Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A stochastic model for early HIV-1 population dynamics

H C Tuckwell1, E Le Corfec

  • 1Epidémiologie et Sciences de l'Information, Université Paris 6, INSERM U444, Institut Fédératif de Recherche sur la Santé St-Antoine, 27 rue Chaligny, Paris Cedex 12, F-75571, France.

Journal of Theoretical Biology
|December 5, 1998
PubMed
Summary

This study models early human immunodeficiency virus type-1 (HIV-1) infection dynamics using a stochastic approach. Results show significant variability in viral load peaks, with implications for HIV detection strategies.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A mathematical model for evaluating the impact of vaccination schedules: application to Neisseria meningitidis.

Epidemiology and infection·2003
Same author

Epidemic spread and bifurcation effects in two-dimensional network models with viral dynamics.

Physical review. E, Statistical, nonlinear, and soft matter physics·2001
Same author

Nature of equilibria and effects of drug treatments in some simple viral population dynamical models.

IMA journal of mathematics applied in medicine and biology·2001
Same author

Cortical potential distributions and information processing.

Neural computation·2000
Same author

Enhancement of epidemic spread by noise and stochastic resonance in spatial network models with viral dynamics.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics·2000
Same author

[HIV-1 quantitative dynamics in vivo: a review of mathematical models].

Revue d'epidemiologie et de sante publique·2000

Area of Science:

  • Mathematical modeling
  • Virology
  • Immunology

Background:

  • Human immunodeficiency virus type-1 (HIV-1) infection involves complex population dynamics of infected and uninfected CD4(+)T cells and free virions.
  • Stochastic effects, or chance mechanisms, play a role in the early stages of HIV-1 infection, influencing cell activation and infection progression.

Purpose of the Study:

  • To develop and investigate a simple stochastic mathematical model for early HIV-1 population dynamics.
  • To estimate the variability in viral load peaks and their timing during initial HIV-1 infection.
  • To explore the impact of parameter variations on HIV-1 growth and response variability.

Main Methods:

  • A multi-dimensional diffusion process model was developed, incorporating uninfected CD4(+)T cells, latently and actively infected CD4(+)T cells, and plasma virions.

Related Experiment Videos

  • Stochastic effects were modeled in CD4(+)T cell infection and transitions between cell states.
  • The stochastic system was solved numerically using current parameter values to analyze intrinsic variability and the effects of parameter perturbations.

    • Main Results:

    • The model predicts viral load peaks within the experimental range, with a 95% confidence interval for peak occurrence between 19.4 and 25.1 days.
    • Stochasticity significantly impacts viral density growth in the initial days post-infection, with threshold effects observed at low virion levels.
    • Parameter changes related to cell death rates and viral clearance/production rates influenced response magnitude and variability, while initial dose and latent cell conversion rates had minor effects.

    Conclusions:

    • Stochastic modeling provides valuable insights into the intrinsic variability of early HIV-1 infection dynamics.
    • The model's predictions align with experimental observations and can inform the development of HIV detection and testing procedures.
    • Understanding parameter sensitivities aids in assessing between-patient variability and refining predictive models for HIV-1 progression.