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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221109T140000
DTEND;TZID=Asia/Seoul:20221109T150000
DTSTAMP:20260425T113844
CREATED:20221028T010418Z
LAST-MODIFIED:20221031T003941Z
UID:6747-1668002400-1668006000@www.ibs.re.kr
SUMMARY:Developing and designing dynamic mobile applications that transform wearable data with machine learning and mathematical models.
DESCRIPTION:Wearable analytics hold far more potential than sleep tracking or step counting. In recent years\, a number of applications have emerged which leverage the massive quantities of data being amassed by wearables around the world\, such as real-time mood detection\, advanced COVID screening\, and heart rate variability analysis. Yet packaging insights from research for success in the consumer market means prioritizing design and understandability\, while also seamlessly managing the sometimes-unreliable stream of data from the device. In this presentation\, I will discuss my own experiences building apps which interface with wearable data and process the data using mathematical modeling\, as well as recent work extending to other wearable streams and environmental controls.
URL:https://www.ibs.re.kr/bimag/event/2022-11-09/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2022/10/KakaoTalk_Photo_2022-10-28-10-19-48.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20221109T160000
DTEND;TZID=Asia/Seoul:20221109T170000
DTSTAMP:20260425T113844
CREATED:20220825T012221Z
LAST-MODIFIED:20220902T003131Z
UID:6486-1668009600-1668013200@www.ibs.re.kr
SUMMARY:Modeling cell-to-cell heterogeneity from a signaling network
DESCRIPTION:Cells make individual fate decisions through linear and nonlinear regulation of gene network\, generating diverse dynamics from a single reaction pathway. In this colloquium\, I will present two topics of our recent work on signaling dynamics at cellular and patient levels. The first example is about the initial value of the model\, as a mechanism to generate different dynamics from a single pathway in cancer and the use of the dynamics for stratification of the patients [1-3]. Models of ErbB receptor signaling have been widely used in prediction of drug sensitivity for many types of cancers. We trained the ErbB model with the data obtained from cancer cell lines and predicted the common parameters of the model. By simulation of the ErbB model with those parameters and individual patient transcriptome data as initial values\, we were able to classify the prognosis of breast cancer patients and drug sensitivity based on their in silico signaling dynamics. This result raises the question whether gene expression levels\, rather than genetic mutations\, might be better suited to classify the disease. Another example is about the regulation of transcription factors\, the recipients of signal dynamics\, for target gene expression [4-6]. By focusing on the NFkB transcription factor\, we found that the opening and closing of chromatin at the DNA regions of the putative transcription factor binding sites and the cooperativity in their interaction significantly influenced the cell-to cell heterogeneity in gene expression levels. This study indicates that the noise in gene expression is rather strongly regulated by the DNA side\, even though the signals are similarly regulated in a cell population. Overall these mechanisms are important in our understanding the cell as a system for encoding and decoding signals for fate decisions and its application to human diseases. \n[References] \n[1] Nakakuki et al. Cell 2010\,\n[2] Imoto et al. iScience 2022\,\n[3] Imoto et al. STAR Protocols 2022\,\n[4] Shinohara et al. Science 2014\,\n[5] Michida et al. Cell Reports 2020\,\n[6] Wibisana et al. PLoS Genetics 2022
URL:https://www.ibs.re.kr/bimag/event/2022-11-09-colloquium/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
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ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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