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Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods

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  • Oana-Teodora Chis
  • Julio R Banga
  • Eva Balsa-Canto

Abstract

Analysing the properties of a biological system through in silico experimentation requires a satisfactory mathematical representation of the system including accurate values of the model parameters. Fortunately, modern experimental techniques allow obtaining time-series data of appropriate quality which may then be used to estimate unknown parameters. However, in many cases, a subset of those parameters may not be uniquely estimated, independently of the experimental data available or the numerical techniques used for estimation. This lack of identifiability is related to the structure of the model, i.e. the system dynamics plus the observation function. Despite the interest in knowing a priori whether there is any chance of uniquely estimating all model unknown parameters, the structural identifiability analysis for general non-linear dynamic models is still an open question. There is no method amenable to every model, thus at some point we have to face the selection of one of the possibilities. This work presents a critical comparison of the currently available techniques. To this end, we perform the structural identifiability analysis of a collection of biological models. The results reveal that the generating series approach, in combination with identifiability tableaus, offers the most advantageous compromise among range of applicability, computational complexity and information provided.

Suggested Citation

  • Oana-Teodora Chis & Julio R Banga & Eva Balsa-Canto, 2011. "Structural Identifiability of Systems Biology Models: A Critical Comparison of Methods," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-16, November.
    • Handle: RePEc:plo:pone00:0027755
    DOI: 10.1371/journal.pone.0027755
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    References listed on IDEAS

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    1. Ryan N Gutenkunst & Joshua J Waterfall & Fergal P Casey & Kevin S Brown & Christopher R Myers & James P Sethna, 2007. "Universally Sloppy Parameter Sensitivities in Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-8, October.
    2. Pablo Achard & Erik De Schutter, 2006. "Complex Parameter Landscape for a Complex Neuron Model," PLOS Computational Biology, Public Library of Science, vol. 2(7), pages 1-11, July.
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    1. Alejandro F Villaverde & Julio R Banga, 2017. "Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-18, November.
    2. Adam Mahdi & Nicolette Meshkat & Seth Sullivant, 2014. "Structural Identifiability of Viscoelastic Mechanical Systems," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.

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