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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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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230901T100000
DTEND;TZID=Asia/Seoul:20230901T120000
DTSTAMP:20260425T105135
CREATED:20230810T082738Z
LAST-MODIFIED:20230831T040832Z
UID:8236-1693562400-1693569600@www.ibs.re.kr
SUMMARY:Hyeongjun Jang\, Generalized Michaelis–Menten rate law with time-varying molecular concentrations
DESCRIPTION:We will discuss about “Generalized Michaelis–Menten rate law with time-varying molecular concentrations”\, Lim\, Roktaek\, et al.\,bioRxiv (2022): 2022-01 \n  \nAbstract \n\n\n\n\n\n\nThe Michaelis–Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry\, biophysics\, cell biology\, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules\, at each moment\, approaches an equilibrium much faster than the molecular concentrations change. Yet\, this assumption is not always justified. Here\, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations\, termed the effective time-delay scheme (ETS)\, is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein– protein and protein–DNA interaction modeling\, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover)\, which goes beyond the quasi-steady state assumption.
URL:https://www.ibs.re.kr/bimag/event/2023-09-01-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:20230908T140000
DTEND;TZID=Asia/Seoul:20230908T160000
DTSTAMP:20260425T105135
CREATED:20230829T100233Z
LAST-MODIFIED:20230907T044351Z
UID:8369-1694181600-1694188800@www.ibs.re.kr
SUMMARY:Dongju Lim\, Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics
DESCRIPTION:We will discuss about “Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics”\, Wang\, Yiling\, et al.\, bioRxiv (2023): 2023-08. \n  \nAbstract \n\n\n\n\n\n\nThe classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching\, transcription\, translation\, degradation and dilution. While this model is easily simulated\, its analytical solution remains an unsolved problem. Here we modify this model to explicitly include cell-cycle dynamics and then derive an exact solution for the time-dependent joint distribution of mRNA and protein numbers. We show large differences between this model and the classical model which captures cell-cycle effects implicitly via effective first-order dilution reactions. In particular we find that the Fano factor of protein numbers calculated from a population snapshot measurement are underestimated by the classical model whereas the correlation between mRNA and protein can be either over- or underestimated\, depending on the timescales of mRNA degradation and promoter switching relative to the mean cell-cycle duration time. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-09-08-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:20230915T140000
DTEND;TZID=Asia/Seoul:20230915T160000
DTSTAMP:20260425T105135
CREATED:20230829T100538Z
LAST-MODIFIED:20230914T051626Z
UID:8371-1694786400-1694793600@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks
DESCRIPTION:We will discuss about “Antithetic proportional-integral feedback for reduced variance and improved control performance of stochastic reaction networks\n”\,Briat\, Corentin\, Ankit Gupta\, and Mustafa Khammash.\, Journal of The Royal Society Interface 15.143 (2018): 20180079 \nAbstract \n\n\n\n\n\n\n\nThe ability of a cell to regulate and adapt its internal state in response to unpredictable environmental changes is called homeostasis and this ability is crucial for the cell’s survival and proper functioning. Understanding how cells can achieve homeostasis\, despite the intrinsic noise or randomness in their dynamics\, is fundamentally important for both systems and synthetic biology. In this context\, a significant development is the proposed antithetic integral feedback (AIF) motif\, which is found in natural systems\, and is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. From the standpoint of applications\, one drawback of this motif is that it often leads to an increased cell-to-cell heterogeneity or variance when compared to a constitutive (i.e. open-loop) control strategy. Our goal in this paper is to show that this performance deterioration can be countered by combining the AIF motif and a negative feedback strategy. Using a tailored moment closure method\, we derive approximate expressions for the stationary variance for the controlled network that demonstrate that increasing the strength of the negative feedback can indeed decrease the variance\, sometimes even below its constitutive level. Numerical results verify the accuracy of these results and we illustrate them by considering three biomolecular networks with two types of negative feedback strategies. Our computational analysis indicates that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time.
URL:https://www.ibs.re.kr/bimag/event/2023-09-15-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:20230922T140000
DTEND;TZID=Asia/Seoul:20230922T160000
DTSTAMP:20260425T105135
CREATED:20230901T091012Z
LAST-MODIFIED:20230906T083720Z
UID:8440-1695391200-1695398400@www.ibs.re.kr
SUMMARY:Yun Min Song\, A data-driven approach for timescale decomposition of biochemical reaction networks
DESCRIPTION:We will discuss about “A data-driven approach for timescale decomposition of biochemical reaction networks”\, Amir Akbari\, Zachary B. Haiman\, Bernhard O. Palsson\, bioRxiv (2023) \nAbstract \n\nUnderstanding the dynamics of biological systems in evolving environments is a challenge due to their scale and complexity. Here\, we present a computational framework for timescale decomposition of biochemical reaction networks to distill essential patterns from their intricate dynamics. This approach identifies timescale hierarchies\, concentration pools\, and coherent structures from time-series data\, providing a system-level description of reaction networks at physiologically important timescales. We apply this technique to kinetic models of hypothetical and biological pathways\, validating it by reproducing analytically characterized or previously known concentration pools of these pathways. Moreover\, by analyzing the timescale hierarchy of the glycolytic pathway\, we elucidate the connections between the stoichiometric and dissipative structures of reaction networks and the temporal organization of coherent structures. Specifically\, we show that glycolysis is a cofactor driven pathway\, the slowest dynamics of which are described by a balance between high-energy phosphate bond and redox trafficking. Overall\, this approach provides more biologically interpretable characterizations of network dynamics than large-scale kinetic models\, thus facilitating model reduction and personalized medicine applications. \n\n 
URL:https://www.ibs.re.kr/bimag/event/2023-09-22-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
END:VCALENDAR