ArticleBulletin of mathematical biology2008
Modeling and estimation of kinetic parameters and replicative fitness of HIV-1 from flow-cytometry-based growth competition experiments.
Article in Bulletin of mathematical biology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.Viruses · 2025Article
- Identifiability investigation of within-host models of acute virus infection.Mathematical biosciences and engineering : MBE · 2024Article
- Identifiability investigation of within-host models of acute virus infection.bioRxiv : the preprint server for biology · 2024Article
- The effect of model structure and data availability on Usutu virus dynamics at three biological scales.Royal Society open science · 2024Article
- A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.Journal of biological dynamics · 2023Article
- Article
- Model Averaging in Viral Dynamic Models.The AAPS journal · 2020Article
- Structural and practical identifiability of dual-input kinetic modeling in dynamic PET of liver inflammation.Physics in medicine and biology · 2019Article
- Generalized Ordinary Differential Equation Models.Journal of the American Statistical Association · 2014Article
- Factors affecting relative fitness measurements in pairwise competition assays of human immunodeficiency viruses.Journal of virological methods · 2013Article
- A practical approach to parameter estimation applied to model predicting heart rate regulation.Journal of mathematical biology · 2013Article
- Evaluation of multitype mathematical models for CFSE-labeling experiment data.Bulletin of mathematical biology · 2012Article
- Modeling of influenza-specific CD8+ T cells during the primary response indicates that the spleen is a major source of effectors.Journal of immunology (Baltimore, Md. : 1950) · 2011Article
- ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS.SIAM review. Society for Industrial and Applied Mathematics · 2011Article
- ESTIMATION OF CONSTANT AND TIME-VARYING DYNAMIC PARAMETERS OF HIV INFECTION IN A NONLINEAR DIFFERENTIAL EQUATION MODEL.The annals of applied statistics · 2010Article
- Sieve Estimation of Constant and Time-Varying Coefficients in Nonlinear Ordinary Differential Equation Models by Considering Both Numerical Error and Measurement Error.Annals of statistics · 2010Article
- Differential equation modeling of HIV viral fitness experiments: model identification, model selection, and multimodel inference.Biometrics · 2009Article
- Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models.Journal of the American Statistical Association · 2008Article
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7 authors.
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Abstract
Growth competition assays have been developed to quantify the relative fitness of HIV-1 mutants. In this article, we develop mathematical models to describe viral/cellular dynamic interactions in the assay system from which the competitive fitness indices or parameters are defined. In our previous HIV-viral fitness experiments, the concentration of uninfected target cells was assumed to be constant (Wu et al. 2006). But this may not be true in some experiments. In addition, dual infection may frequently occur in viral fitness experiments and may not be ignorable. Here, we relax these two assumptions and extend our earlier viral fitness model (Wu et al. 2006). The resulting models then become nonlinear ODE systems for which closed-form solutions are not achievable. In the new model, the viral relative fitness is a function of time since it depends on the target cell concentration. First, we studied the structure identifiability of the nonlinear ODE models. The identifiability analysis showed that all parameters in the proposed models are identifiable from the flow-cytometry-based experimental data that we collected. We then employed a global optimization approach (the differential evolution algorithm) to directly estimate the kinetic parameters as well as the relative fitness index in the nonlinear ODE models using nonlinear least square regression based on the experimental data. Practical identifiability was investigated via Monte Carlo simulations.
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