Literature DB >> 11217892

Small variations in multiple parameters account for wide variations in HIV-1 set-points: a novel modelling approach.

V Müller1, A F Marée, R J De Boer.   

Abstract

Steady-state levels of HIV-1 viraemia in the plasma vary more than a 1,000-fold between HIV-positive patients and are thought to be influenced by several different host and viral factors such as host target cell availability, host anti-HIV immune response and the virulence of the virus. Previous mathematical models have taken the form of classical ecological food-chain models and are unable to account for this multifactorial nature of the disease. These models suggest that the steady-state viral load (i.e. the set-point) is determined by immune response parameters only. We have devised a generalized consensus model in which the conventional parameters are replaced by so-called 'process functions'. This very general approach yields results that are insensitive to the precise form of the mathematical model. Here we applied the approach to HIV-1 infections by estimating the steady-state values of several process functions from published patient data. Importantly, these estimates are generic because they are independent of the precise form of the underlying processes. We recorded the variation in the estimated steady-state values of the process functions in a group of HIV-1 patients. We developed a novel model by providing explicit expressions for the process functions having the highest patient-to-patient variation in their estimated values. Small variations from patient to patient for several parameters of the new model collectively accounted for the large variations observed in the steady-state viral burden. The novel model remains in full agreement with previous models and data.

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Year:  2001        PMID: 11217892      PMCID: PMC1088597          DOI: 10.1098/rspb.2000.1358

Source DB:  PubMed          Journal:  Proc Biol Sci        ISSN: 0962-8452            Impact factor:   5.349


  40 in total

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2.  Antiviral pressure exerted by HIV-1-specific cytotoxic T lymphocytes (CTLs) during primary infection demonstrated by rapid selection of CTL escape virus.

Authors:  P Borrow; H Lewicki; X Wei; M S Horwitz; N Peffer; H Meyers; J A Nelson; J E Gairin; B H Hahn; M B Oldstone; G M Shaw
Journal:  Nat Med       Date:  1997-02       Impact factor: 53.440

3.  Quantitative image analysis of HIV-1 infection in lymphoid tissue.

Authors:  A T Haase; K Henry; M Zupancic; G Sedgewick; R A Faust; H Melroe; W Cavert; K Gebhard; K Staskus; Z Q Zhang; P J Dailey; H H Balfour; A Erice; A S Perelson
Journal:  Science       Date:  1996-11-08       Impact factor: 47.728

4.  Population dynamics of immune responses to persistent viruses.

Authors:  M A Nowak; C R Bangham
Journal:  Science       Date:  1996-04-05       Impact factor: 47.728

5.  HIV-1 dynamics in vivo: virion clearance rate, infected cell life-span, and viral generation time.

Authors:  A S Perelson; A U Neumann; M Markowitz; J M Leonard; D D Ho
Journal:  Science       Date:  1996-03-15       Impact factor: 47.728

6.  Clinical data sets of human immunodeficiency virus type 1 reverse transcriptase-resistant mutants explained by a mathematical model.

Authors:  N I Stilianakis; C A Boucher; M D De Jong; R Van Leeuwen; R Schuurman; R J De Boer
Journal:  J Virol       Date:  1997-01       Impact factor: 5.103

7.  Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection.

Authors:  D D Ho; A U Neumann; A S Perelson; W Chen; J M Leonard; M Markowitz
Journal:  Nature       Date:  1995-01-12       Impact factor: 49.962

8.  Prognosis in HIV-1 infection predicted by the quantity of virus in plasma.

Authors:  J W Mellors; C R Rinaldo; P Gupta; R M White; J A Todd; L A Kingsley
Journal:  Science       Date:  1996-05-24       Impact factor: 47.728

9.  High levels of anti-human immunodeficiency virus type 1 (HIV-1) memory cytotoxic T-lymphocyte activity and low viral load are associated with lack of disease in HIV-1-infected long-term nonprogressors.

Authors:  C Rinaldo; X L Huang; Z F Fan; M Ding; L Beltz; A Logar; D Panicali; G Mazzara; J Liebmann; M Cottrill
Journal:  J Virol       Date:  1995-09       Impact factor: 5.103

10.  Genomic structure of an attenuated quasi species of HIV-1 from a blood transfusion donor and recipients.

Authors:  N J Deacon; A Tsykin; A Solomon; K Smith; M Ludford-Menting; D J Hooker; D A McPhee; A L Greenway; A Ellett; C Chatfield; V A Lawson; S Crowe; A Maerz; S Sonza; J Learmont; J S Sullivan; A Cunningham; D Dwyer; D Dowton; J Mills
Journal:  Science       Date:  1995-11-10       Impact factor: 47.728

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  23 in total

1.  Recruitment times, proliferation, and apoptosis rates during the CD8(+) T-cell response to lymphocytic choriomeningitis virus.

Authors:  R J De Boer; M Oprea; R Antia; K Murali-Krishna; R Ahmed; A S Perelson
Journal:  J Virol       Date:  2001-11       Impact factor: 5.103

2.  Understanding the failure of CD8+ T-cell vaccination against simian/human immunodeficiency virus.

Authors:  Rob J De Boer
Journal:  J Virol       Date:  2007-01-03       Impact factor: 5.103

3.  A low dimensional dynamical model of the initial pulmonary innate response to infection.

Authors:  Todd R Young; Richard Buckalew; Addison K May; Erik M Boczko
Journal:  Math Biosci       Date:  2012-01-04       Impact factor: 2.144

4.  Estimate of effective recombination rate and average selection coefficient for HIV in chronic infection.

Authors:  Rebecca Batorsky; Mary F Kearney; Sarah E Palmer; Frank Maldarelli; Igor M Rouzine; John M Coffin
Journal:  Proc Natl Acad Sci U S A       Date:  2011-03-21       Impact factor: 11.205

5.  Boosting immunity by antiviral drug therapy: a simple relationship among timing, efficacy, and success.

Authors:  Natalia L Komarova; Eleanor Barnes; Paul Klenerman; Dominik Wodarz
Journal:  Proc Natl Acad Sci U S A       Date:  2003-02-06       Impact factor: 11.205

6.  The race between initial T-helper expansion and virus growth upon HIV infection influences polyclonality of the response and viral set-point.

Authors:  H Korthals Altes; R M Ribeiro; R J de Boer
Journal:  Proc Biol Sci       Date:  2003-07-07       Impact factor: 5.349

Review 7.  Modeling the within-host dynamics of HIV infection.

Authors:  Alan S Perelson; Ruy M Ribeiro
Journal:  BMC Biol       Date:  2013-09-03       Impact factor: 7.431

8.  The evolutionary dynamics of a rapidly mutating virus within and between hosts: the case of hepatitis C virus.

Authors:  Fabio Luciani; Samuel Alizon
Journal:  PLoS Comput Biol       Date:  2009-11-13       Impact factor: 4.475

9.  Which of our modeling predictions are robust?

Authors:  Rob J De Boer
Journal:  PLoS Comput Biol       Date:  2012-07-26       Impact factor: 4.475

Review 10.  Modelling the course of an HIV infection: insights from ecology and evolution.

Authors:  Samuel Alizon; Carsten Magnus
Journal:  Viruses       Date:  2012-10-04       Impact factor: 5.048

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