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Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model
The ability to measure and analyze the complex dynamic multi-marker features of macrophages is critical for the understanding of their diverse phenotypes and functions in health and disease. To that end, we have recently developed a multi-pathway computational model that for the first time enables a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365221/ https://www.ncbi.nlm.nih.gov/pubmed/34430914 http://dx.doi.org/10.1016/j.xpro.2021.100739 |
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author | Zhao, Chen Popel, Aleksander S. |
author_facet | Zhao, Chen Popel, Aleksander S. |
author_sort | Zhao, Chen |
collection | PubMed |
description | The ability to measure and analyze the complex dynamic multi-marker features of macrophages is critical for the understanding of their diverse phenotypes and functions in health and disease. To that end, we have recently developed a multi-pathway computational model that for the first time enables a systems-level characterization of macrophage signaling and activation from quantitative, temporal, dose-dependent, and single-cell aspects. This protocol includes instructions to utilize this model to computationally explore different biological scenarios with high resolution and efficiency. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2021). |
format | Online Article Text |
id | pubmed-8365221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83652212021-08-23 Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model Zhao, Chen Popel, Aleksander S. STAR Protoc Protocol The ability to measure and analyze the complex dynamic multi-marker features of macrophages is critical for the understanding of their diverse phenotypes and functions in health and disease. To that end, we have recently developed a multi-pathway computational model that for the first time enables a systems-level characterization of macrophage signaling and activation from quantitative, temporal, dose-dependent, and single-cell aspects. This protocol includes instructions to utilize this model to computationally explore different biological scenarios with high resolution and efficiency. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2021). Elsevier 2021-08-12 /pmc/articles/PMC8365221/ /pubmed/34430914 http://dx.doi.org/10.1016/j.xpro.2021.100739 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Protocol Zhao, Chen Popel, Aleksander S. Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
title | Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
title_full | Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
title_fullStr | Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
title_full_unstemmed | Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
title_short | Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
title_sort | protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365221/ https://www.ncbi.nlm.nih.gov/pubmed/34430914 http://dx.doi.org/10.1016/j.xpro.2021.100739 |
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