DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, Hyeontae | ko |
dc.contributor.author | Hong, Hyukpyo | ko |
dc.contributor.author | Hwang, Hyung Ju | ko |
dc.contributor.author | Chang, Won | ko |
dc.contributor.author | Kim, Jae Kyoung | ko |
dc.date.accessioned | 2024-07-15T07:00:06Z | - |
dc.date.available | 2024-07-15T07:00:06Z | - |
dc.date.created | 2024-07-13 | - |
dc.date.issued | 2024-02 | - |
dc.identifier.citation | Patterns, v.5, no.2 | - |
dc.identifier.issn | 2666-3899 | - |
dc.identifier.uri | http://hdl.handle.net/10203/320254 | - |
dc.description.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. | - |
dc.language | English | - |
dc.publisher | Cell Press | - |
dc.title | Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85182651509 | - |
dc.type.rims | ART | - |
dc.citation.volume | 5 | - |
dc.citation.issue | 2 | - |
dc.citation.publicationname | Patterns | - |
dc.identifier.doi | 10.1016/j.patter.2023.100899 | - |
dc.contributor.localauthor | Kim, Jae Kyoung | - |
dc.contributor.nonIdAuthor | Jo, Hyeontae | - |
dc.contributor.nonIdAuthor | Hwang, Hyung Ju | - |
dc.contributor.nonIdAuthor | Chang, Won | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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