컨텐츠 시작
학술대회/행사
초록검색
| 제출번호(No.) | 0163 |
|---|---|
| 분류(Section) | Special Session |
| 분과(Session) | (SS-16) Computational Methods for PDEs and Dynamical Systems (SS-16) |
| 발표시간(Time) | 20th-C-11:00 -- 11:30 |
| 영문제목 (Title(Eng.)) |
Density Physics-Informed Neural Network infers an arbitrary density distribution for non-Markovian system |
| 저자(Author(s)) |
Hyeontae Jo1, Hyukpyo Hong1, Hyung Ju Hwang3, Won Chang4, Jae Kyoung Kim1 IBS-BIMAG1, KAIST2, POSTECH3, University of Cincinnati4 |
| 초록본문(Abstract) | The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multi-modality in a transduction-time distribution indicates that the response is regulated by multiple pathways with different transduction speeds. Here, we developed a method called density physics-informed neural networks (Density-PINNs) to infer the transduction-time distribution from measurable final stress response time traces. We applied Density-PINNs to single-cell gene expression data from sixteen promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINNs can also be applied to understand other time delay systems, including infectious diseases. |
| 분류기호 (MSC number(s)) |
68T07 |
| 키워드(Keyword(s)) | Physics-informed neural networks, signaling patwhays |
| 강연 형태 (Language of Session (Talk)) |
Korean |