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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221104T150000
DTEND;TZID=Asia/Seoul:20221104T170000
DTSTAMP:20260426T062448
CREATED:20220930T035218Z
LAST-MODIFIED:20221030T231656Z
UID:6648-1667574000-1667581200@www.ibs.re.kr
SUMMARY:Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach
DESCRIPTION:We will discuss about “Model Reduction for the Chemical Master Equation: an Information-Theoretic Approach”\, Öcal\, Kaan\, Guido Sanguinetti\, and Ramon Grima.\, arXiv preprint arXiv:2210.05329 (2022). \nAbstract: \nThe complexity of mathematical models in biology has rendered model reduction an essential tool in the quantitative biologist’s toolkit. For stochastic reaction networks described using the Chemical Master Equation\, commonly used methods include time-scale separation\, the Linear Mapping Approximation and state-space lumping. Despite the success of these techniques\, they appear to be rather disparate and at present no general-purpose approach to model reduction for stochastic reaction networks is known. In this paper we show that most common model reduction approaches for the Chemical Master Equation can be seen as minimising a well-known information-theoretic quantity between the full model and its reduction\, the Kullback-Leibler divergence defined on the space of trajectories. This allows us to recast the task of model reduction as a variational problem that can be tackled using standard numerical optimisation approaches. In addition we derive general expressions for the propensities of a reduced system that generalise those found using classical methods. We show that the Kullback-Leibler divergence is a useful metric to assess model discrepancy and to compare different model reduction techniques using three examples from the literature: an autoregulatory feedback loop\, the Michaelis-Menten enzyme system and a genetic oscillator.
URL:https://www.ibs.re.kr/bimag/event/2022-11-04-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221111T150000
DTEND;TZID=Asia/Seoul:20221111T170000
DTSTAMP:20260426T062448
CREATED:20221028T015855Z
LAST-MODIFIED:20221107T064232Z
UID:6740-1668178800-1668186000@www.ibs.re.kr
SUMMARY:PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
DESCRIPTION:We will discuss about “PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations”\,\nZhong\, Weiheng\, and Hadi Meidani\, Computer Methods in Applied Mechanics and Engineering 403 (2023): 115664. \nAbstract\nWe propose a new class of physics-informed neural networks\, called the Physics-Informed Variational Auto-Encoder (PI-VAE)\, to solve stochastic differential equations (SDEs) or inverse problems involving SDEs. In these problems the governing equations are known but only a limited number of measurements of system parameters are available. PI-VAE consists of a variational autoencoder (VAE)\, which generates samples of system variables and parameters. This generative model is integrated with the governing equations. In this integration\, the derivatives of VAE outputs are readily calculated using automatic differentiation\, and used in the physics-based loss term. In this work\, the loss function is chosen to be the Maximum Mean Discrepancy (MMD) for improved performance\, and neural network parameters are updated iteratively using the stochastic gradient descent algorithm. We first test the proposed method on approximating stochastic processes. Then we study three types of problems related to SDEs: forward and inverse problems together with mixed problems where system parameters and solutions are simultaneously calculated. The satisfactory accuracy and efficiency of the proposed method are numerically demonstrated in comparison with physics-informed Wasserstein generative adversarial network (PI-WGAN).
URL:https://www.ibs.re.kr/bimag/event/2022-11-11-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:20221118T150000
DTEND;TZID=Asia/Seoul:20221118T170000
DTSTAMP:20260426T062448
CREATED:20221117T034958Z
LAST-MODIFIED:20221117T034958Z
UID:6871-1668783600-1668790800@www.ibs.re.kr
SUMMARY:Detecting critical state before phase transition of complex biological systems by hidden Markov model
DESCRIPTION:We will discuss about “Detecting critical state before phase transition of complex biological systems by hidden Markov model”\, Chen\, Pei\, et al. Bioinformatics 32.14 (2016): 2143-2150. \n  \nAbstract \nMotivation: Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task\, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages\, i.e. before-transition state\, pre-transition state and after-transition state\, which can be considered as three different Markov processes. \nResults: By exploring the rich dynamical information provided by high-throughput data\, we present a novel computational method\, i.e. hidden Markov model (HMM) based approach\, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process)\, thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness\, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets\, and further identify the pre-transition states as well as their critical modules for three real datasets\, i.e. the acute lung injury triggered by phosgene inhalation\, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results.
URL:https://www.ibs.re.kr/bimag/event/2022-11-18-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
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