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From live cell imaging to moment-based variational inference

June 1 @ 5:00 pm - 6:00 pm KST

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: 1234)

This talk will be presented online. Zoom link: 997 8258 4700 (pw: 1234)

Abstract: Quantitative characterization of biomolecular networks is important for the analysis and design of network functionality. Reliable models of such networks need to account for intrinsic and extrinsic noise present in the cellular environment. Stochastic kinetic models provide a principled framework for developing quantitatively predictive tools in this scenario. Calibration of such models requires an experimental setup capable of monitoring a large number of individual cells over time, automatic extraction of fluorescence levels for each cell and a scalable inference approach. In the first part of the talk we will cover our microfluidic setup and a deep-learning based approach to cell segmentation and data extraction. The second part will introduce moment-based variational inference as a scalable framework for approximate inference of kinetic models based on single cell data.

Details

Date:
June 1
Time:
5:00 pm - 6:00 pm KST
Event Category:

Venue

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)
(pw: 1234)

Organizer

Jae Kyoung Kim
Email:
jaekkim@kaist.ac.kr
IBS 의생명수학그룹 Biomedical Mathematics Group
기초과학연구원 수리및계산과학연구단 의생명수학그룹
대전 유성구 엑스포로 55 (우) 34126
IBS Biomedical Mathematics Group (BIMAG)
Institute for Basic Science (IBS)
55 Expo-ro Yuseong-gu Daejeon 34126 South Korea
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