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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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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20250502T140000
DTEND;TZID=Asia/Seoul:20250502T160000
DTSTAMP:20260423T061636
CREATED:20250330T073307Z
LAST-MODIFIED:20250424T070416Z
UID:10929-1746194400-1746201600@www.ibs.re.kr
SUMMARY:Boolean modelling as a logic-based dynamic approach in systems medicine - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “Boolean modelling as a logic-based dynamic approach in systems medicine” by Ahmed Abdelmonem Hemedan et al.\, Computational and Structural biotechnology journal (2022). \nAbstract  \nMolecular mechanisms of health and disease are often represented as systems biology diagrams\, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models\, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive\, one for present or active. Because of this approximation\, Boolean modelling is applicable to large diagrams\, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally\, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
URL:https://www.ibs.re.kr/bimag/event/boolean-modelling-as-a-logic-based-dynamic-approach-in-systems-medicine-kevin-spinicci/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250509T140000
DTEND;TZID=Asia/Seoul:20250509T160000
DTSTAMP:20260423T061636
CREATED:20250426T142850Z
LAST-MODIFIED:20250507T002814Z
UID:11058-1746799200-1746806400@www.ibs.re.kr
SUMMARY:Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Network inference from short\, noisy\, low time-resolution\, partial measurements: Application to C. elegans neuronal calcium dynamics” by Amitava Banerjee\, Sarthak Chandra\, and Edward Ott\, PNAS\, 2023. \nAbstract \nNetwork link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications\, e.g.\, estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases\, the measured time series data may be subject to limitations\, including limited duration\, low sampling rate\, observational noise\, and partial nodal state measurement. However\, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here\, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality\, transfer entropy\, and\, a machine learning-based method. Furthermore\, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.
URL:https://www.ibs.re.kr/bimag/event/chaos-is-not-rare-in-natural-ecosystems-olive-cawiding/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 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:20250530T140000
DTEND;TZID=Asia/Seoul:20250530T160000
DTSTAMP:20260423T061636
CREATED:20250426T143239Z
LAST-MODIFIED:20250528T035910Z
UID:11061-1748613600-1748620800@www.ibs.re.kr
SUMMARY:Direct Estimation of Parameters in ODE Models Using WENDy - Kangmin Lee
DESCRIPTION:In this talk\, we discuss the paper “Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics” by David M. Bortz\, Daniel A. Messenger\, and Vanja Dukic\, Bulletin of Mathematical Biology\, 2023. \nAbstract \nWe introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers\, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data\, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems\, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form\, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework\, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions\, created from a set of C∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology\, neuroscience\, and biochemistry\, including logistic growth\, Lotka-Volterra\, FitzHugh-Nagumo\, Hindmarsh-Rose\, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy.
URL:https://www.ibs.re.kr/bimag/event/quantifying-and-correcting-bias-in-transcriptional-parameter-inference-from-single-cell-data-kangmin-lee/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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