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Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction

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Title
Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction
Author(s)
Hyeontae Jo; Hyukpyo Hong; Hwang, Hyung Ju; Chang, Won; Jae Kyoung Kim
Publication Date
2024-02
Journal
Patterns, v.5, no.2
Publisher
Cell Press
Abstract
Understanding cellular signaling pathways is crucial because their dysregulation can lead to diseases and treatment resistance. For instance, if signaling pathways that respond to antibiotics or cancer therapeutics show a large heterogeneity in response between cells, some cells could survive the treatment, while others are killed by it. Valuable information about the signaling pathway, such as its speed, precision, and structure, can be inferred from the transduction time, the time it takes for a signal to travel from its initiation to its final response. Therefore, developing methods that can estimate the transduction-time distribution of a signaling pathway could enable the identification of sources of cellular heterogeneity and could ultimately help develop better treatment agents that can avoid or overcome heterogeneous cellular responses.
URI
https://pr.ibs.re.kr/handle/8788114/14792
DOI
10.1016/j.patter.2023.100899
ISSN
2666-3899
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Biomedical Mathematics Group(의생명 수학 그룹) > 1. Journal Papers (저널논문)
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > 1. Journal Papers (저널논문)
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