Differential equation modeling of HIV viral fitness experiments: model identification, model selection, and multimodel inference.
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.
Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA.
Hongyu
H
Miao
Carrie
C
Dykes
Lisa M
LM
Demeter
Hulin
H
Wu
D001699:Biometry / Q000379:methods*; D015497:HIV-1 / Q000235:genetics / Q000502:physiology*; D008954:Models, Biological; D009154:Mutation; D018406:Virus Physiological Phenomena