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In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches
The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune re...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3997136/ https://www.ncbi.nlm.nih.gov/pubmed/24804217 http://dx.doi.org/10.1155/2014/371809 |
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author | Belfiore, Mariagrazia Pennisi, Marzio Aricò, Giuseppina Ronsisvalle, Simone Pappalardo, Francesco |
author_facet | Belfiore, Mariagrazia Pennisi, Marzio Aricò, Giuseppina Ronsisvalle, Simone Pappalardo, Francesco |
author_sort | Belfiore, Mariagrazia |
collection | PubMed |
description | The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels. |
format | Online Article Text |
id | pubmed-3997136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39971362014-05-06 In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches Belfiore, Mariagrazia Pennisi, Marzio Aricò, Giuseppina Ronsisvalle, Simone Pappalardo, Francesco Biomed Res Int Review Article The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels. Hindawi Publishing Corporation 2014 2014-04-06 /pmc/articles/PMC3997136/ /pubmed/24804217 http://dx.doi.org/10.1155/2014/371809 Text en Copyright © 2014 Mariagrazia Belfiore et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Belfiore, Mariagrazia Pennisi, Marzio Aricò, Giuseppina Ronsisvalle, Simone Pappalardo, Francesco In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches |
title | In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches |
title_full | In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches |
title_fullStr | In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches |
title_full_unstemmed | In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches |
title_short | In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches |
title_sort | in silico modeling of the immune system: cellular and molecular scale approaches |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3997136/ https://www.ncbi.nlm.nih.gov/pubmed/24804217 http://dx.doi.org/10.1155/2014/371809 |
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