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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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TZOFFSETTO:+0900
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DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210702T120000
DTEND;TZID=Asia/Seoul:20210702T130000
DTSTAMP:20260427T140321
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:20210709T130000
DTEND;TZID=Asia/Seoul:20210709T140000
DTSTAMP:20260427T140321
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:20210715T130000
DTEND;TZID=Asia/Seoul:20210715T140000
DTSTAMP:20260427T140321
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:20210722T130000
DTEND;TZID=Asia/Seoul:20210722T140000
DTSTAMP:20260427T140321
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:20210730T130000
DTEND;TZID=Asia/Seoul:20210730T140000
DTSTAMP:20260427T140321
CREATED:20210726T125859Z
LAST-MODIFIED:20210726T125859Z
UID:4785-1627650000-1627653600@www.ibs.re.kr
SUMMARY:Stochastic reaction networks in dynamic compartment populations
DESCRIPTION:We will discuss about “Stochastic reaction networks in dynamic compartment populations”\, Duso and Zechner\, PNAS\, 2020 \nAbstract: Compartmentalization of biochemical processes underlies all biological systems\, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse\, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems\, but a general and sufficiently effective approach remains lacking. In this work\, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes\, including subcellular compartmentalization and tissue homeostasis.
URL:https://www.ibs.re.kr/bimag/event/2021-07-30/
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
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