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Modeling Evolutionary Dynamics of HIV Infection
We have modelled the within-patient evolutionary process during HIV infection. We have studied viral evolution at population level (competition on the same receptor) and at species level (competitions on different receptors). During the HIV infection, several mutants of the virus arise, which are ab...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2006
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119952/ http://dx.doi.org/10.1007/11885191_14 |
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author | Sguanci, Luca Liò, Pietro Bagnoli, Franco |
author_facet | Sguanci, Luca Liò, Pietro Bagnoli, Franco |
author_sort | Sguanci, Luca |
collection | PubMed |
description | We have modelled the within-patient evolutionary process during HIV infection. We have studied viral evolution at population level (competition on the same receptor) and at species level (competitions on different receptors). During the HIV infection, several mutants of the virus arise, which are able to use different chemokine receptors, in particular the CCR5 and CXCR4 coreceptors (termed R5 and X4 phenotypes, respectively). Phylogenetic inference of chemokine receptors suggests that virus mutational pathways may generate R5 variants able to interact with a wide range of chemokine receptors different from CXCR4. Using the chemokine tree topology as conceptual framework for HIV viral speciation, we present a model of viral phenotypic mutations from R5 to X4 strains which reflect HIV late infection dynamics. Our model investigates the action of Tumor Necrosis Factor in AIDS progression and makes suggestions on better design of HAART therapy. |
format | Online Article Text |
id | pubmed-7119952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2006 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71199522020-04-06 Modeling Evolutionary Dynamics of HIV Infection Sguanci, Luca Liò, Pietro Bagnoli, Franco Computational Methods in Systems Biology Article We have modelled the within-patient evolutionary process during HIV infection. We have studied viral evolution at population level (competition on the same receptor) and at species level (competitions on different receptors). During the HIV infection, several mutants of the virus arise, which are able to use different chemokine receptors, in particular the CCR5 and CXCR4 coreceptors (termed R5 and X4 phenotypes, respectively). Phylogenetic inference of chemokine receptors suggests that virus mutational pathways may generate R5 variants able to interact with a wide range of chemokine receptors different from CXCR4. Using the chemokine tree topology as conceptual framework for HIV viral speciation, we present a model of viral phenotypic mutations from R5 to X4 strains which reflect HIV late infection dynamics. Our model investigates the action of Tumor Necrosis Factor in AIDS progression and makes suggestions on better design of HAART therapy. 2006 /pmc/articles/PMC7119952/ http://dx.doi.org/10.1007/11885191_14 Text en © Springer-Verlag Berlin Heidelberg 2006 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Sguanci, Luca Liò, Pietro Bagnoli, Franco Modeling Evolutionary Dynamics of HIV Infection |
title | Modeling Evolutionary Dynamics of HIV Infection |
title_full | Modeling Evolutionary Dynamics of HIV Infection |
title_fullStr | Modeling Evolutionary Dynamics of HIV Infection |
title_full_unstemmed | Modeling Evolutionary Dynamics of HIV Infection |
title_short | Modeling Evolutionary Dynamics of HIV Infection |
title_sort | modeling evolutionary dynamics of hiv infection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7119952/ http://dx.doi.org/10.1007/11885191_14 |
work_keys_str_mv | AT sguanciluca modelingevolutionarydynamicsofhivinfection AT liopietro modelingevolutionarydynamicsofhivinfection AT bagnolifranco modelingevolutionarydynamicsofhivinfection |