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Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number
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 ov...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932658/ https://www.ncbi.nlm.nih.gov/pubmed/35302852 http://dx.doi.org/10.1126/sciadv.abl4598 |
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author | Kim, Dae Wook Hong, Hyukpyo Kim, Jae Kyoung |
author_facet | Kim, Dae Wook Hong, Hyukpyo Kim, Jae Kyoung |
author_sort | Kim, Dae Wook |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8932658 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89326582022-03-31 Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number Kim, Dae Wook Hong, Hyukpyo Kim, Jae Kyoung Sci Adv Social and Interdisciplinary Sciences 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. American Association for the Advancement of Science 2022-03-18 /pmc/articles/PMC8932658/ /pubmed/35302852 http://dx.doi.org/10.1126/sciadv.abl4598 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Social and Interdisciplinary Sciences Kim, Dae Wook Hong, Hyukpyo Kim, Jae Kyoung Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number |
title | Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number |
title_full | Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number |
title_fullStr | Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number |
title_full_unstemmed | Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number |
title_short | Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number |
title_sort | systematic inference identifies a major source of heterogeneity in cell signaling dynamics: the rate-limiting step number |
topic | Social and Interdisciplinary Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932658/ https://www.ncbi.nlm.nih.gov/pubmed/35302852 http://dx.doi.org/10.1126/sciadv.abl4598 |
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