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Differential Equation Modeling of HIV Viral Fitness Experiments: Model Identification, Model Selection, and Multimodel Inference
Author(s) -
Miao Hongyu,
Dykes Carrie,
Demeter Lisa M.,
Wu Hulin
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01059.x
Subject(s) - identifiability , inference , ode , model selection , identification (biology) , ordinary differential equation , computer science , statistical inference , selection (genetic algorithm) , set (abstract data type) , system identification , estimation theory , nonlinear system , machine learning , mathematics , differential equation , artificial intelligence , data mining , algorithm , statistics , biology , mathematical analysis , physics , quantum mechanics , measure (data warehouse) , programming language , botany
Summary Many biological processes and systems can be described by a set of differential equation (DE) models. However, literature in statistical inference for DE models is very sparse. We propose statistical estimation, model selection, and multimodel averaging methods for HIV viral fitness experiments in vitro that can be described by a set of nonlinear ordinary differential equations (ODE). The parameter identifiability of the ODE models is also addressed. We apply the proposed methods and techniques to experimental data of viral fitness for HIV‐1 mutant 103N. We expect that the proposed modeling and inference approaches for the DE models can be widely used for a variety of biomedical studies.