ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS

By Hongyu Miao, Xiaohua Xia, Alan S. Perelson, Hulin Wu. SIAM Review, 2011

655 citations recorded by the source corpus.

Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice.

Subfields: Genetics, Molecular Biology, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/68e0cdf5b6aa33caf1dcdaa51e093f0c36af82d3

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In Genetics

1. Natesh S. Pillai
2. Eleni N. Chatzi

In Molecular Biology

1. Natesh S. Pillai
2. Sayan Mukherjee
3. M. Chatzis
4. Andrew W. Smyth
5. K. McGoff
6. Eleni N. Chatzi

Citations in the corpus

• Global Identifiability of Differential Models (2018, neutral): “(a) “θ ∈ θ”: Some authors [6, 27, 29, 33, 47, 48, 52] defined the identifiability of all the parameters.”
• On the observability and identifiability of nonlinear structural and mechanical systems (2015, methodology): “Alternative methods for identifiability exist; for example, the similarity transformation method proposed in [20], and a thorough presentation of these methods is provided in [21].”
• Statistical inference for dynamical systems: A review (2012, background): “For a recent and comprehensive review of identifiability in ODEs, see [118].”
• Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters (2013, neutral): “, models describing HIV dynamics studied in [33, 47] and [34]).”