Journal
BIOMETRICS
Volume 65, Issue 1, Pages 292-300Publisher
WILEY
DOI: 10.1111/j.1541-0420.2008.01059.x
Keywords
Differential equation modeling; Global optimization; HIV dual infection; Identifiability analysis; Model selection; Multimodel inference; Viral fitness
Funding
- NIAID/NIH [AI50020, AI052765, AI055290, AI 065217, AI27658]
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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.
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