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Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis
Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563504/ https://www.ncbi.nlm.nih.gov/pubmed/31100972 http://dx.doi.org/10.3390/v11050449 |
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author | Peter, Stephan Hölzer, Martin Lamkiewicz, Kevin di Fenizio, Pietro Speroni Al Hwaeer, Hassan Marz, Manja Schuster, Stefan Dittrich, Peter Ibrahim, Bashar |
author_facet | Peter, Stephan Hölzer, Martin Lamkiewicz, Kevin di Fenizio, Pietro Speroni Al Hwaeer, Hassan Marz, Manja Schuster, Stefan Dittrich, Peter Ibrahim, Bashar |
author_sort | Peter, Stephan |
collection | PubMed |
description | Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model’s organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area. |
format | Online Article Text |
id | pubmed-6563504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65635042019-06-17 Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis Peter, Stephan Hölzer, Martin Lamkiewicz, Kevin di Fenizio, Pietro Speroni Al Hwaeer, Hassan Marz, Manja Schuster, Stefan Dittrich, Peter Ibrahim, Bashar Viruses Article Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model’s organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area. MDPI 2019-05-16 /pmc/articles/PMC6563504/ /pubmed/31100972 http://dx.doi.org/10.3390/v11050449 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Peter, Stephan Hölzer, Martin Lamkiewicz, Kevin di Fenizio, Pietro Speroni Al Hwaeer, Hassan Marz, Manja Schuster, Stefan Dittrich, Peter Ibrahim, Bashar Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis |
title | Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis |
title_full | Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis |
title_fullStr | Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis |
title_full_unstemmed | Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis |
title_short | Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis |
title_sort | structure and hierarchy of influenza virus models revealed by reaction network analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563504/ https://www.ncbi.nlm.nih.gov/pubmed/31100972 http://dx.doi.org/10.3390/v11050449 |
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