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  • Introduction to Bayesian ML/DL, with Application to Parameter Inference of Coupled Non-linear ODEs – Part 2

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    In this talk, the speaker will present introductory materials about Bayesian Machine Learning. Abstract The 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. …

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  • Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model", Ito et. al., PloS ONE, 2011 Transfer 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 …

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  • Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes", Hempel et. al., bioRxiv, 2021 In 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 …

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  • Neural network aided approximation and parameter inference of stochastic models of gene expression

    Livestream
    ZOOM ID: 709 120 4849 (ibsbimag) (pw: 1234)

    This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) Abstract: 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 …

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  • Towards individualized predictions of human sleep and circadian timing

    Livestream
    ZOOM ID: 709 120 4849 (ibsbimag) (pw: 1234)

    This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) Abstract: 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 …

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  • DNA as a universal substrate for chemical kinetics

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "DNA as a universal substrate for chemical kinetics ", Soloveichik et al., PNAS (2009) Molecular 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 …

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  • Deciphering circadian clock cell network morphology within the biological master clock, the suprachiasmatic nucleus

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    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 …

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  • Statistical Inference with Neural Network Imputation for Item Nonresponse

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    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 …

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  • Collective Oscillations in coupled cell systems

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about "Collective Oscillations in coupled cell systems", Chen and Sinh, Bulletin of Mathematical Biology, 2021 We 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 …

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  • DeepCME: A deep learning framework for solving the Chemical Master Equation

    B305 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

    We will discuss about “DeepCME: A deep learning framework for solving the Chemical Master Equation,” Gupta et al., bioRxiv, 2021 Stochastic 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 …

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