Parameter subset selection techniques for problems in mathematical biology View Full Text


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Article Info

DATE

2018-10-30

AUTHORS

Christian Haargaard Olsen, Johnny T. Ottesen, Ralph C. Smith, Mette S. Olufsen

ABSTRACT

Patient-specific models for diagnostics and treatment planning require reliable parameter estimation and model predictions. Mathematical models of physiological systems are often formulated as systems of nonlinear ordinary differential equations with many parameters and few options for measuring all state variables. Consequently, it can be difficult to determine which parameters can reliably be estimated from available data. This investigation highlights pitfalls associated with practical parameter identifiability and subset selection. The latter refer to the process associated with selecting a subset of parameters that can be identified uniquely by parameter estimation protocols. The methods will be demonstrated using five examples of increasing complexity, as well as with patient-specific model predicting arterial blood pressure. This study demonstrates that methods based on local sensitivities are preferable in terms of computational cost and model fit when good initial parameter values are available, but that global methods should be considered when initial parameter value is not known or poorly understood. For global sensitivity analysis, Morris screening provides results in terms of parameter sensitivity ranking at a much lower computational cost. More... »

PAGES

121-138

References to SciGraph publications

  • 1981. Automatic Differentiation: Techniques and Applications in NONE
  • 2015. A Review on Global Sensitivity Analysis Methods in UNCERTAINTY MANAGEMENT IN SIMULATION-OPTIMIZATION OF COMPLEX SYSTEMS
  • 2012-05-16. A practical approach to parameter estimation applied to model predicting heart rate regulation in JOURNAL OF MATHEMATICAL BIOLOGY
  • 2012-07-07. CasADi: A Symbolic Package for Automatic Differentiation and Optimal Control in RECENT ADVANCES IN ALGORITHMIC DIFFERENTIATION
  • 2006-12. DRAM: Efficient adaptive MCMC in STATISTICS AND COMPUTING
  • 2008-09-25. Parameter estimation and determinability analysis applied to Drosophila gap gene circuits in BMC SYSTEMS BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00422-018-0784-8

    DOI

    http://dx.doi.org/10.1007/s00422-018-0784-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1107924036

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30377765


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