ibs.re.kr
SUMMARY:Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data
DESCRIPTION:We will discuss about “Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data”\, Huang\, Qi\, Journal of The Royal Society Interface 15.139 (2018): 20170885. \nAbstract: Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity\, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates\, based on probabilities of transitions between rest and activity\, that are interpretable and of interest to circadian research.
URL:https://www.ibs.re.kr/bimag/event/2022-09-02-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220826T130000
DTEND;TZID=Asia/Seoul:20220826T140000
DTSTAMP:20260423T031620
CREATED:20220825T190000Z
LAST-MODIFIED:20220825T155707Z
UID:6348-1661518800-1661522400@www.ibs.re.kr
SUMMARY:Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
DESCRIPTION:We will discuss about “Inferring Regulatory Networks from Expression Data Using Tree-Based Methods\,” Huynh-Thu et al.\, PLoS ONE (2010). \nAbstract: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data\, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article\, we present GENIE3\, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems\, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes)\, using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data\, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn’t make any assumption about the nature of gene regulation\, can deal with combinatorial and non-linear interactions\, produces directed GRNs\, and is fast and scalable. In conclusion\, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm\, based on feature selection with tree-based ensemble methods\, is simple and generic\, making it adaptable to other types of genomic data and interactions.
URL:https://www.ibs.re.kr/bimag/event/2022-08-26-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220812T130000
DTEND;TZID=Asia/Seoul:20220812T140000
DTSTAMP:20260423T031620
CREATED:20220811T190000Z
LAST-MODIFIED:20220728T092951Z
UID:6338-1660309200-1660312800@www.ibs.re.kr
SUMMARY:Molecular convolutional neural networks with DNA regulatory circuits
DESCRIPTION:We will discuss about “Molecular convolutional neural networks with DNA regulatory circuits”\, Pei\, Hao\, et al.\, Nature Machine Intelligence (2022): 1-11. \nAbstract: Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s\, synthetic DNA circuits in liquid phase have been developed as computational hardware to perform neural network-like computations that harness the collective properties of complex biochemical systems. However\, scaling up such DNA-based neural networks to support more powerful computation remains challenging. Here we present a systematic molecular implementation of a convolutional neural network algorithm with synthetic DNA regulatory circuits based on a simple switching gate architecture. Our DNA-based weight-sharing convolutional neural network can simultaneously implement parallel multiply–accumulate operations for 144-bit inputs and recognize patterns in up to eight categories autonomously. Further\, this system can be connected with other DNA circuits to construct hierarchical networks to recognize patterns in up to 32 categories with a two-step approach: coarse classification on language (Arabic numerals\, Chinese oracles\, English alphabets and Greek alphabets) followed by classification into specific handwritten symbols. We also reduced the computation time from hours to minutes by using a simple cyclic freeze–thaw approach. Our DNA-based regulatory circuits are a step towards the realization of a molecular computer with high computing power and the ability to classify complex and noisy information.
URL:https://www.ibs.re.kr/bimag/event/2022-08-12-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220805T130000
DTEND;TZID=Asia/Seoul:20220805T140000
DTSTAMP:20260423T031620
CREATED:20220804T190000Z
LAST-MODIFIED:20220729T014246Z
UID:6341-1659704400-1659708000@www.ibs.re.kr
SUMMARY:Neural Ordinary Differential Equations
DESCRIPTION:We will discuss about “Neural Ordinary Differential Equations”\, Chen\, Ricky TQ\, et al.\, Advances in neural information processing systems 31 (2018). \nAbstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers\, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost\, adapt their evaluation strategy to each input\, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows\, a generative model that can train by maximum likelihood\, without partitioning or ordering the data dimensions. For training\, we show how to scalably backpropagate through any ODE solver\, without access to its internal operations. This allows end-to-end training of ODEs within larger models.
URL:https://www.ibs.re.kr/bimag/event/2022-08-05-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220729T130000
DTEND;TZID=Asia/Seoul:20220729T140000
DTSTAMP:20260423T031620
CREATED:20220728T190000Z
LAST-MODIFIED:20220728T085252Z
UID:6250-1659099600-1659103200@www.ibs.re.kr
SUMMARY:Learning stable and predictive structures in kinetic systems
DESCRIPTION:We will discuss about “Learning stable and predictive structures in kinetic systems”\, Niklas Pfister \, Stefan Bauer\, and Jonas Peters. PNAS\, 2019 \nAbstract: Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework\, called CausalKinetiX\, that identifies structure from discrete time\, noisy observations\, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying\, invariant kinetic model\, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well-established approaches focusing solely on predictive performance\, especially for out-of-sample generalization.
URL:https://www.ibs.re.kr/bimag/event/2022-07-29-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220722T130000
DTEND;TZID=Asia/Seoul:20220722T140000
DTSTAMP:20260423T031620
CREATED:20220629T010032Z
LAST-MODIFIED:20220629T010032Z
UID:6248-1658494800-1658498400@www.ibs.re.kr
SUMMARY:Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells
DESCRIPTION:We will discuss about “Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells”\, Jia\, Chen\, and Ramon Grima\, bioRxiv (2022). \nAbstract: The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA\, and switching of the gene between active and inactive states. While commonly used\, this model does not describe how fluctuations are influenced by the cell cycle phase\, cellular growth and division\, and other crucial aspects of cellular biology. Here we derive the analytical time-dependent solution of a stochastic model that explicitly considers various sources of intrinsic and extrinsic noise: switching between inactive and active states\, doubling of gene copy numbers upon DNA replication\, dependence of the mRNA synthesis rate on cellular volume\, gene dosage compensation\, partitioning of molecules during cell division\, cell-cycle duration variability\, and cell-size control strategies. We show that generally the analytical distribution of transcript numbers in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models\, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak\, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.
URL:https://www.ibs.re.kr/bimag/event/2022-07-22-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220708T130000
DTEND;TZID=Asia/Seoul:20220708T140000
DTSTAMP:20260423T031620
CREATED:20220707T190000Z
LAST-MODIFIED:20220629T005506Z
UID:6246-1657285200-1657288800@www.ibs.re.kr
SUMMARY:Chemical Organisation Theory
DESCRIPTION:We will discuss about “Chemical Organisation Theory\n“\, Dittrich\, Peter\, and Pietro Speroni Di Fenizio\, Bulletin of mathematical biology 69.4 (2007): 1199-1231. \nAbstract: Complex dynamical reaction networks consisting of many components that interact and produce each other are difficult to understand\, especially\, when new component types may appear and present component types may vanish completely. Inspired by Fontana and Buss (Bull. Math. Biol.\, 56\, 1–64) we outline a theory to deal with such systems. The theory consists of two parts. The first part introduces the concept of a chemical organisation as a closed and self-maintaining set of components. This concept allows to map a complex (reaction) network to the set of organisations\, providing a new view on the system’s structure. The second part connects dynamics with the set of organisations\, which allows to map a movement of the system in state space to a movement in the set of organisations. The relevancy of our theory is underlined by a theorem that says that given a differential equation describing the chemical dynamics of the network\, then every stationary state is an instance of an organisation. For demonstration\, the theory is applied to a small model of HIV-immune system interaction by Wodarz and Nowak (Proc. Natl. Acad. USA\, 96\, 14464–14469) and to a large model of the sugar metabolism of E. Coli by Puchalka and Kierzek (Biophys. J.\, 86\, 1357–1372). In both cases organisations where uncovered\, which could be related to functions.
URL:https://www.ibs.re.kr/bimag/event/2022-07-08-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220705T100000
DTEND;TZID=Asia/Seoul:20220705T110000
DTSTAMP:20260423T031620
CREATED:20220704T160000Z
LAST-MODIFIED:20220704T035619Z
UID:6122-1657015200-1657018800@www.ibs.re.kr
SUMMARY:AI Pontryagin or how artificial neural networks learn to control dynamical systems
DESCRIPTION:We will discuss about “AI Pontryagin or how artificial neural networks learn to control dynamical systems”\, Böttcher\, L.\, Antulov-Fantulin\, N. & Asikis\, T.\, Nat Commun 13\, 333 (2022). \nAbstract: The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems\, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variational calculus\, their solution for complex systems is often analytically and computationally intractable. To overcome this outstanding challenge\, we present AI Pontryagin\, a versatile control framework based on neural ordinary differential equations that automatically learns control signals that steer high-dimensional dynamical systems towards a desired target state within a specified time interval. We demonstrate the ability of AI Pontryagin to learn control signals that closely resemble those found by corresponding optimal control frameworks in terms of control energy and deviation from the desired target state. Our results suggest that AI Pontryagin is capable of solving a wide range of control and optimization problems\, including those that are analytically intractable
URL:https://www.ibs.re.kr/bimag/event/2022-07-05-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220623T123000
DTEND;TZID=Asia/Seoul:20220623T133000
DTSTAMP:20260423T031620
CREATED:20220622T183000Z
LAST-MODIFIED:20220623T060141Z
UID:6104-1655987400-1655991000@www.ibs.re.kr
SUMMARY:Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE\, UMAP\, TriMAP\, and PaCMAP for Data Visualization
DESCRIPTION:We will discuss about “Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE\, UMAP\, TriMAP\, and PaCMAP for Data Visualization”\, Wang\, Yingfan\, et al.\, J. Mach. Learn. Res.\, 2021. \nAbstract: Dimension reduction (DR) techniques such as t-SNE\, UMAP\, and TriMAP have demonstrated impressive visualization performance on many real world datasets. One tension that has always faced these methods is the trade-off between preservation of global structure and preservation of local structure: these methods can either handle one or the other\, but not both. In this work\, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce. Towards the goal of local structure preservation\, we provide several useful design principles for DR loss functions based on our new understanding of the mechanisms behind successful DR methods. Towards the goal of global structure preservation\, our analysis illuminates that the choice of which components to preserve is important. We leverage these insights to design a new algorithm for DR\, called Pairwise Controlled Manifold Approximation Projection (PaCMAP)\, which preserves both local and global structure. Our work provides several unexpected insights into what design choices both to make and avoid when constructing DR algorithms.
URL:https://www.ibs.re.kr/bimag/event/2022-06-23-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220616T130000
DTEND;TZID=Asia/Seoul:20220616T140000
DTSTAMP:20260423T031620
CREATED:20220615T190000Z
LAST-MODIFIED:20220623T060231Z
UID:6124-1655384400-1655388000@www.ibs.re.kr
SUMMARY:Identifying the critical states of complex diseases by the dynamic change of multivariate distribution
DESCRIPTION:We will discuss about “Identifying the critical states of complex diseases by the dynamic change of multivariate distribution”\, Peng\, Hao\, et al.\, Briefings in Bioinformatics\, 2022. \nAbstract: The dynamics of complex diseases are not always smooth; they are occasionally abrupt\, i.e. there is a critical state transition or tipping point at which the disease undergoes a sudden qualitative shift. There are generally a few significant differences in the critical state in terms of gene expressions or other static measurements\, which may lead to the failure of traditional differential expression-based biomarkers to identify such a tipping point. In this study\, we propose a computational method\, the direct interaction network-based divergence\, to detect the critical state of complex diseases by exploiting the dynamic changes in multivariable distributions inferred from observable samples and local biomolecular direct interaction networks. Such a method is model-free and applicable to both bulk and single-cell expression data. Our approach was validated by successfully identifying the tipping point just before the occurrence of a critical transition for both a simulated data set and seven real data sets\, including those from The Cancer Genome Atlas and two single-cell RNA-sequencing data sets of cell differentiation. Functional and pathway enrichment analyses also validated the computational results from the perspectives of both molecules and networks.
URL:https://www.ibs.re.kr/bimag/event/2022-06-16-jc-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220603T130000
DTEND;TZID=Asia/Seoul:20220603T140000
DTSTAMP:20260423T031620
CREATED:20220525T170000Z
LAST-MODIFIED:20220529T181413Z
UID:5986-1654261200-1654264800@www.ibs.re.kr
SUMMARY:Approximating Solutions of the Chemical Master Equation using Neural Networks
DESCRIPTION:We will discuss about “Approximating Solutions of the Chemical Master Equation using Neural Networks”\, Sukys et al.\, bioRxiv\, 2022 \nAbstract: The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions\, but it cannot be solved analytically for most systems of practical interest. While Monte Carlo methods provide a principled means to probe the system dy- namics\, their high computational cost can render the estimation of molecule number distributions and other numerical tasks infeasible due to the large number of repeated simulations typically required. In this paper we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training a neural network to learn the distributions predicted by the CME from a relatively small number of stochastic simulations\, thereby accelerating computationally intensive tasks such as parameter exploration and inference. We show on biologically relevant examples that simple neural networks with one hidden layer are able to cap- ture highly complex distributions across parameter space. We provide a detailed discussion of the neural network implementation and code for easy reproducibility.
URL:https://www.ibs.re.kr/bimag/event/2022-06-03-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220512T150000
DTEND;TZID=Asia/Seoul:20220512T160000
DTSTAMP:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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:20260423T031620
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
END:VCALENDAR