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A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV

To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here...

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Detalles Bibliográficos
Autores principales: Kreger, Jesse, Komarova, Natalia L., Wodarz, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730440/
https://www.ncbi.nlm.nih.gov/pubmed/34936647
http://dx.doi.org/10.1371/journal.pcbi.1009713
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author Kreger, Jesse
Komarova, Natalia L.
Wodarz, Dominik
author_facet Kreger, Jesse
Komarova, Natalia L.
Wodarz, Dominik
author_sort Kreger, Jesse
collection PubMed
description To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability.
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spelling pubmed-87304402022-01-06 A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV Kreger, Jesse Komarova, Natalia L. Wodarz, Dominik PLoS Comput Biol Research Article To study viral evolutionary processes within patients, mathematical models have been instrumental. Yet, the need for stochastic simulations of minority mutant dynamics can pose computational challenges, especially in heterogeneous systems where very large and very small sub-populations coexist. Here, we describe a hybrid stochastic-deterministic algorithm to simulate mutant evolution in large viral populations, such as acute HIV-1 infection, and further include the multiple infection of cells. We demonstrate that the hybrid method can approximate the fully stochastic dynamics with sufficient accuracy at a fraction of the computational time, and quantify evolutionary end points that cannot be expressed by deterministic models, such as the mutant distribution or the probability of mutant existence at a given infected cell population size. We apply this method to study the role of multiple infection and intracellular interactions among different virus strains (such as complementation and interference) for mutant evolution. Multiple infection is predicted to increase the number of mutants at a given infected cell population size, due to a larger number of infection events. We further find that viral complementation can significantly enhance the spread of disadvantageous mutants, but only in select circumstances: it requires the occurrence of direct cell-to-cell transmission through virological synapses, as well as a substantial fitness disadvantage of the mutant, most likely corresponding to defective virus particles. This, however, likely has strong biological consequences because defective viruses can carry genetic diversity that can be incorporated into functional virus genomes via recombination. Through this mechanism, synaptic transmission in HIV might promote virus evolvability. Public Library of Science 2021-12-22 /pmc/articles/PMC8730440/ /pubmed/34936647 http://dx.doi.org/10.1371/journal.pcbi.1009713 Text en © 2021 Kreger 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
Kreger, Jesse
Komarova, Natalia L.
Wodarz, Dominik
A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV
title A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV
title_full A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV
title_fullStr A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV
title_full_unstemmed A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV
title_short A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV
title_sort hybrid stochastic-deterministic approach to explore multiple infection and evolution in hiv
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8730440/
https://www.ncbi.nlm.nih.gov/pubmed/34936647
http://dx.doi.org/10.1371/journal.pcbi.1009713
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