BEGIN:VCALENDAR
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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
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:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230303T110000
DTEND;TZID=Asia/Seoul:20230303T120000
DTSTAMP:20260425T164807
CREATED:20230213T110430Z
LAST-MODIFIED:20230227T013418Z
UID:7331-1677841200-1677844800@www.ibs.re.kr
SUMMARY:Shinya Kuroda\, Systems Biology of Insulin Action
DESCRIPTION:Abstract: \n1. The “temporal information code” of insulin action: a bottom-up approach One of the essential elements of signaling networks is to encode information from a wide variety of inputs into a limited set of molecules. We have proposed a “temporal information code” that regulates a variety of physiological functions by encoding input information in temporal patterns of molecular activity\, and based on this concept\, we are analyzing biological homeostasis by insulin signaling. Taking blood insulin as an example\, we will explain how the temporal information of blood insulin is selectively decoded by downstream networks. \n2. Transomics of insulin action: a top-down approach In order to obtain a complete picture of insulin action\, we performed transomics measurements integrating metabolomics and transcriptomics\, and found that metabolism is regulated by allosteric regulation in the liver of normal mice and by compensatory gene expression in the liver of obese mice. (Top-down approach). I will talk about approach the principle of homeostasis of living organisms by temporal patterns\, using the analysis of systems biology of insulin action using two different approaches.
URL:https://www.ibs.re.kr/bimag/event/systems-biology-of-insulin-action/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230303T140000
DTEND;TZID=Asia/Seoul:20230303T160000
DTSTAMP:20260425T164807
CREATED:20230127T063333Z
LAST-MODIFIED:20230130T080633Z
UID:7280-1677852000-1677859200@www.ibs.re.kr
SUMMARY:Seho Park\, Dynamical information enables inference of gene regulation at single-cell scale
DESCRIPTION:We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”\, Zhang\, Stephen Y.\, and Michael PH Stumpf.\, bioRxiv (2023): 2023-01. \nAbstract \n\nCellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes\, we propose locaTE\, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific\, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods\, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets\, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data\, locaTE provides a powerful\, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.
URL:https://www.ibs.re.kr/bimag/event/2023-03-03-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:20230310T100000
DTEND;TZID=Asia/Seoul:20230310T110000
DTSTAMP:20260425T164807
CREATED:20230213T105750Z
LAST-MODIFIED:20230306T000259Z
UID:7321-1678442400-1678446000@www.ibs.re.kr
SUMMARY:Martin Nowak\, Evolution of cooperation
DESCRIPTION:Abstract: Cooperation means that one individual pays a cost for another to receive a benefit. Cooperation can be at variance with natural selection. Why should you help competitors? Yet cooperation is abundant in nature and is important component of evolutionary innovation. Cooperation can be seen as the master architect of evolution and as the third fundamental principle of evolution beside mutation and selection. I will present five mechanisms for the evolution of cooperation: direct reciprocity\, indirect reciprocity\, spatial selection\, group selection and kin selection. Global cooperation and the cooperation with future generations is necessary to ensure the survival of our species. \nFurther reading:\nNowak MA (2006). Evolutionary Dynamics. Harvard University Press\nNowak MA & Highfield R (2011) SuperCooperators. Simon & Schuster.\nHauser OP\, Rand DG\, Peysakhovich A & Nowak MA (2014). Cooperating with the future. Nature 511: 220-223\nHilbe C\, Šimsa Š\, Chatterjee K & Nowak MA (2018). Evolution of cooperation in stochastic games. Nature 559: 246-249\nHauser OP\, Hilbe C\, Chatterjee K & Nowak MA (2019). Social dilemmas among unequals. Nature 572: 524-527
URL:https://www.ibs.re.kr/bimag/event/evolution-of-cooperation/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/02/MartinNowak_250.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230310T140000
DTEND;TZID=Asia/Seoul:20230310T160000
DTSTAMP:20260425T164807
CREATED:20230228T075546Z
LAST-MODIFIED:20230309T012315Z
UID:7391-1678456800-1678464000@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks
DESCRIPTION:We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”\, Briat\, Corentin\, Ankit Gupta\, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26. \nAbstract \n\nThe ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback can be used to achieve homeostasis when networks behave deterministically\, the effect of noise on their regulatory function is not understood. Here\, we combine probability and control theory to develop a theory of biological regulation that explicitly takes into account the noisy nature of biochemical reactions. We introduce tools for the analysis and design of robust homeostatic circuits and propose a new regulation motif\, which we call antithetic integral feedback. This motif exploits stochastic noise\, allowing it to achieve precise regulation in scenarios where similar deterministic regulation fails. Specifically\, antithetic integral feedback preserves the stability of the overall network\, steers the population of any regulated species to a desired set point\, and adapts perfectly. We suggest that this motif may be prevalent in endogenous biological circuits and useful when creating synthetic circuits.
URL:https://www.ibs.re.kr/bimag/event/2023-03-10-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:20230313T110000
DTEND;TZID=Asia/Seoul:20230313T120000
DTSTAMP:20260425T164807
CREATED:20230310T010131Z
LAST-MODIFIED:20230310T010131Z
UID:7445-1678705200-1678708800@www.ibs.re.kr
SUMMARY:Marko Ćosić\, The morphological analysis of the collagen straightness in the colon mucosa away from the cancer
DESCRIPTION:Abstract: The morphological method – based on the topology and singularity theory and originally developed for the analysis of the scattering experiments – was extended to be applicable for the analysis of biological data. The usefulness of the topological viewpoint was demonstrated by quantification of the changes of collagen fiber straightness in the human colon mucosa (healthy mucosa\, colorectal cancer\, and uninvolved mucosa far from cancer).\nThis has been done by modeling the distribution of collagen segment angles by the polymorphic beta-distribution. Its shapes were classified according to the number and type of critical points. We found that biologically relevant shapes could be classified as shapes without any preferable orientation (i.e. shapes without local extrema)\, transitional forms (i.e. forms with one broad local maximum)\, and highly oriented forms (i.e. forms with two minima at both ends and one very narrow maximum between them). Thus\, changes in the fiber organization were linked to the metamorphoses of the beta-distribution forms.\nThe obtained classification was used to define a new\, shape-aware/based\, measure of the collagen straightness\, which revealed a slight\, and moderate increase of the straightness in mucosa samples taken 20 cm and 10 cm away from the tumor. The largest increase of collagen straightness was found in samples of cancer tissue. Samples of the healthy individuals have a uniform distribution of beta-distribution forms. We found that this distribution has the maximal information entropy. At 20 cm and 10 cm away from cancer\, the transition forms redistribute into unoriented and highly oriented forms. Closer to cancer the number of unoriented forms decreases rapidly leaving only highly oriented forms present in the samples of the cancer tissue\, whose distribution has minimal information entropy. The polarization of the distribution was followed by a significant increase in the number of quasi-symmetrical forms in samples 20 cm away from cancer which decreases closer to cancer.\nThis work shows that the evolution of the distribution of the beta-distribution forms – an abstract construction of the mind – follows the familiar laws of statistical mechanics. Additionally\, the polarization of the beta-distribution forms together with the described change in the number of quasi-symmetrical forms\, clearly visible in the parametric space of the beta-distribution and very difficult to notice in the observable space\, can be a useful indicator of the early stages in the development of colorectal cancer.
URL:https://www.ibs.re.kr/bimag/event/marko-cosic-the-morphological-analysis-of-the-collagen-straightness-in-the-colon-mucosa-away-from-the-cancer/
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:20230315T160000
DTEND;TZID=Asia/Seoul:20230315T170000
DTSTAMP:20260425T164807
CREATED:20230213T105947Z
LAST-MODIFIED:20230312T051759Z
UID:7324-1678896000-1678899600@www.ibs.re.kr
SUMMARY:Julio Saez-Rodriguez\, Dynamic logic models complement machine learning for personalized medicine
DESCRIPTION:Abstract: \nMulti-omics technologies\, and in particular those with single-cell and spatial resolution\, provide unique opportunities to study the deregulation of intra- and inter-cellular signaling processes in disease. I will present recent methods and applications from our group toward this aim\, focusing on computational approaches that combine data with biological knowledge within statistical and machine learning methods. This combination allows us to increase both the statistical power of our analyses and the mechanistic interpretability of the results. These approaches allow us to identify key processes\, that can be in turn studied in detailed with dynamic mechanistic models. I will then present how cell-specific logic models\, trained with measurements upon perturbations\, can provides new biomarkers and treatment opportunities. Finally\, I will show how\, using novel microfluidics-based technologies\, this approach can also be applied directly to biopsies\, allowing to build mechanistic models for individual cancer patients\, and use these models to prose new therapies.
URL:https://www.ibs.re.kr/bimag/event/dynamic-logic-models-complement-machine-learning-for-personalized-medicine/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/02/SAEZ_Rodriguez_Julio_March_2014-copy-e1508925747488.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230317T140000
DTEND;TZID=Asia/Seoul:20230317T160000
DTSTAMP:20260425T164807
CREATED:20230228T075515Z
LAST-MODIFIED:20230315T020610Z
UID:7393-1679061600-1679068800@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Single-sample landscape entropy reveals the imminent phase transition during disease progression
DESCRIPTION:We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”\, Liu R\, Chen P\, Chen L.\, Bioinformatics. 2020 Mar 1;36(5):1522-1532. \nAbstract \n\n\nMotivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt\, that is\, there is a tipping point during such a process at which the system state shifts from the normal state to a disease state. It is challenging to predict such disease state with the measured omics data\, in particular when only a single sample is available. \nResults: In this study\, we developed a novel approach\, i.e. single-sample landscape entropy (SLE) method\, to identify the tipping point during disease progression with only one sample data. Specifically\, by evaluating the disorder of a network projected from a single-sample data\, SLE effectively characterizes the criticality of this single sample network in terms of network entropy\, thereby capturing not only the signals of the impending transition but also its leading network\, i.e. dynamic network biomarkers. Using this method\, we can characterize sample-specific state during disease progression and thus achieve the disease prediction of each individual by only one sample. Our method was validated by successfully identifying the tipping points just before the serious disease symptoms from four real datasets of individuals or subjects\, including influenza virus infection\, lung cancer metastasis\, prostate cancer and acute lung injury.
URL:https://www.ibs.re.kr/bimag/event/2023-03-17-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:20230320T110000
DTEND;TZID=Asia/Seoul:20230320T120000
DTSTAMP:20260425T164807
CREATED:20230316T004827Z
LAST-MODIFIED:20230316T004827Z
UID:7493-1679310000-1679313600@www.ibs.re.kr
SUMMARY:Marko Ćosić\, Stewart’s Catastrophic Swing
DESCRIPTION:Abstract\nThe standard approach to problem-solving in physics consists of identifying state variables of the system\, setting differential equations governing the state evolution\, and solving the obtained. The behavior of the system for different values of parameters can be examined only as a fourth step. On the contrary\, the modern approach to studying dynamical systems relies on Morphological/Topological analysis which alleviates the necessity for the explicit solution of differential equations. \nThe stability analysis of the parabolic swing will demonstrate the merit of such an approach. It will be shown how to construct a qualitatively correct model of system dynamics that is surprisingly quantitatively correct as well. The sudden (catastrophic) change in the swing’s stability\, caused by a slight change in the critical value of system parameters\, will be linked to the drastic topological change of the corresponding phase-space portraits. \nIt will be shown that for a system’s parameters close to critical ones\, the system’s behavior is identical to a specific simple universal prototype given by catastrophe theory. A short survey of the simplest elementary catastrophes will be given that represents the basis for applying catastrophe theory in other fields of science.
URL:https://www.ibs.re.kr/bimag/event/marko-cosic-stewarts-catastrophic-swing/
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:20230324T140000
DTEND;TZID=Asia/Seoul:20230324T160000
DTSTAMP:20260425T164807
CREATED:20230228T075941Z
LAST-MODIFIED:20230228T075941Z
UID:7395-1679666400-1679673600@www.ibs.re.kr
SUMMARY:Candan Celik\, The effect of microRNA on protein variability and gene expression fidelity
DESCRIPTION:We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”\, Hilfinger\, Andreas\, and Raymond Fan.\, Biophysical journal 122.3 (2023): 537a. \nAbstract \n\nSmall regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability of gene expression around average levels. Here we quantify noise in stochastic gene expression models with and without such regulation. Our results suggest that silencing mRNA post-transcriptionally will always increase rather than decrease gene expression noise when the silencing of mRNA also increases its degradation as is expected for microRNA interactions with mRNA. In that regime we also find that silencing mRNA generally reduces the fidelity of signal transmission from deterministically varying upstream factors to protein levels. These findings suggest that microRNA binding to mRNA does not generically confer precision to protein expression
URL:https://www.ibs.re.kr/bimag/event/2023-03-24-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:20230324T160000
DTEND;TZID=Asia/Seoul:20230324T170000
DTSTAMP:20260425T164808
CREATED:20230213T105312Z
LAST-MODIFIED:20230320T010451Z
UID:7318-1679673600-1679677200@www.ibs.re.kr
SUMMARY:(Rescheduled: 3/22 -> 3/24) Stefan Bauer\, Neural Causal Models for Experimental Design
DESCRIPTION:Abstract: Many questions in everyday life as well as in research are causal in nature: How would the climate change if we lower train prices or will my headache go away if I take an aspirin? Inherently\, such questions need to specify the causal variables relevant to the question and their interactions. However\, existing algorithms for learning causal graphs from data are often not scaling well both with the number of variables or the number of observations. This talk will provide a brief introduction to causal structure learning\, recent efforts in using continuous optimization to learn causal graphs at scale and systematic approaches for causal experimental design at scale.
URL:https://www.ibs.re.kr/bimag/event/neural-causal-models-for-experimental-design/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/02/jItlmUQr_400x400.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230327T160000
DTEND;TZID=Asia/Seoul:20230327T170000
DTSTAMP:20260425T164808
CREATED:20230323T064118Z
LAST-MODIFIED:20230323T064136Z
UID:7536-1679932800-1679936400@www.ibs.re.kr
SUMMARY:Sungwoong Cho\, HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
DESCRIPTION:Fast and accurate predictions for complex physical dynamics are a big challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in the real-world problems. The deep operator network (DeepONet) has recently been proposed as a framework for learning nonlinear mappings between function spaces. However\, the DeepONet requires many parameters and has a high computational cost when learning operators\, particularly those with complex (discontinuous or non-smooth) target functions. In this study\, we propose HyperDeepONet\, which uses the expressive power of the hypernetwork to enable learning of a complex operator with smaller set of parameters. The DeepONet and its variant models can be thought of as a method of injecting the input function information into the target function. From this perspective\, these models can be viewed as a special case of HyperDeepONet. We analyze the complexity of DeepONet and conclude that HyperDeepONet needs relatively lower complexity to obtain the desired accuracy for operator learning. HyperDeepONet was successfully applied to various operator learning problems using low computational resources compared to other benchmarks.
URL:https://www.ibs.re.kr/bimag/event/2023-03-27-seminar/
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
END:VCALENDAR