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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:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210728T170000
DTEND;TZID=Asia/Seoul:20210728T180000
DTSTAMP:20260425T044824
CREATED:20210407T040301Z
LAST-MODIFIED:20210717T235315Z
UID:4383-1627491600-1627495200@www.ibs.re.kr
SUMMARY:Theory and design of molecular integral feedback controllers
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)\nAbstract: \nHomeostasis is a recurring theme in biology that ensures that regulated variables robustly adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control\, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation. Despite its benefits\, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. In this talk I will show that there is a single fundamental biomolecular controller topology that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept\, I will describe a genetically engineered synthetic integral feedback controller in living cells and demonstrate its tunability and adaptation properties. A growth-rate control application in Escherichia coli shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. These results provide conceptual and practical tools in the area of cybergenetics\, for engineering synthetic controllers that steer the dynamics of living systems.
URL:https://www.ibs.re.kr/bimag/event/2021-07-28/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/04/MustafaKhammash_profile-e1617768310550.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210723T150000
DTEND;TZID=Asia/Seoul:20210723T160000
DTSTAMP:20260425T044824
CREATED:20210629T013222Z
LAST-MODIFIED:20210629T013222Z
UID:4686-1627052400-1627056000@www.ibs.re.kr
SUMMARY:Scalable Modeling Approaches in Systems Immunology
DESCRIPTION:Abstract: \nSystems biology seeks to build quantitative predictive models of biological system behavior. Biological systems\, such as the mammalian immune system\, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus\, mechanistic\, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. First\, we aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions\, we developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high-dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next\, we sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal\, we built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells\, followed by experimental validation. Moreover\, by bridging these models across organizational scales\, we derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells\, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. We propose that our framework and predictions may help guide therapeutic manipulation of immune homeostasis to treat cancer and autoimmune diseases. \n  \nReferences: \nPark\, K.\, Prüstel\, T.\, Lu\, Y.\, and Tsang\, J.S. (2019). Machine learning of stochastic gene network phenotypes. BioRxiv 825943. \nWong\, H.S.\, Park\, K.\, Gola\, A.\, Baptista\, A.P.\, Miller\, C.H.\, Deep\, D.\, Lou\, M.\, Boyd\, L.F.\, Rudensky\, A.Y.\, Savage\, P.A.\, et al. (2021). A local regulatory T cell feedback circuit maintains immune homeostasis by pruning self-activated T cells. Cell S0092867421006589.
URL:https://www.ibs.re.kr/bimag/event/2021-07-23/
LOCATION:B305 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:20210723T110000
DTEND;TZID=Asia/Seoul:20210723T120000
DTSTAMP:20260425T044824
CREATED:20210707T160416Z
LAST-MODIFIED:20210707T160416Z
UID:4715-1627038000-1627041600@www.ibs.re.kr
SUMMARY:Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC
DESCRIPTION:Abstract: Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC In this study\, we discuss model robustness. Model robustness is consistent performance over variations of parameters. We formulate a stochastic target-mediated drug (TMDD) model\, one of the pharmacokinetic models\, to capture bi-exponential drug decay in plasma. A stochastic process is used to account for system randomness\, and this process is transformed into system of stochastic differential equations. Parameter inference is performed by Approximation Bayesian Computation using the likelihood-free method. Using these collected samples\, global sensitivity of parameters is compared to Uniform and Normal distributions. This approach in the TMDD model may improve model robustness without changing the global sensitivity of parameters and the model.
URL:https://www.ibs.re.kr/bimag/event/inference-method-for-a-stochastic-target-mediated-drug-disposition-model-via-abc-mcmc/
LOCATION:B305 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:20210722T130000
DTEND;TZID=Asia/Seoul:20210722T140000
DTSTAMP:20260425T044824
CREATED:20210721T190000Z
LAST-MODIFIED:20210726T125353Z
UID:4754-1626958800-1626962400@www.ibs.re.kr
SUMMARY:Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter
DESCRIPTION:We will discuss about “Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter”\, Bonarius et. al.\, IEEE Trans. Biomed. Eng.\, 2021 \nAbstract \nObjective: In the near future\, real-time estimation of peoples unique\, precise circadian clock state has the potential to improve the efficacy of medical treatments and improve human performance on a broad scale. Humancentric lighting can bring circadian-rhythm support using biodynamic lighting solutions that sync light with the time of day. We investigate a method to improve the tracking of individual’s circadian processes. Methods: In literature\, the human circadian physiology has been mathematically modeled using ordinary differential equations\, the state of which can be tracked via the signal processing concept of a Particle Filter. We show that substantial improvements can be made if the estimator not only tracks state variables\, such as the phase and amplitude of the circadian pacemaker\, but also fits specific model parameters to the individual. In particular\, we optimize model parameter τx\, which reflects the intrinsic period of the circadian pacemaker (τ). We show that both state and model parameters can be estimated based on minimally-invasive light exposure measurements and sleep-wake state observations. We also quantify the effect of inaccuracies in sensing. Results: We demonstrate improved performance by estimating τx for every individual\, both with artificially created and human subject data. Prediction accuracy improves with every newly available observation. The estimated τx-s correlate well with the subjects’ chronotypes\, in a similar way as τ correlates. Conclusion: Our results show that individualizing the estimation of model parameters can improve circadian state estimation accuracy. Significance: These findings underscore the potential improvements in personalized models over one-size fits all approaches.
URL:https://www.ibs.re.kr/bimag/event/2021-07-22/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210715T130000
DTEND;TZID=Asia/Seoul:20210715T140000
DTSTAMP:20260425T044824
CREATED:20210713T071946Z
LAST-MODIFIED:20210715T002734Z
UID:4721-1626354000-1626357600@www.ibs.re.kr
SUMMARY:Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions
DESCRIPTION:We will discuss about “Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions”\, Thurley et al.\, Cell Systems\, 2021 \nAbstract: \nCell-to-cell communication networks have critical roles in coordinating diverse organismal processes\, such as tissue development or immune cell response. However\, compared with intracellular signal transduction networks\, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here\, we make use of a framework that treats intracellular signal transduction networks as “black boxes” with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time\, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.
URL:https://www.ibs.re.kr/bimag/event/2021-07-15/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210714T170000
DTEND;TZID=Asia/Seoul:20210714T180000
DTSTAMP:20260425T044824
CREATED:20210406T074701Z
LAST-MODIFIED:20210420T215116Z
UID:4368-1626282000-1626285600@www.ibs.re.kr
SUMMARY:Inference for Circadian Pacemaking
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Organisms have evolved an internal biological clock which allows them to temporally regulate and organize their physiological and behavioral responses to cope in an optimal way with the fundamentally periodic nature of the environment. It is now well established that the molecular genetics of such rhythms within the cell consist of interwoven transcriptional-translational feedback loops involving about 15 clock genes\, which generate circa 24-h oscillations in many cellular functions at cell population or whole organism levels. We will present statistical methods and modelling approaches that address newly emerging large circadian data sets\, namely spatio-temporal gene expression in SCN neurons and rest-activity actigraph data obtained from non-invasive e-monitoring\, both of which provide unique opportunities for furthering progress in understanding the synchronicity of circadian pacemaking and address implications for monitoring patients in chronotherapeutic healthcare.
URL:https://www.ibs.re.kr/bimag/event/2021-07-14/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/04/barbel_finkenstadt_rand_crop-e1617768405446.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210712T100000
DTEND;TZID=Asia/Seoul:20210712T120000
DTSTAMP:20260425T044824
CREATED:20210617T030615Z
LAST-MODIFIED:20210617T030615Z
UID:4658-1626084000-1626091200@www.ibs.re.kr
SUMMARY:Analysis of sleep-wake cycles via machine learning and mathematical modeling
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-07-21/
LOCATION:B305 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:20210709T130000
DTEND;TZID=Asia/Seoul:20210709T140000
DTSTAMP:20260425T044824
CREATED:20210705T061640Z
LAST-MODIFIED:20210705T131643Z
UID:4710-1625835600-1625839200@www.ibs.re.kr
SUMMARY:DeepCME: A deep learning framework for solving the Chemical Master Equation
DESCRIPTION:We will discuss about “DeepCME: A deep learning framework for solving the Chemical Master Equation\,” Gupta et al.\, bioRxiv\, 2021 \nStochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models\, the Kolmogorov’s forward equation is called the chemical master equation (CME)\, and it is a fundamental system of linear ordinary differential equations (ODEs) that describes the evolution of the probability distribution of the random state-vector representing the copy-numbers of all the reacting species. The size of this system is given by the number of states that are accessible by the chemical system\, and for most examples of interest this number is either very large or infinite. Moreover\, approximations that reduce the size of the system by retaining only a finite number of important chemical states (e.g. those with non-negligible probability) result in high-dimensional ODE systems\, even when the number of reacting species is small. Consequently\, accurate numerical solution of the CME is very challenging\, despite the linear nature of the underlying ODEs. One often resorts to estimating the solutions via computationally intensive stochastic simulations. The goal of the present paper is to develop a novel deep-learning approach for solving high-dimensional CMEs by reformulating the stochastic dynamics using Kolmogorov’s backward equation. The proposed method leverages superior approximation properties of Deep Neural Networks (DNNs) and is algorithmically based on reinforcement learning. It only requires a moderate number of stochastic simulations (in comparison to typical simulation-based approaches) to train the “policy function”. This allows not just the numerical approximation of the CME solution but also of its sensitivities to all the reaction network parameters (e.g. rate constants). We provide four examples to illustrate our methodology and provide several directions for future research.
URL:https://www.ibs.re.kr/bimag/event/2021-07-09/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210702T120000
DTEND;TZID=Asia/Seoul:20210702T130000
DTSTAMP:20260425T044824
CREATED:20210507T124154Z
LAST-MODIFIED:20210622T234621Z
UID:4550-1625227200-1625230800@www.ibs.re.kr
SUMMARY:Collective Oscillations in coupled cell systems
DESCRIPTION:We will discuss about “Collective Oscillations in coupled cell systems”\, Chen and Sinh\, Bulletin of Mathematical Biology\, 2021 \nWe investigate oscillations in coupled systems. The methodology is based on the Hopf bifurcation theorem and a condition extended from the Routh–Hurwitz criterion. Such a condition leads to locating the bifurcation values of the parameters. With such an approach\, we analyze a single-cell system modeling the minimal genetic negative feedback loop and the coupled-cell system composed by these single-cell systems. We study the oscillatory properties for these systems and compare these properties between the model with Hill-type repression and the one with protein-sequestration-based repression. As the parameters move from the Hopf bifurcation value for single cells to the one for coupled cells\, we compute the eigenvalues of the linearized systems to obtain the magnitude of the collective frequency when the periodic solution of the coupled-cell system is generated. Extending from this information on the parameter values\, we further compute and compare the collective frequency for the coupled-cell system and the average frequency of the decoupled individual cells. To compare these scenarios with other biological oscillators\, we perform parallel analysis and computations on a segmentation clock model.
URL:https://www.ibs.re.kr/bimag/event/2021-07-02/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210701T110000
DTEND;TZID=Asia/Seoul:20210701T120000
DTSTAMP:20260425T044824
CREATED:20210603T003009Z
LAST-MODIFIED:20210604T082929Z
UID:4605-1625137200-1625140800@www.ibs.re.kr
SUMMARY:Statistical Inference with Neural Network Imputation for Item Nonresponse
DESCRIPTION:Abstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects\, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation\, including variance estimation\, is challenging because the basis for function estimation is estimated rather than known. In this paper\, we tackle the problem of statistical inference with neural network imputation by treating the hidden nodes in a neural network as data-driven basis functions. We prove that the uncertainty in estimating the basis functions can be safely ignored and hence the linearization method for neural network imputation can be greatly simplified. A simulation study confirms that the proposed approach results in efficient and well-calibrated confidence intervals even when classic approaches fail due to severe nonlinearity and complicated interactions.
URL:https://www.ibs.re.kr/bimag/event/2021-07-01/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/06/JKK_profile2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210629T130000
DTEND;TZID=Asia/Seoul:20210629T140000
DTSTAMP:20260425T044824
CREATED:20210607T235505Z
LAST-MODIFIED:20210607T235505Z
UID:4627-1624971600-1624975200@www.ibs.re.kr
SUMMARY:Deciphering circadian clock cell network morphology within the biological master clock\, the suprachiasmatic nucleus
DESCRIPTION:Abstract: The biological master clock\, the suprachiasmatic nucleus (SCN) of a mouse\, contains many (~20\,000) clock cells heterogeneous\, particularly with respect to their circadian period. Despite the inhomogeneity\, within an intact SCN\, they maintain a very high degree of circadian phase coherence\, which is generally rendered visible as system-wide propagating phase waves. The phase coherence is vital for mammals sustaining various circadian rhythmic activities. It is supposedly achieved not by one but a few different cell-to-cell coupling mechanisms: Among others\, action potential (AP)-mediated connectivity is known to be essential. However\, due to technical difficulties and limitations in experiments\, so far\, very little information is available about the (connectome) morphology of the AP-mediated SCN neural connectivity. With that limited amount of information\, here we exhaustively and systematically explore a large (~25\,000) pool of various model network morphologies to come up with the most realistic case for the SCN. All model networks within this pool reflect an actual indegree distribution as well as a physical range distribution of afferent clock cells\, which were acquired in earlier optogenetic connectome experiments. Subsequently\, our network selection scheme is based on a collection of multitude criteria\, testing the properties of SCN circadian phase waves in perturbed (or driven) as well as in their natural states: Key properties include\, 1) degree of phase synchrony (or dispersal) and direction of wave propagation\, 2) entrainability of the model oscillator networks to an external circadian forcing (mimicking the light modulation subject to the geophysical circadian rhythm)\, and 3) emergence of “phase-singularities” following a global perturbation and their decay. The selected network morphologies require several common features that 1) the indegree – outdegree relation must have a positive correlation; 2) the cells in the SCN core region have a larger total (in+out) degree than that of the shell region; 3) core to shell (or shell to core) connections should be much less than core to core (and shell to shell) connections. Taken all together\, our comprehensive test results strongly suggest that degree distribution over the whole SCN is not uniform but position-dependent and raise a question of whether this inhomogeneous degree distribution is related to the distribution of known subpopulations of SCN cells.
URL:https://www.ibs.re.kr/bimag/event/deciphering-circadian-clock-cell-network-morphology-within-the-biological-master-clock-the-suprachiasmatic-nucleus/
LOCATION:B305 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:20210618T130000
DTEND;TZID=Asia/Seoul:20210618T140000
DTSTAMP:20260425T044824
CREATED:20210608T152356Z
LAST-MODIFIED:20210612T121922Z
UID:4635-1624021200-1624024800@www.ibs.re.kr
SUMMARY:Introduction to immersed boundary method for biofluids
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-06-18-2/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/02/SookkyungLim-e1706058905732.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210611T123000
DTEND;TZID=Asia/Seoul:20210611T133000
DTSTAMP:20260425T044824
CREATED:20210507T123416Z
LAST-MODIFIED:20210601T035036Z
UID:4545-1623414600-1623418200@www.ibs.re.kr
SUMMARY:DNA as a universal substrate for chemical kinetics
DESCRIPTION:We will discuss about “DNA as a universal substrate for chemical kinetics “\, Soloveichik et al.\, PNAS (2009) \nMolecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive\, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously\, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator\, a limit-cycle oscillator\, a chaotic system\, and systems implementing feedback digital logic and algorithmic behavior.
URL:https://www.ibs.re.kr/bimag/event/2021-05-27/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210610T110000
DTEND;TZID=Asia/Seoul:20210610T120000
DTSTAMP:20260425T044824
CREATED:20210406T074242Z
LAST-MODIFIED:20210607T080017Z
UID:4364-1623322800-1623326400@www.ibs.re.kr
SUMMARY:Towards individualized predictions of human sleep and circadian timing
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Accurate assessment of circadian timing is critical to many applications\, including timing of drug delivery\, prediction of neurobehavioral performance\, and optimized scheduling of sleep. Current methods for measuring circadian timing are onerous and do not produce results in real time. Mathematical models have been developed for predicting circadian timing from an individual’s light exposure patterns\, which can be applied to passively collected data. These models are now well validated in the field at the group-average level\, but tend to perform poorly at the individual level. One potential solution to this problem is the estimation of model parameters at an individual level. We explored whether this approach could be applied to parameters relating to an individual’s light sensitivity. We found that these parameters can account for inter-individual and intra-individual variation in circadian timing. These findings demonstrate that model parametrization based on physiological measurements of light sensitivity could lead to more accurate individual-level circadian phase prediction.
URL:https://www.ibs.re.kr/bimag/event/2021-06-10/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/04/AndrewPhillips_profile_crop-e1617768455279.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210526T170000
DTEND;TZID=Asia/Seoul:20210526T180000
DTSTAMP:20260425T044824
CREATED:20210311T114629Z
LAST-MODIFIED:20210407T040940Z
UID:4248-1622048400-1622052000@www.ibs.re.kr
SUMMARY:Neural network aided approximation and parameter inference of stochastic models of gene expression
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Non-Markov models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models\, as well as the inference of their parameters from data\, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markov models by the solutions of much simpler time-inhomogeneous Markov models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markov model. We show using a variety of models\, where the delays stem from transcriptional processes and feedback control\, that the Markov models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.
URL:https://www.ibs.re.kr/bimag/event/2021-05-26/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/DjvWsbfJ-e1617756286824.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210520T123000
DTEND;TZID=Asia/Seoul:20210520T133000
DTSTAMP:20260425T044824
CREATED:20210507T123654Z
LAST-MODIFIED:20210507T123746Z
UID:4547-1621513800-1621517400@www.ibs.re.kr
SUMMARY:Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes
DESCRIPTION:We will discuss about “Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes”\, Hempel et. al.\, bioRxiv\, 2021 \nIn order to advance the mission of in silico cell biology\, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success\, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes\, the number of independent or weakly coupled subsystems increases\, and the number of global system states increase exponentially\, making the sampling of all distinct global states unfeasible. In this work\, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology\, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.
URL:https://www.ibs.re.kr/bimag/event/2021-05-20/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210514T110000
DTEND;TZID=Asia/Seoul:20210514T120000
DTSTAMP:20260425T044824
CREATED:20210507T124508Z
LAST-MODIFIED:20210507T124508Z
UID:4555-1620990000-1620993600@www.ibs.re.kr
SUMMARY:Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model
DESCRIPTION:We will discuss about “Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model”\, Ito et. al.\, PloS ONE\, 2011 \nTransfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive\, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic\, as synaptic delays between cortical neurons\, for example\, range from one to tens of milliseconds. In addition\, neurons produce bursts of spikes spanning multiple time bins. To address these issues\, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance\, we applied these extensions of TE to a spiking cortical network model (Izhikevich\, 2006) with known connectivity and a range of synaptic delays. For comparison\, we also investigated single-delay TE\, at a message length of one bin (D1TE)\, and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE\, this dramatically improved to 73% of true connections. In addition\, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE\, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE\, when used on currently available desktop computers\, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
URL:https://www.ibs.re.kr/bimag/event/2021-05-14/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210507T123000
DTEND;TZID=Asia/Seoul:20210507T133000
DTSTAMP:20260425T044824
CREATED:20210503T075749Z
LAST-MODIFIED:20210503T075749Z
UID:4525-1620390600-1620394200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 2
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nThe problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics\, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors. However\, if the underlying graphical model is too complex or the data is in very high dimensions\, then such sampling-based methodologies run into several problems. Variational inference (Jordan et al.\, 1999; Wainwright and Jordan\, 2008) is a family of machine learning methodologies that transforms the problem of approximating posterior densities to an optimization\, which lets us circumvent all such problems. In the first part\, I will introduce the general framework of variational inference and some underlying theory\, accompanied by an illustrative example of LDA (Blei et al.\, 2003). In the second part\, I will introduce some recent works on applying variational inference to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-2/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210430T160000
DTEND;TZID=Asia/Seoul:20210430T170000
DTSTAMP:20260425T044824
CREATED:20210319T021820Z
LAST-MODIFIED:20210412T013739Z
UID:4282-1619798400-1619802000@www.ibs.re.kr
SUMMARY:What is the role of oscillatory signals in intracellular systems?
DESCRIPTION:Oscillatory signals are ubiquitously observed in many different intracellular systems such as immune systems and DNA repair processes. While we know how oscillatory signals are created\, we do not fully understand what a critical role they play to regulate signal pathway systems in cells. Recently by using a stochastic nucleosome system\, we found that a special signal (NFkB signal) in an immune cell can enhance the variability of the immune response to inflammatory stimulations when the signal is oscillatory. Hence in this talk\, we discuss the roles of oscillatory and non-oscillatory NFkB signals in an inflammatory system of immune cells as the main example for revealing the role of oscillatory signals. And then we will talk about how this finding can be generalized for other intra- or extra-cellular systems to study why cells use oscillations.
URL:https://www.ibs.re.kr/bimag/event/2021-04-30/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/Jinsu-Kim-9-e1617756454410.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210429T120000
DTEND;TZID=Asia/Seoul:20210429T130000
DTSTAMP:20260425T044824
CREATED:20210425T180554Z
LAST-MODIFIED:20210425T180554Z
UID:4499-1619697600-1619701200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 1
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nGaussian process(GP) is a stochastic process such that the joint distribution of an arbitrary finite subset of the random variables is a multivariate normal. It plays a fundamental role in Bayesian machine learning as it can be interpreted as a prior over functions (Rasmussen and Williams\, 2006)\, hence providing a nonparametric approach to various tasks. In the first part\, I will introduce the general framework of GP and some underlying theory\, accompanied by an illustrative example of GP regression\, also known as Kringing. In the second part\, I will introduce some recent works on applying GP to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-1/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210422T120000
DTEND;TZID=Asia/Seoul:20210422T130000
DTSTAMP:20260425T044824
CREATED:20210417T101617Z
LAST-MODIFIED:20210419T021327Z
UID:4477-1619092800-1619096400@www.ibs.re.kr
SUMMARY:A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics
DESCRIPTION:We will discuss about “A Simple and Flexible Computational Framework for\nInferring Sources of Heterogeneity from Single-Cell\nDynamics”\, Dharmarajan et al.\, Cell Systems (2019) \nSingle-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability\, a key step for understanding heterogeneity in cell populations. However\, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple\, flexible\, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks\, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics
URL:https://www.ibs.re.kr/bimag/event/2021-04-22/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210421T170000
DTEND;TZID=Asia/Seoul:20210421T183000
DTSTAMP:20260425T044824
CREATED:20210324T050549Z
LAST-MODIFIED:20210421T074343Z
UID:4307-1619024400-1619029800@www.ibs.re.kr
SUMMARY:Advice to my younger self
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nFacebook live streaming: https://www.facebook.com/10226475900150025/videos/10226475902790091 \nAge brings the benefit of experience and looking back at my job as a professor\, there are a couple of things that fall into the category “I wish someone had told me that earlier”. In this seminar\, I would like to share some of the things I learned and which\, I hope\, will be useful for younger scientists. \nThe questions I will touch upon include \n\n\n\nWhat is productivity\, for a scientist?\nWhat are qualities of successful people?\nHow can one create motivation and success?\nHow to organize myself? (project management; getting things done)\nHow to communicate effectively?\nSeeking fulfillment\n\n\n\nThe seminar is targeted at PhD students\, postdocs\, and junior group leaders. \n \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-21/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/olaf-wolkenhauer-e1617756681631.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210416T120000
DTEND;TZID=Asia/Seoul:20210416T130000
DTSTAMP:20260425T044824
CREATED:20210412T110458Z
LAST-MODIFIED:20210412T110458Z
UID:4423-1618574400-1618578000@www.ibs.re.kr
SUMMARY:Synthetic multistability in mammalian cells
DESCRIPTION:We will discuss about “Synthetic multistability in mammalian cells”\, Zhu et al.\, bioRxiv (2021) \nIn multicellular organisms\, gene regulatory circuits generate thousands of molecularly distinct\, mitotically heritable states\, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require interactions among cells in distinct states. Here we introduce MultiFate\, a naturally-inspired\, synthetic circuit that supports long-term\, controllable\, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design\, we engineered MultiFate circuits that generate up to seven states\, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs\, and can be easily expanded with additional transcription factors. Together\, these results provide a foundation for engineering multicellular behaviors in mammalian cells. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-16/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210415T170000
DTEND;TZID=Asia/Seoul:20210415T173000
DTSTAMP:20260425T044824
CREATED:20210411T124935Z
LAST-MODIFIED:20210412T012752Z
UID:4429-1618506000-1618507800@www.ibs.re.kr
SUMMARY:Practical considerations for measuring the effective reproductive number
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-04-15_2/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/04/SHC_profile2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210415T163000
DTEND;TZID=Asia/Seoul:20210415T170000
DTSTAMP:20260425T044824
CREATED:20210411T124649Z
LAST-MODIFIED:20210412T013301Z
UID:4426-1618504200-1618506000@www.ibs.re.kr
SUMMARY:Mathematical modeling for infectious disease using epidemiological data
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-04-15_1/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/04/HJL_profile5.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210415T110000
DTEND;TZID=Asia/Seoul:20210415T120000
DTSTAMP:20260425T044824
CREATED:20210314T044747Z
LAST-MODIFIED:20210412T021311Z
UID:4258-1618484400-1618488000@www.ibs.re.kr
SUMMARY:Dynamics-based data science in biology
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Life science has been a prosperous subject for a long time\, and is still developing with high speed now. One of its major aims is to study the mechanisms of various biological processes on the basis of biological big-data. Many statistics-based methods have been proposed to catch the essence by mining those data\, including the popular category classification\, variables regression\, group clustering\, statistical comparison\, dimensionality reduction\, and component analysis\, which\, however\, mainly elucidate static features or steady behavior of living organisms due to lack of temporal data. But\, a biological system is inherently dynamic\, and with increasingly accumulated time-series data\, dynamics-based approaches based on physical and biological laws are demanded to reveal dynamic features or complex behavior of biological systems. In this talk\, I will present a new concept “dynamics-based data science” and the approaches for studying dynamical bio-processes\, including dynamical network biomarkers (DNB)\, autoreservoir neural networks (ARNN) and partical cross-mapping. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamics-based data-driven approaches as explicable\, quantifiable\, and generalizable. In particular\, dynamics-based data science approaches exploit the essential features of dynamical systems in terms of data\, e.g. strong fluctuations near a bifurcation point\, low-dimensionality of a center manifold or an attractor\, and phase-space reconstruction from a single variable by delay embedding theorem\, and thus are able to provide different or additional information to the traditional approaches\, i.e. statistics-based data science approaches. The dynamical-based data science approaches will further play an important role in the systematical research of biology and medicine in future.
URL:https://www.ibs.re.kr/bimag/event/2021-04-15/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/LC_profile2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210409T120000
DTEND;TZID=Asia/Seoul:20210409T130000
DTSTAMP:20260425T044824
CREATED:20210323T105030Z
LAST-MODIFIED:20210407T041048Z
UID:4304-1617969600-1617973200@www.ibs.re.kr
SUMMARY:Highly accurate fluorogenic DNA sequencing with information theory–based error correction
DESCRIPTION:We will discuss about “Highly accurate fluorogenic DNA sequencing with information theory–based error correction”\, Chen et al.\, Nature Biotechnology (2017) \nEliminating errors in next-generation DNA sequencing has proved challenging. Here we present error-correction code (ECC) sequencing\, a method to greatly improve sequencing accuracy by combining fluorogenic sequencing-by-synthesis (SBS) with an information theory–based error-correction algorithm. ECC embeds redundancy in sequencing reads by creating three orthogonal degenerate sequences\, generated by alternate dual-base reactions. This is similar to encoding and decoding strategies that have proved effective in detecting and correcting errors in information communication and storage. We show that\, when combined with a fluorogenic SBS chemistry with raw accuracy of 98.1%\, ECC sequencing provides single-end\, error-free sequences up to 200 bp. ECC approaches should enable accurate identification of extremely rare genomic variations in various applications in biology and medicine. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-09/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210401T120000
DTEND;TZID=Asia/Seoul:20210401T130000
DTSTAMP:20260425T044824
CREATED:20210331T003338Z
LAST-MODIFIED:20210406T075108Z
UID:4352-1617278400-1617282000@www.ibs.re.kr
SUMMARY:Yun Min Song\, A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light
DESCRIPTION:We will discuss about “A stochastic oscillator model simulates the entrainment of vertebrate cellular clocks by light”\, Kumpost et al.\, bioRxiv (2021) \nThe circadian clock is a cellular mechanism that synchronizes various biological processes with respect to the time of the day. While much progress has been made characterizing the molecular mechanisms underlying this clock\, it is less clear how external light cues influence the dynamics of the core clock mechanism and thereby entrain it with the light-dark cycle. Zebrafish-derived cell cultures possess clocks that are directly light-entrainable\, thus providing an attractive laboratory model for circadian entrainment. Here\, we have developed a stochastic oscillator model of the zebrafish circadian clock\, which accounts for the core clock negative feedback loop\, light input\, and the proliferation of single-cell oscillator noise into population-level luminescence recordings. The model accurately predicts the entrainment dynamics observed in bioluminescent clock reporter assays upon exposure to a wide range of lighting conditions. Furthermore\, we have applied the model to obtain refitted parameter sets for cell cultures exposed to a variety of pharmacological treatments and predict changes in single-cell oscillator parameters. Our work paves the way for model-based\, large-scale screens for genetic or pharmacologically-induced modifications to the entrainment of circadian clock function.
URL:https://www.ibs.re.kr/bimag/event/2021-04-02/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210325T110000
DTEND;TZID=Asia/Seoul:20210325T120000
DTSTAMP:20260425T044824
CREATED:20210301T013812Z
LAST-MODIFIED:20210406T075105Z
UID:4167-1616670000-1616673600@www.ibs.re.kr
SUMMARY:Daniel Forger\, The mathematics of the wearables with applications to circadian rhythms and sleep
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Millions of individuals track their steps\, heart rate\, and other physiological signals through wearables. This data scale is unprecedented; I will describe several of our apps and ongoing studies\, each of which collects wearable and mobile data from thousands of users\, even in > 100 countries. This data is so noisy that it often seems unusable and in desperate need of new mathematical techniques to extract key signals used in the (ode) mathematical modeling typically done in mathematical biology. I will describe several techniques we have developed to analyze this data and simulate models\, including gap orthogonalized least squares\, a new ansatz for coupled oscillators\, which is similar to the popular ansatz by Ott and Antonsen\, but which gives better fits to biological data and a new level-set Kalman Filter that can be used to simulate population densities. My focus applications will be determining the phase of circadian rhythms\, the scoring of sleep and the detection of COVID with wearables.
URL:https://www.ibs.re.kr/bimag/event/2021-03-25/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/dannyg.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210322T100000
DTEND;TZID=Asia/Seoul:20210322T110000
DTSTAMP:20260425T044824
CREATED:20210315T062250Z
LAST-MODIFIED:20210406T075215Z
UID:4261-1616407200-1616410800@www.ibs.re.kr
SUMMARY:Dae Wook Kim\, Revealing causes of disrupted wake-sleep cycles using mathematical model (BRIC Webinar)
DESCRIPTION:Registration is required to attend this talk (link: https://www.ibric.org/seminar/#)\, and it will be presented in Korean. \nAbstract: 생체 시계 (Circadian clock)를 구성하는 핵심 단백질인 PERIOD (PER)의 양은 12시간 동안 증가했다가 12시간 동안 감소하며 24시간 주기로 변화한다. 이 24시간 주기의 PER 리듬이 우리 몸의 시계 역할을 하여 수면 시간 등 다양한 행동 및 생리 작용의 시간을 결정한다. PER의 24시간 주기 리듬 생성 원리는 2017년 노벨생리의학상을 수상한 마이클 영\, 제프리 홀 그리고 마이클 로스배시 교수에 의해서 밝혀졌다. 12시간 동안 세포질에서 축적된 PER 단백질은 세포 핵 안으로 들어가 스스로 PER 유전자의 전사 (Transcription)를 방해함으로써 12시간 동안 PER 단백질의 양을 감소 시킨다. 하지만 12시간 동안 다른 시간에 생산된 수 천개의 PER 분자들이 어떻게 매일 같은 시간에 핵 안으로 들어가는지는 생체시계 분야의 큰 난제였다. \n본 연구에서는 PER 단백질의 세포 내 움직임을 묘사하는 시공간적 확률론적 모형을 개발하여 분석함으로써 이 난제를 해결하였다. 구체적으로\, PER 단백질이 핵에 들어가는데 필요한 인산화가 핵 주변에서 PER 단백질의 농도가 충분히 높을 때에만 발생함을 밝혔다. 이러한 PER 인산화의 동기화 덕분에 수천 개의 PER 단백질이 매일 일정한 시간에 함께 핵 안으로 들어갈 수 있었고 안정적인 24시간 주기의 일주기 리듬 (Circadian rhythms)과 수면 사이클을 유지할 수 있었던 것이다. \n이러한 핵 주변에서 PER인산화 동기화가 발생하기 위해서는 핵 주변으로 PER이 충분히 응축되어야 한다. 하지만\, 세포 내 환경이 과도하게 혼잡해져 PER 분자의 움직임이 크게 방해를 받으면 PER이 충분히 응축되지 않고 PER 인산화 동기화가 발생하지 않게 된다. 그 결과\, PER이 핵 안으로 들어가는 시간이 불규칙해져 일주기 리듬과 수면 사이클이 불안정해진다. \n이러한 수리 모델 예측은 미국 플로리다 주립대학 이주곤 교수 팀과의 협업을 통해 실험으로 검증하였다. 이는 세포질 혼잡 (Cytoplasmic trafficking)을 유발하는 것으로 알려진 비만\, 치매\, 노화가 어떻게 수면 질환을 유발하는지에 대한 메커니즘과 더불어 새로운 수면 장애 치료법을 제시한다. \n 
URL:https://www.ibs.re.kr/bimag/event/2021-03-22/
CATEGORIES:Biomedical Mathematics Seminar,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/DaeWookKim_profile.jpg
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