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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220401T130000
DTEND;TZID=Asia/Seoul:20220401T140000
DTSTAMP:20260424T220013
CREATED:20220331T190000Z
LAST-MODIFIED:20220320T093117Z
UID:5564-1648818000-1648821600@www.ibs.re.kr
SUMMARY:Physics-informed learning of governing equations from scarce data
DESCRIPTION:We will discuss about “Physics-informed learning of governing equations from scarce data”\, Chen et al.\, Nature Communications\, 2021 \nAbstract: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive\, as in climate science\, neuroscience\, ecology\, finance\, and epidemiology\, to name only a few examples. In this work\, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics\, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular\, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems\, from simple canonical systems\, including linear and nonlinear oscillators and the chaotic Lorenz system\, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.
URL:https://www.ibs.re.kr/bimag/event/2022-04-01/
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:20220407T110000
DTEND;TZID=Asia/Seoul:20220407T120000
DTSTAMP:20260424T220013
CREATED:20220406T170000Z
LAST-MODIFIED:20220224T002321Z
UID:5585-1649329200-1649332800@www.ibs.re.kr
SUMMARY:Universal biology in adaptation and evolution: dimensional reduction\, and fluctuation-response relationship
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: A macroscopic theory for cellular states with steady-growth is presented\, based on consistency between cellular growth and molecular replication\, together with robustness of phenotypes against perturbations. Adaptive changes in high-dimensional phenotypes are shown to be restricted within a low-dimensional slow manifold\, from which a macroscopic law for cellular states is derived\, as is confirmed by adaptation experiments of bacteria under stress. The theory is extended to phenotypic evolution\, leading to proportionality between phenotypic responses against genetic evolution and by environmental adaptation\, which explains the evolutionary fluctuation-response relationship previously uncovered.   \nReferences \n\n Kaneko K.\, Life: An Introduction to Complex Systems Biology\, Springer (2006)\n K. Kaneko\, C.Furusawa\, T. Yomo\, “Macroscopic phenomenology for cells in steady-growth state”\, Phys.Rev.X(2015) 011014\n C. Furusawa\, K. Kaneko “Global Relationships in Fluctuation and Response in Adaptive Evolution”\, J of Royal Society Interface 12(2015)\, 20150482.\n C. Furusawa\, K. Kaneko ” Formation of Dominant Mode by Evolution in Biological Systems” Phys. Rev. E 97(2018)042410\n K. Kaneko\, C. Furusawa “Macroscopic Theory for Evolving Biological Systems Akin to Thermodynamics”\, Annual Rev. Biophys. (2018) 47\, 273-290\n A. Sakata and K. Kaneko\, “Dimensional Reduction in Evolving Spin-Glass Model: Correlation of Phenotypic Responses to Environmental and Mutational Changes”\, Phys. Rev. Lett. (2020) 124\, 218101\n Q-Y. Tang and K. Kaneko\, “ Dynamics-evolution correspondence in protein structures”\,  Phys. Rev. Lett. (2021) 127\, 098103
URL:https://www.ibs.re.kr/bimag/event/2022-04-07/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/Kunihiko-Kaneko.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220408T130000
DTEND;TZID=Asia/Seoul:20220408T140000
DTSTAMP:20260424T220013
CREATED:20220407T190000Z
LAST-MODIFIED:20220405T042614Z
UID:5870-1649422800-1649426400@www.ibs.re.kr
SUMMARY:RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy
DESCRIPTION:We will discuss about “RTransferEntropy — Quantifying information flow between different time series using effective transfer entropy”\, Behrendt et al.\, SoftwareX\, 2019 \nAbstract: This paper shows how to quantify and test for the information flow between two time series with Shannon transfer entropy and Rényi transfer entropy using the R package RTransferEntropy. We discuss the methodology\, the bias correction applied to calculate effective transfer entropy and outline how to conduct statistical inference. Furthermore\, we describe the package in detail and demonstrate its functionality by means of several simulated processes and present an application to financial time series.
URL:https://www.ibs.re.kr/bimag/event/2022-04-08-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:20220414T103000
DTEND;TZID=Asia/Seoul:20220414T110000
DTSTAMP:20260424T220013
CREATED:20220413T163000Z
LAST-MODIFIED:20220130T045637Z
UID:5588-1649932200-1649934000@www.ibs.re.kr
SUMMARY:An overview of methods used for multi-scale modeling and analysis
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2022-04-14-1/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/DK_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220414T110000
DTEND;TZID=Asia/Seoul:20220414T120000
DTSTAMP:20260424T220013
CREATED:20220413T170000Z
LAST-MODIFIED:20220224T002525Z
UID:5591-1649934000-1649937600@www.ibs.re.kr
SUMMARY:A systems biology approach using multi-scale modeling to understand the immune response to tuberculosis infection and treatment
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb)\, the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. The structure of granulomas\, lesions forming in lungs in response to Mtb infection\, create heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb.   We can use a systems biology approach pairing experimental data from non-human primates with computational modeling to represent and predict how factors impact antibiotic regimen efficacy and granuloma bacterial sterilization. We utilize an agent-based\, computational model that simulates granuloma formation\, function and treatment\, called GranSim.  A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. We also created a whole host model\, called HOSTSIM\, to study Mtb dynamics within a human host.  Overall\, we use these models to help better understand TB treatment and strengthen our ability to predict regimens that can improve clinical treatment of TB.
URL:https://www.ibs.re.kr/bimag/event/2022-04-14-2/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/DK_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220415T110000
DTEND;TZID=Asia/Seoul:20220415T130000
DTSTAMP:20260424T220013
CREATED:20220414T182000Z
LAST-MODIFIED:20220414T012030Z
UID:5868-1650020400-1650027600@www.ibs.re.kr
SUMMARY:A topological data analysis based classifier
DESCRIPTION:We will discuss about “A topological data analysis based classifier”\, Kindelan et al.\, arXiv\, 2022 \nAbstract: Topological Data Analysis is an emergent field that aims to discover the underlying dataset’s topological information. Topological Data Analysis tools have been commonly used to create filters and topological descriptors to improve Machine Learning (ML) methods. This paper proposes a different Topological Data Analysis pipeline to classify balanced and imbalanced multi-class datasets without additional ML methods. Our proposed method was designed to solve multi-class problems. It resolves multi-class imbalanced classification problems with no data resampling preprocessing stage. The proposed Topological Data Analysis-based classifier builds a filtered simplicial complex on the dataset representing high-order data relationships. Following the assumption that a meaningful sub-complex exists in the filtration that approximates the data topology\, we apply Persistent Homology to guide the selection of that sub-complex by considering detected topological features. We use each unlabeled point’s link and star operators to provide different sized and multi-dimensional neighborhoods to propagate labels from labeled to unlabeled points. The labeling function depends on the filtration entire history of the filtered simplicial complex and is encoded within the persistent diagrams at various dimensions. We select eight datasets with different dimensions\, degrees of class overlap\, and imbalanced samples per class. The TDABC outperforms all baseline methods classifying multi-class imbalanced data with high imbalanced ratios and data with overlapped classes. Also\, on average\, the proposed method was better than KNN and weighted-KNN and behaved competitively with SVM and Random Forest baseline classifiers in balanced datasets.
URL:https://www.ibs.re.kr/bimag/event/2022-04-15-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:20220421T160000
DTEND;TZID=Asia/Seoul:20220421T170000
DTSTAMP:20260424T220013
CREATED:20220420T220000Z
LAST-MODIFIED:20220416T063046Z
UID:5864-1650556800-1650560400@www.ibs.re.kr
SUMMARY:Dynamical and topological hallmarks of regulatory networks driving phenotypic plasticity and heterogeneity in cancers
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: \nMetastasis and therapy resistance cause over 90% of cancer-related deaths. Despite extensive ongoing efforts\, no unique genetic or mutational signature has emerged for metastasis. Instead\, the ability of genetically identical cells to adapt reversibly by exhibiting multiple phenotypes (phenotypic/non-genetic heterogeneity) and switch among them (phenotypic plasticity) is proposed as a hallmark of metastasis. Also\, drug resistance can emerge from such non-genetic adaptive cellular changes. However\, the origins of such non-genetic heterogeneity in most cancers are poorly understood. I will present our findings on a) how non-genetic heterogeneity emerges in a population of cancer\, and b) what design principles underlie regulatory networks enabling non-genetic heterogeneity across multiple cancers. Our results unravel how systems-levels approaches integrating mechanistic mathematical modeling with in vitro and in vivo data can identify causes and consequences of such non-genetic heterogeneity.
URL:https://www.ibs.re.kr/bimag/event/2022-04-21/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220422T130000
DTEND;TZID=Asia/Seoul:20220422T140000
DTSTAMP:20260424T220013
CREATED:20220421T190000Z
LAST-MODIFIED:20220329T005550Z
UID:5874-1650632400-1650636000@www.ibs.re.kr
SUMMARY:An Efficient Characterization of Complex-Balanced\, Detailed-Balanced\, and Weakly Reversible Systems
DESCRIPTION:We will discuss about “An Efficient Characterization of Complex-Balanced\, Detailed-Balanced\, and Weakly Reversible Systems”\, Craciun et al.\, SIAM Journal on Applied Mathematics\, 2020 \nAbstract: Very often\, models in biology\, chemistry\, physics\, and engineering are systems of polynomial or power-law ordinary differential equations\, arising from a reaction network. Such dynamical systems can be generated by many different reaction networks. On the other hand\, networks with special properties (such as reversibility or weak reversibility) are known or conjectured to give rise to dynamical systems that have special properties: existence of positive steady states\, persistence\, permanence\, and (for well-chosen parameters) complex balancing or detailed balancing. These last two are related to thermodynamic equilibrium\, and therefore the positive steady states are unique and stable. We describe a computationally efficient characterization of polynomial or power-law dynamical systems that can be obtained as complex-balanced\, detailed-balanced\, weakly reversible\, and reversible mass-action systems.
URL:https://www.ibs.re.kr/bimag/event/2022-04-22-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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220428T110000
DTEND;TZID=Asia/Seoul:20220428T120000
DTSTAMP:20260424T220013
CREATED:20220427T170000Z
LAST-MODIFIED:20220224T002639Z
UID:5593-1651143600-1651147200@www.ibs.re.kr
SUMMARY:Scaling behaviors in physiological fluctuations: relevance to circadian regulation and insights into the development of Alzheimer’s disease
DESCRIPTION:This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234) \nAbstract: Outputs from health biological systems display complex fluctuations that are not random but display robust and often self-similar (fractal) temporal correlations at different time scales— scaling behaviors. The scaling behaviors in the fluctuations of biological outputs such as neural activities\, cardiac dynamics\, motor activity are believed to be originated from feedbacks within the complex biological networks\, reflecting the system adaptability to internal and external inputs. Supporting this concept\, our studies have demonstrated a mechanistic link between the scaling regulation of physiological fluctuations and the circadian control system— a result of evolutionary adaptation to daily environmental light-dark cycles on the earth. In this talk\, I will discuss certain evidence for this ‘scaling-circadian’ link and its related implications. Moreover\, I will review some recent studies\, in which we examined how the scaling patterns of human motor activity fluctuations change with aging and in Alzheimer’s disease. Our results showed that (1) alterations in scaling activity patterns occur before the clinical manifestation of Alzheimer’s disease (i.e.\, cognitive impairment) and predict cognitive decline and the risk for Alzheimer’s dementia; and (2) the progression of Alzheimer’s disease accelerates the aging effect on the scaling activity patterns. Our work provides strong evidence that altered scaling activity patterns may also be a risk factor for neurodegeneration\, playing a role in the development and progression of Alzheimer’s disease.
URL:https://www.ibs.re.kr/bimag/event/2022-04-28/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/01/KH_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220429T130000
DTEND;TZID=Asia/Seoul:20220429T140000
DTSTAMP:20260424T220013
CREATED:20220329T103359Z
LAST-MODIFIED:20220329T103359Z
UID:5877-1651237200-1651240800@www.ibs.re.kr
SUMMARY:Toroidal topology of population activity in grid cells
DESCRIPTION:We will discuss about “Toroidal topology of population activity in grid cells”\, Gardner et al.\, Nature\, 2021. \nAbstract: The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment. Grid cells\, a key component of this system\, fire in a characteristic hexagonal pattern of locations\, and are organized in modules that collectively form a population code for the animal’s allocentric position. The invariance of the correlation structure of this population code across environments and behavioral states\, independent of specific sensory inputs\, has pointed to intrinsic\, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern. However\, whether grid cell networks show continuous attractor dynamics\, and how they interface with inputs from the environment\, has remained unclear owing to the small samples of cells obtained so far. Here\, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis\, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold\, as expected in a two-dimensional CAN. Positions on the torus correspond to the positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep\, as predicted by CAN models for grid cells but not by alternative feedforward models. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.
URL:https://www.ibs.re.kr/bimag/event/2022-04-29-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