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A multi-approach and multi-scale platform to model CD4+ T cells responding to infections
Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In eac...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376204/ https://www.ncbi.nlm.nih.gov/pubmed/34343169 http://dx.doi.org/10.1371/journal.pcbi.1009209 |
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author | Wertheim, Kenneth Y. Puniya, Bhanwar Lal La Fleur, Alyssa Shah, Ab Rauf Barberis, Matteo Helikar, Tomáš |
author_facet | Wertheim, Kenneth Y. Puniya, Bhanwar Lal La Fleur, Alyssa Shah, Ab Rauf Barberis, Matteo Helikar, Tomáš |
author_sort | Wertheim, Kenneth Y. |
collection | PubMed |
description | Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology. |
format | Online Article Text |
id | pubmed-8376204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-83762042021-08-20 A multi-approach and multi-scale platform to model CD4+ T cells responding to infections Wertheim, Kenneth Y. Puniya, Bhanwar Lal La Fleur, Alyssa Shah, Ab Rauf Barberis, Matteo Helikar, Tomáš PLoS Comput Biol Research Article Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology. Public Library of Science 2021-08-03 /pmc/articles/PMC8376204/ /pubmed/34343169 http://dx.doi.org/10.1371/journal.pcbi.1009209 Text en © 2021 Wertheim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wertheim, Kenneth Y. Puniya, Bhanwar Lal La Fleur, Alyssa Shah, Ab Rauf Barberis, Matteo Helikar, Tomáš A multi-approach and multi-scale platform to model CD4+ T cells responding to infections |
title | A multi-approach and multi-scale platform to model CD4+ T cells responding to infections |
title_full | A multi-approach and multi-scale platform to model CD4+ T cells responding to infections |
title_fullStr | A multi-approach and multi-scale platform to model CD4+ T cells responding to infections |
title_full_unstemmed | A multi-approach and multi-scale platform to model CD4+ T cells responding to infections |
title_short | A multi-approach and multi-scale platform to model CD4+ T cells responding to infections |
title_sort | multi-approach and multi-scale platform to model cd4+ t cells responding to infections |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8376204/ https://www.ncbi.nlm.nih.gov/pubmed/34343169 http://dx.doi.org/10.1371/journal.pcbi.1009209 |
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