BEGIN:VCALENDAR
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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:20220927T160000
DTEND;TZID=Asia/Seoul:20220927T170000
DTSTAMP:20260424T095204
CREATED:20220920T065117Z
LAST-MODIFIED:20220920T080942Z
UID:6633-1664294400-1664298000@www.ibs.re.kr
SUMMARY:Causal Inference – basics and examples
DESCRIPTION:Abstract: \nIn real world\, people are interested in causality rather than association. For example\, pharmaceutical companies want to know effectiveness of their new drugs against diseases. South Korea Government officials are concerned about the effects of recent regulation with respect to an electric car subsidy from United States. Due to this reason\, causal inference has been received much attention in decades and it is now a big research field in statistics. In this seminar\, I will talk about basic idea and theory in the causal inference. Real data examples will be discussed.
URL:https://www.ibs.re.kr/bimag/event/2022-09-27-seminar/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
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:20260424T095204
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:20220919T133000
DTEND;TZID=Asia/Seoul:20220919T140000
DTSTAMP:20260424T095204
CREATED:20220904T124842Z
LAST-MODIFIED:20220904T124842Z
UID:6555-1663594200-1663596000@www.ibs.re.kr
SUMMARY:Design frameworks for engineering efficient cell factory performance within host and industrial constraints
DESCRIPTION:This talk will be given online. \nAbstract: \nSynthetic biology and microbial biotechnology offer sustainable routes to the manufacture of commodity and high value chemicals from agricultural by-products instead of petrochemical feedstocks. However\, engineered gene circuits and metabolic pathways both co-opt the cell’s gene expression machinery for protein/enzyme production and divert metabolic flux away from key host biosynthetic building blocks to a desired product. These interactions impair host growth and complicate the engineering of synthetic functions. To overcome these difficulties\, we propose a host-aware engineering approach which accounts for these constraints during the circuit/pathway design process. Here we present a dynamic whole cell modelling framework of microbial growth\, metabolism\, and gene expression which captures key host-circuit/pathway interactions. By coupling our modelling framework with systems engineering approaches and multi-objective optimization tools\, we identify key design trade-offs\, make recommendations for optimal host resource usage\, and develop feedback control strategies which improve pathway productivity and yields.
URL:https://www.ibs.re.kr/bimag/event/2022-09-19-seminar-2/
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220919T130000
DTEND;TZID=Asia/Seoul:20220919T133000
DTSTAMP:20260424T095204
CREATED:20220904T124617Z
LAST-MODIFIED:20220904T125510Z
UID:6552-1663592400-1663594200@www.ibs.re.kr
SUMMARY:STEM Initiatives for Agricultural 4.0 and Beyond
DESCRIPTION:This talk will be given online. \nAbstract: \nThe establishment of UN Sustainable Development Goals (SDG) has led to widespread initiative in STEM learning and research in realising these goals. Here\, we will look at some of the works that use control engineering approaches for smart farming (also known as Agriculture 4.0) applications that addresses UN SDG Goal No. 2 – ZERO HUNGER. The tools developed have tremendous potential in optimising conditions required for enhanced crop efficiency and productivity for Agriculture 4.0.
URL:https://www.ibs.re.kr/bimag/event/2022-09-19-seminar-1/
CATEGORIES:Biomedical Mathematics Seminar
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:20260424T095204
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:20260424T095204
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:20220902T110000
DTEND;TZID=Asia/Seoul:20220902T120000
DTSTAMP:20260424T095204
CREATED:20220825T010806Z
LAST-MODIFIED:20220829T000006Z
UID:6463-1662116400-1662120000@www.ibs.re.kr
SUMMARY:Cell signaling in 2D vs. 3D
DESCRIPTION:Abstract: \nThe activation of Ras depends upon the translocation of its guanine nucleotide exchange factor\, Sos\, to the plasma membrane. Moreover\, artificially inducing Sos to translocate to the plasma membrane is sufficient to bring about Ras activation and activation of Ras’s targets. There are many other examples of signaling proteins that must translocate to the membrane in order to relay a signal. \nOne attractive idea is that translocation promotes signaling by bringing a protein closer to its target. However\, proteins that are anchored to the membrane diffuse more slowly than cytosolic proteins do\, and it is not clear whether the concentration effect or the diffusion effect would be expected to dominate. Here we have used a reconstituted\, controllable system to measure the association rate for the same binding reaction in 3D vs. 2D to see whether association is promoted\, and\, if so\, how.
URL:https://www.ibs.re.kr/bimag/event/20220902_colloquium/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/08/Ferrell_profile-250x250-1.jpg
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:20260424T095204
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:20220816T100000
DTEND;TZID=Asia/Seoul:20220816T110000
DTSTAMP:20260424T095204
CREATED:20220815T160000Z
LAST-MODIFIED:20220815T124820Z
UID:6376-1660644000-1660647600@www.ibs.re.kr
SUMMARY:Circadian Interventions in Shift Workers
DESCRIPTION:This talk will be given online (If you want to join\, please send me an email to jaekkim@ibs.re.kr) \nAbstract \nCoupling Math with User-Centric Design Shift workers experience profound circadian disruption due to the nature of their work\, which often has them on-the-clock at times when their internal clock is sending a strong\, sleep-promoting signal. Mathematical models can be used to generate recommendations for shift workers that move their internal clock state to better align with their work schedules\, promote overall sleep\, promote alertness at key times\, or achieve other desired outcomes. Yet for these schedules to have a positive effect in the real world\, they need to be acceptable to the shift workers themselves. In this talk\, I will survey the types of schedules a shift worker may be recommended by an algorithm\, and how they can collide with the preferences of the real people being asked to follow them\, and how math can be used to arrive at new schedules that take these human factors into account.
URL:https://www.ibs.re.kr/bimag/event/2022-08-16-seminar/
CATEGORIES:Biomedical Mathematics Seminar
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:20260424T095204
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:20260424T095204
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:20260424T095204
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:20260424T095204
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:20260424T095204
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:20220705T160000
DTEND;TZID=Asia/Seoul:20220705T170000
DTSTAMP:20260424T095204
CREATED:20220704T220000Z
LAST-MODIFIED:20220625T051518Z
UID:6240-1657036800-1657040400@www.ibs.re.kr
SUMMARY:TENET+: a tool for reconstructing gene networks by integrating single cell expression and chromatin accessibility data
DESCRIPTION:Reconstruction of gene regulatory networks (GRNs) is a powerful approach to capture a prioritized gene set controlling cellular processes. In our previous study\, we developed TENET a GRN reconstructor from single cell RNA sequencing (scRNAseq). TENET has a superior capability to identify key regulators compared with other algorithms. However\, accurate inference of gene regulation is still challenging. Here\, we suggest an integrative strategy called TENET+ by combining single cell transcriptome and chromatin accessibility data. By applying TENET+ to a paired scRNAseq and scATACseq dataset of human peripheral blood mononuclear cells\, we found critical regulators and their epigenetic regulations for the differentiations of CD4 T cells\, CD8 T cells\, B cells and monocytes. Interestingly\, TENET+ predicted LRRFIP1 and ZBTB16 as top regulators of CD4 and CD8 T cells which were not predicted in a motif-based tool SCENIC. In sum\, TENET+ is a tool predicting epigenetic gene regulatory programs in unbiased way\, suggesting that novel epigenetic regulations can be identified by TENET+.
URL:https://www.ibs.re.kr/bimag/event/2022-07-05-seminar/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
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:20260424T095204
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:20260424T095204
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:20220616T160000
DTEND;TZID=Asia/Seoul:20220616T170000
DTSTAMP:20260424T095204
CREATED:20220613T130628Z
LAST-MODIFIED:20220613T130628Z
UID:6180-1655395200-1655398800@www.ibs.re.kr
SUMMARY:Deep Learning-based Uncertainty Quantification for Mathematical Models
DESCRIPTION:Over the recent years\, various methods based on deep neural networks have been developed and utilized in a wide range of scientific fields. Deep neural networks are highly suitable for analyzing time series or spatial data with complicated dependence structures\, making them particularly useful for environmental sciences and biosciences where such type of simulation model output and observations are prevalent. In this talk\, I will introduce my recent efforts in utilizing various deep learning methods for statistical analysis of mathematical simulations and observational data in those areas\, including surrogate modeling\, parameter estimation\, and long-term trend reconstruction. Various scientific application examples will also be discussed\, including ocean diffusivity estimation\, WRF-hydro calibration\, AMOC reconstruction\, and SIR calibration.  
URL:https://www.ibs.re.kr/bimag/event/2022-06-13-seminar-wonchang/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
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:20260424T095204
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:20220615T160000
DTEND;TZID=Asia/Seoul:20220615T170000
DTSTAMP:20260424T095204
CREATED:20220613T144731Z
LAST-MODIFIED:20220613T144731Z
UID:6188-1655308800-1655312400@www.ibs.re.kr
SUMMARY:Optimized persistent random walk in zebrafish airineme search process
DESCRIPTION:In addition to diffusive signals\, cells in tissue also communicate via long\, thin cellular protrusions\, such as airinemes in zebrafish. Before establishing communication\, cellular protrusions must find their target cell. In this talk\, we demonstrate that the shapes of airinemes in zebrafish are consistent with a persistent random walk model. The probability of contacting the target cell is maximized for a balance between ballistic search (straight) and diffusive search (highly curved\, random). We find that the curvature of airinemes in zebrafish\, extracted from live cell microscopy\, is approximately the same value as the optimum in the simple persistent random walk model. We also explore the ability of the target cell to infer direction of the airineme’s source\, finding that there is a theoretical trade-off between search optimality and directional information. This provides a framework to characterize the shape\, and performance objectives\, of non-canonical cellular protrusions in general.
URL:https://www.ibs.re.kr/bimag/event/2022-06-15-seminar-hjkim/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220613T160000
DTEND;TZID=Asia/Seoul:20220613T170000
DTSTAMP:20260424T095204
CREATED:20220612T220000Z
LAST-MODIFIED:20220529T114627Z
UID:6088-1655136000-1655139600@www.ibs.re.kr
SUMMARY:Dynamical System Perspective for Machine Learning
DESCRIPTION:Abstract: The connection between deep neural networks and ordinary differential equations (ODEs) is an active field of research in machine learning. In this talk\, we view the hidden states of a neural network as a continuous object governed by a dynamical system. The underlying vector field is written using a dictionary representation motivated by the equation discovery method. Within this framework\, we develop models for two particular machine learning tasks: time-series classification and dimension reduction. We train the parameters in the models by minimizing a loss\, which is defined using the solution to the governing ODE. To attain a regular vector field\, we introduce a regularization term measuring the mean total kinetic energy of the flow\, which is motivated by optimal transportation theory. We solve the optimization problem using a gradient-based method where the gradients are computed via the adjoint method from optimal control theory. Through various experiments on synthetic and real-world datasets\, we demonstrate the performance of the proposed models. We also interpret the learned models by visualizing the phase plots of the underlying vector field and solution trajectories.  \n 
URL:https://www.ibs.re.kr/bimag/event/2022-06-13-sem/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220610T130000
DTEND;TZID=Asia/Seoul:20220610T140000
DTSTAMP:20260424T095204
CREATED:20220530T075825Z
LAST-MODIFIED:20220530T075825Z
UID:6133-1654866000-1654869600@www.ibs.re.kr
SUMMARY:Phase Estimation of Nonlinear State-space Model of the Circadian Pacemaker Using Level Set Kalman Filter and Raw Wearable Data
DESCRIPTION:Abstract: \nCircadian rhythm is a robust internal 24 hours timekeeping mechanism maintained by the master circadian pacemaker Suprachiasmatic Nuclei (SCN). Numerous mathematical models have been proposed to capture SCN’s timekeeping mechanism and predict the circadian phase. There has been an increased demand for applying these models to the various unexplored data sets. One potential application is on data from commercially available wearable devices\, which provide the noninvasive measurements of physiological proxies\, such as activity and heart rate. Using these physiological proxies\, we can estimate the circadian phase of the central and peripheral circadian pacemakers. Here\, we propose a new framework for estimating the circadian phase using wearable data and the Level Set Kalman Filter on the nonlinear state-space model of the human circadian pacemaker. Analysis of over 200\,000 days of wearable data from over 3\,000 subjects using our framework successfully identified misalignment in central and peripheral pacemakers with a significantly smaller uncertainty than previous methods.
URL:https://www.ibs.re.kr/bimag/event/2022-06-10-sem/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
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:20260424T095204
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:20220602T160000
DTEND;TZID=Asia/Seoul:20220602T170000
DTSTAMP:20260424T095204
CREATED:20220520T122202Z
LAST-MODIFIED:20220520T122202Z
UID:6028-1654185600-1654189200@www.ibs.re.kr
SUMMARY:Introduction to matrix and tensor factorization models and related stochastic nonconvex and constrained optimization algorithms
DESCRIPTION:Abstract. Matrix/tensor factorization models such as principal component analysis \, nonnegative matrix factorization\, and CANDECOM/PARAFAC tensor decomposition provide powerful framework for dimension reduction and interpretable feature extraction\, which are important in analyzing high-dimensional data that comes in large volume. Their diverse applications include image denoising and reconstruction\, dictionary learning\, topic modeling\, and network data analysis. Fitting such factorization models to training data gives rise to various nonconvex and constrained optimization algorithms. Moreover\, such models can be trained efficiently for streaming data using stochastic/online versions of such algorithms. After introducing matrix/tensor factorization models and their applications in various contexts\, we survey some well-known nonconvex constrained optimization algorithms such as block coordinate descent and projected gradient descent. We also discuss some recent developments in general stochastic optimization algorithms such as stochastic proximal gradient descent and stochastic regularized majorization-minimization and their convergence and complexity guarantees under general Markovian streaming data.
URL:https://www.ibs.re.kr/bimag/event/2022-06-02-sem/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220601T170000
DTEND;TZID=Asia/Seoul:20220601T180000
DTSTAMP:20260424T095204
CREATED:20220531T223000Z
LAST-MODIFIED:20220317T001720Z
UID:5601-1654102800-1654106400@www.ibs.re.kr
SUMMARY:From live cell imaging to moment-based variational inference
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: Quantitative characterization of biomolecular networks is important for the analysis and design of network functionality. Reliable models of such networks need to account for intrinsic and extrinsic noise present in the cellular environment. Stochastic kinetic models provide a principled framework for developing quantitatively predictive tools in this scenario. Calibration of such models requires an experimental setup capable of monitoring a large number of individual cells over time\, automatic extraction of fluorescence levels for each cell and a scalable inference approach. In the first part of the talk we will cover our microfluidic setup and a deep-learning based approach to cell segmentation and data extraction. The second part will introduce moment-based variational inference as a scalable framework for approximate inference of kinetic models based on single cell data.
URL:https://www.ibs.re.kr/bimag/event/2022-06-01/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/HK_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220525T170000
DTEND;TZID=Asia/Seoul:20220525T180000
DTSTAMP:20260424T095204
CREATED:20220524T230000Z
LAST-MODIFIED:20220224T003504Z
UID:5609-1653498000-1653501600@www.ibs.re.kr
SUMMARY:Multi-resolution methods for modelling intracellular processes
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: I will discuss the development\, analysis and applications of multi-resolution methods for spatio-temporal modelling of intracellular processes\, which use (detailed) Brownian dynamics or molecular dynamics simulations in localized regions of particular interest (in which accuracy and microscopic details are important) and a (less-detailed) coarser model in other regions in which accuracy may be traded for simulation efficiency. I will discuss the error analysis and convergence properties of the developed multi-resolution methods\, their software implementation and applications of these multiscale methodologies to modelling of intracellular calcium dynamics\, actin dynamics and DNA dynamics. I will also discuss the development of multiscale methods which couple molecular dynamics and coarser stochastic models in the same dynamic simulation.
URL:https://www.ibs.re.kr/bimag/event/2022-05-25-2/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/RE_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220525T163000
DTEND;TZID=Asia/Seoul:20220525T170000
DTSTAMP:20260424T095204
CREATED:20220524T223000Z
LAST-MODIFIED:20220224T003158Z
UID:5606-1653496200-1653498000@www.ibs.re.kr
SUMMARY:Stochastic modelling of reaction-diffusion processes
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: I will introduce mathematical and computational methods for spatio-temporal modelling in molecular and cell biology\, including all-atom and coarse-grained molecular dynamics (MD)\, Brownian dynamics (BD)\, stochastic reaction-diffusion models and macroscopic mean-field equations. Microscopic (BD\, MD) models are based on the simulation of trajectories of individual molecules and their localized interactions (for example\, reactions). Mesoscopic (lattice-based) stochastic reaction-diffusion approaches divide the computational domain into a finite number of compartments and simulate the time evolution of the numbers of molecules in each compartment\, while macroscopic models are often written in terms of mean-field reaction-diffusion partial differential equations for spatially varying concentrations.
URL:https://www.ibs.re.kr/bimag/event/2022-05-25-1/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/RE_250x250.png
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:20260424T095204
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:20220512T110000
DTEND;TZID=Asia/Seoul:20220512T120000
DTSTAMP:20260424T095204
CREATED:20220511T170000Z
LAST-MODIFIED:20220224T002809Z
UID:5599-1652353200-1652356800@www.ibs.re.kr
SUMMARY:Plasticity and balance in neuronal networks
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: I will first describe how to extend the theory of balanced networks to account for synaptic plasticity. This theory can be used to show when a plastic network will maintain balance\, and when it will be driven into an unbalanced state. I will next discuss how this approach provides evidence for a novel form of rapid compensatory inhibitory plasticity. Experimental evidence for such plasticity comes from optogenetic activation of excitatory neurons in primate visual cortex (area V1) which induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state\, but not during rest. I will shift gears in the final part of the talk\, and discuss how community detection algorithms can help uncover the large scale organization of neuronal networks from connectome data.
URL:https://www.ibs.re.kr/bimag/event/2022-05-12-2/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/03/KJ_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220512T103000
DTEND;TZID=Asia/Seoul:20220512T110000
DTSTAMP:20260424T095204
CREATED:20220511T163000Z
LAST-MODIFIED:20220224T002732Z
UID:5596-1652351400-1652353200@www.ibs.re.kr
SUMMARY:Introduction to balanced networks
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: The idea of balance between excitation and inhibition is central in the theory of biological neural networks.  I will give a brief introduction to the concept of such balance\, and an overview of the mathematical ideas that can be used to study it.
URL:https://www.ibs.re.kr/bimag/event/2022-05-12-1/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/03/KJ_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR