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Accession Number ADA570913
Title Quantitative Quasispecies Theory-Based Model of Virus Escape Mutation Under Immune Selection.
Publication Date 2012
Media Count 7p
Personal Author H. Woo J. Reifman
Abstract Viral infections involve a complex interplay of the immune response and escape mutation of the virus quasispecies inside a single host. Although fundamental aspects of such a balance of mutation and selection pressure have been established by the quasispecies theory decades ago, its implications have largely remained qualitative. Here, we present a quantitative approach to model the virus evolution under cytotoxic T-lymphocyte immune response. The virus quasispecies dynamics are explicitly represented by mutations in the combined sequence space of a set of epitopes within the viral genome. We stochastically simulated the growth of a viral population originating from a single wild-type founder virus and its recognition and clearance by the immune response, as well as the expansion of its genetic diversity. Applied to the immune escape of a simian immunodeficiency virus epitope, model predictions were quantitatively comparable to the experimental data. Within the model parameter space, we found two qualitatively different regimes of infectious disease pathogenesis, each representing alternative fates of the immune response: It can clear the infection in finite time or eventually be overwhelmed by viral growth and escape mutation. The latter regime exhibits the characteristic disease progression pattern of human immunodeficiency virus, while the former is bounded by maximum mutation rates that can be suppressed by the immune response. Our results demonstrate that, by explicitly representing epitope mutations and thus providing a genotype phenotype map, the quasispecies theory can form the basis of a detailed sequence-specific model of real-world viral pathogens evolving under immune selection.
Keywords Chromosomes
Human immunodeficiency viruses
Infectious diseases
Monkey b virus
Next-generation sequencing
Pathogenic microorganisms
Population dynamics
Stochastic simulation
Virus diseases

Source Agency Non Paid ADAS
NTIS Subject Category 57E - Clinical Medicine
57K - Microbiology
Corporate Author Army Medical Research and Materiel Command (Provisional), Fort Detrick, MD. Telemedicine and Advanced Tech Research Center.
Document Type Journal article
Title Note Journal article.
NTIS Issue Number 1315
Contract Number N/A

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