Procedures for reliable estimation of viral fitness from time-series data

In order to develop a better understanding of the evolutionary dynamics of HIV drug resistance, it is necessary to quantify accurately the in vivo fitness costs of resistance mutations. However, the reliable estimation of such fitness costs is riddled with both theoretical and experimental difficult...

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Bibliographic Details
Published in:Proceedings of the Royal Society. B, Biological sciences Biological sciences, 2002-09, Vol.269 (1503), p.1887-1893
Main Authors: Bonhoeffer, Sebastian, Barbour, Andrew D., De Boer, Rob J.
Format: Article
Language:English
Subjects:
Anti-HIV Agents - pharmacology
Cient
Coefficients
Competition Experiments
Competitive Behavior
Computer Simulation
Drug Resistance, Viral
Ecological competition
Estimate reliability
Estimation methods
Evolution, Molecular
Fitness
HIV
HIV - drug effects
HIV - genetics
HIV - physiology
HIV 1
Humans
Internet
Mortality
Mutation
Parameter Estimation
Population estimates
Selection Coef
Selection, Genetic
Sensitivity and Specificity
Standard deviation
Time Factors
Viral Load
Virus Replication - drug effects
Viruses
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Summary:In order to develop a better understanding of the evolutionary dynamics of HIV drug resistance, it is necessary to quantify accurately the in vivo fitness costs of resistance mutations. However, the reliable estimation of such fitness costs is riddled with both theoretical and experimental difficulties. Experimental fitness assays typically suffer from the shortcoming that they are based on in vitro data. Fitness estimates based on the mathematical analysis of in vivo data, however, are often questionable because the underlying assumptions are not fulfilled. In particular, the assumption that the replication rate of the virus population is constant in time is frequently grossly violated. By extending recent work of Marée and colleagues, we present here a new approach that corrects for time-dependent viral replication in time-series data for growth competition of mutants. This approach allows a reliable estimation of the relative replicative capacity (with confidence intervals) of two competing virus variants growing within the same patient, using longitudinal data for the total plasma virus load, the relative frequency of the two variants and the death rate of infected cells. We assess the accuracy of our method using computer-generated data. An implementation of the developed method is freely accessible on the Web (http://www.eco.ethz.ch/fitness.html).
ISSN:0962-8452
1471-2954
DOI:10.1098/rspb.2002.2097