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Determining minimal output sets that ensure structural identifiability

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  • D Joubert
  • J D Stigter
  • J Molenaar

Abstract

The process of inferring parameter values from experimental data can be a cumbersome task. In addition, the collection of experimental data can be time consuming and costly. This paper covers both these issues by addressing the following question: “Which experimental outputs should be measured to ensure that unique model parameters can be calculated?”. Stated formally, we examine the topic of minimal output sets that guarantee a model’s structural identifiability. To that end, we introduce an algorithm that guides a researcher as to which model outputs to measure. Our algorithm consists of an iterative structural identifiability analysis and can determine multiple minimal output sets of a model. This choice in different output sets offers researchers flexibility during experimental design. Our method can determine minimal output sets of large differential equation models within short computational times.

Suggested Citation

  • D Joubert & J D Stigter & J Molenaar, 2018. "Determining minimal output sets that ensure structural identifiability," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0207334
    DOI: 10.1371/journal.pone.0207334
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    Cited by:

    1. Lam, Nicholas N. & Docherty, Paul D. & Murray, Rua, 2022. "Practical identifiability of parametrised models: A review of benefits and limitations of various approaches," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 199(C), pages 202-216.

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