Monte Carlo estimates of natural variation in HIV infection

Heffernan, Jane M.; Wahl, Lindi M.

doi:10.1016/j.jtbi.2005.03.002

Cited by 44 publications

(28 citation statements)

References 42 publications

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“…They noted that there was positive probability that the virus could be eliminated by the process [9]. Monte Carlo approaches were also used by Kamina et al [10] and Heffernan and Wahl [11] to study the probability that an infection would not become established after exposure to a viral inoculum of a given size. Tuckwell and Corfec [12], [13] developed similar multi-dimensional models to study early infection but modeled them as diffusion processes via simulation of stochastic differential equations.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Theory of Early Viral Infection: Continuous versus Burst Production of Virions

2011

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Viral production from infected cells can occur continuously or in a burst that generally kills the cell. For HIV infection, both modes of production have been suggested. Standard viral dynamic models formulated as sets of ordinary differential equations can not distinguish between these two modes of viral production, as the predicted dynamics is identical as long as infected cells produce the same total number of virions over their lifespan. Here we show that in stochastic models of viral infection the two modes of viral production yield different early term dynamics. Further, we analytically determine the probability that infections initiated with any number of virions and infected cells reach extinction, the state when both the population of virions and infected cells vanish, and show this too has different solutions for continuous and burst production. We also compute the distributions of times to establish infection as well as the distribution of times to extinction starting from both a single virion as well as from a single infected cell for both modes of virion production.

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Section: Introductionmentioning

confidence: 99%

Stochastic Theory of Early Viral Infection: Continuous versus Burst Production of Virions

2011

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“…The analytical results were derived from idealized models whereas the MC simulations allowed for more accurate models that incorporate unequal base composition and an evolutionary based substitution matrix that defines the frequency at which base i is replaced by base j during a mutation event. Previously, Monte Carlo methods have been used to study the within-host dynamics of HIV-1 infection (Heffernan and Wahl, 2005; Kamina et al, 2001; Ribeiro and Bonhoeffer, 1999; Ruskin, 2002; Tan and Wu, 1998; Tuckwell and Le Corfec, 1998), but here our focus is on sequence evolution and not viral and T cell dynamics as in these earlier works. Keele and colleagues applied a variant of this model to the analysis of 102 B-clade infected patients (Keele et al, 2008) and of 18 experimentally SIV-infected rhesus macaques (Keele, 2009).…”

Section: Introductionmentioning

confidence: 99%

Modeling sequence evolution in acute HIV-1 infection

Lee

Giorgi

Keele

et al. 2009

Journal of Theoretical Biology

154

222

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We describe a mathematical model and Monte-Carlo (MC) simulation of viral evolution during acute infection. We consider both synchronous and asynchronous processes of viral infection of new target cells. The model enables an assessment of the expected sequence diversity in new HIV-1 infections originating from a single transmitted viral strain, estimation of the most recent common ancestor (MRCA) of the transmitted viral lineage, and estimation of the time to coalesce back to the MRCA. We also calculate the probability of the MRCA being the transmitted virus or an evolved variant. Excluding insertions and deletions, we assume HIV-1 evolves by base substitution without selection pressure during the earliest phase of HIV-1 infection prior to the immune response. Unlike phylogenetic methods that follow a lineage backwards to coalescence, we compare the observed data to a model of the diversification of a viral population forward in time. To illustrate the application of these methods, we provide detailed comparisons of the model and simulations results to 306 envelope sequences obtained from 8 newly infected subjects at a single time point. The data from 6/8 patients were in good agreement with model predictions, and hence compatible with a single-strain infection evolving under no selection pressure. The diversity of the samples from the other two patients was too great to be explained by the model, suggesting multiple HIV-1-strains were transmitted. The model can also be applied to longitudinal patient data to estimate within-host viral evolutionary parameters.

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“…Other modeling frameworks different to our approach, e.g. (Heffernan and Wahl, 2005), that consider the stochastic nature of the emergence and persistence of a DS strain and the eventual replacement of the DR strain might also be applicable to study our question.…”

Section: Discussionmentioning

confidence: 99%

Estimation of the HIV-1 backward mutation rate from transmitted drug-resistant strains

Kitayimbwa

Mugisha

Saenz

2016

Theoretical Population Biology

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One of the serious threats facing the administration of antiretroviral therapy to human immunodeficiency virus (HIV-1) infected patients is the reported increasing prevalence of transmitted drug resistance. However, given that HIV-1 drug-resistant strains are often less fit than the wild-type strains, it is expected that drug-resistant strains that are present during the primary phase of the HIV-1 infection are replaced by the fitter wild-type strains. This replacement of HIV-1 resistant mutations involves the emergence of wild-type strains by a process of backward mutation. How quickly the replacement happens is dependent on the class of HIV-1 mutation group.We estimate the backward mutation rates and relative fitness of various mutational groups known to confer HIV-1 drug resistance. We do this by fitting a stochastic model to data for individuals who were originally infected by an HIV-1 strain carrying any one of the known drug resistance-conferring mutations and observed over a period of time to see whether the resistant strain is replaced. To do this, we seek a distribution, generated from simulations of the stochastic model, that best describes the observed (clinical data) replacement times of a given mutation. We found that Lamivudine/Emtricitabine-associated mutations have a distinctly higher, backward mutation rate and low relative fitness compared to the other classes (as has been reported before) while protease inhibitors-associated mutations have a slower backward mutation rate and high relative fitness. For the other mutation classes, we found more uncertainty in their estimates.

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Monte Carlo estimates of natural variation in HIV infection

Cited by 44 publications

References 42 publications

Stochastic Theory of Early Viral Infection: Continuous versus Burst Production of Virions

Stochastic Theory of Early Viral Infection: Continuous versus Burst Production of Virions

Modeling sequence evolution in acute HIV-1 infection

Estimation of the HIV-1 backward mutation rate from transmitted drug-resistant strains

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