Next Article in Journal
From Lesions to Viral Clones: Biological and Molecular Diversity amongst Autochthonous Brazilian Vaccinia Virus
Next Article in Special Issue
Genetic Diversity and Selective Pressure in Hepatitis C Virus Genotypes 1–6: Significance for Direct-Acting Antiviral Treatment and Drug Resistance
Previous Article in Journal
Advanced Molecular Surveillance of Hepatitis C Virus
Previous Article in Special Issue
Bioinformatics Tools for Small Genomes, Such as Hepatitis B Virus
Article Menu

Export Article

Open AccessArticle
Viruses 2015, 7(3), 1189-1217; doi:10.3390/v7031189

Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

1
National institute for Mathematical and Biological synthesis (NIMBioS), Knoxville, TN 37996, USA
2
Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
3
School of Public Health, University of Illinois at Chicago, Chicago, IL 60612, USA
4
School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104, USA
5
Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA
6
Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Viktor Müller
Received: 19 December 2014 / Revised: 27 February 2015 / Accepted: 3 March 2015 / Published: 13 March 2015
(This article belongs to the Special Issue Bioinformatics and Computational Biology of Viruses)
View Full-Text   |   Download PDF [983 KB, uploaded 12 May 2015]   |  

Abstract

Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results suggest that, in order to appropriately model early HIV/SIV dynamics, additional factors must be considered in the model development. These may include variability in monkey susceptibility to infection, within-host competition between different viruses for target cells at the initial site of virus replication in the mucosa, innate immune response, and possibly the inclusion of several different tissue compartments. The sobering news is that while an increase in model complexity is needed to explain the available experimental data, testing and rejection of more complex models may require more quantitative data than is currently available. View Full-Text
Keywords: early SIV/HIV infection; mathematical model; eclipse phase; stochastic; Gillespie algorithm early SIV/HIV infection; mathematical model; eclipse phase; stochastic; Gillespie algorithm
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Noecker, C.; Schaefer, K.; Zaccheo, K.; Yang, Y.; Day, J.; Ganusov, V.V. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics. Viruses 2015, 7, 1189-1217.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Viruses EISSN 1999-4915 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top