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Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number

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Title
Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number
Author(s)
Dae Wook Kim; Jae Kyoung Kim; Hyukpyo Hong
Publication Date
2022-03
Journal
Science Advances, v.8, no.11
Publisher
American Association for the Advancement of Science
Abstract
Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.
URI
https://pr.ibs.re.kr/handle/8788114/12391
DOI
10.1126/sciadv.abl4598
ISSN
2375-2548
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > 1. Journal Papers (저널논문)
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Biomedical Mathematics Group(의생명 수학 그룹) > 1. Journal Papers (저널논문)
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