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  • 4/11(목) 16:30 김재경 교수(KAIST)

    • 등록일
      2024.03.27
    • 조회수
      820
  • 첨부파일

* 일시: 04/11(목) 16:30

 

* 연사: 김재경 교수(KAIST)

 

* 장소: 벤처관 711호

 

* 제목:  Inference of Dynamic Networks in Biological Systems

 

*초록 :

Biological systems are complex dynamic networks. In this talk, I will introduce GOBI (General Model-based Inference), a simple and scalable method for inferring regulatory networks from time-series data. GOBI can infer gene regulatory networks and ecological networks that cannot be obtained with previous causation detection methods(e.g., Granger, CCM, PCM). I will then introduce Density-PINN (Physics-Informed Neural Network), a method for inferring the shape of the time-delay distribution of interactions in a network. The inferred shape of time-delay distribution can be used to identify the number of pathways that induce a signaling response against antibiotics, which solves the long-standing mystery, the major source of cell-to-cell heterogeneity in response to stress. I will talk how to infer the dynamic information from just network structure information, which can be used to identify the targets (nodes) perturbing the homeostasis of the systems. Finally, I will talk about our scRNA-seq analysis algorithm detecting signal robustly to infer networks among single cells.

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