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Bioinformatics Advance Access published online on December 21, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti230
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received August 24, 2004
Revised December 13, 2004
Accepted December 15, 2004

Article

Evaluation of HIV-1 kinetic models using quantitative discrimination analysis

Andrea L. Knorr 1 and Ranjan Srivastava 1*

1 Department of Chemical Engineering, University of Connecticut, Storrs, CT 06269, USA

* To whom correspondence should be addressed.
Ranjan Srivastava, E-mail: srivasta{at}engr.uconn.edu


  Abstract

Motivation: Since the identification of human immunodeficiency virus (HIV) over twenty years ago, many mathematical models of HIV dynamics have been proposed. The purpose of this study was to evaluate intracellular and intercellular scale HIV models that best described the dynamics of viral and cell titers of a person, where parameters were determined using typically available patient data. In this case, "best" was defined as the model most capable of describing experimental patient data and was determined by Bayesian-based model discrimination analysis and the ability to provide realistic results.

Results: Twenty models of HIV-1 viral dynamics were initially evaluated to determine whether parameters could be obtained from readily available clinical data from established HIV-1 patients with stable disease. Based on this analysis, three models were chosen for further examination and comparison. Parameters were estimated using experimental data from a cohort of 338 people monitored for up to 2,484 days. The models were evaluated using a Bayesian technique to determine which model was most probable. The model ultimately selected as most probable was overwhelmingly favored relative to the remaining two models, and it accounted for uninfected cells, infected cells, and cytotoxic T lymphocyte (CTL) dynamics. The authors developed a fourth model for comparison purposes by combining the features of the original three models. Parameters were estimated for the new model and the statistical analysis was repeated for all four models. The model that was initially favored was selected again upon model discrimination analysis.


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A. L. Knorr, R. Jain, and R. Srivastava
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[Abstract] [Full Text] [PDF]


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