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Consistent Robustness Analysis (CRA) Identifies Biologically Relevant Properties of Regulatory Network Models

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  • Treenut Saithong
  • Kevin J Painter
  • Andrew J Millar

Abstract

Background: A number of studies have previously demonstrated that “goodness of fit” is insufficient in reliably classifying the credibility of a biological model. Robustness and/or sensitivity analysis is commonly employed as a secondary method for evaluating the suitability of a particular model. The results of such analyses invariably depend on the particular parameter set tested, yet many parameter values for biological models are uncertain. Results: Here, we propose a novel robustness analysis that aims to determine the “common robustness” of the model with multiple, biologically plausible parameter sets, rather than the local robustness for a particular parameter set. Our method is applied to two published models of the Arabidopsis circadian clock (the one-loop [1] and two-loop [2] models). The results reinforce current findings suggesting the greater reliability of the two-loop model and pinpoint the crucial role of TOC1 in the circadian network. Conclusions: Consistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.

Suggested Citation

  • Treenut Saithong & Kevin J Painter & Andrew J Millar, 2010. "Consistent Robustness Analysis (CRA) Identifies Biologically Relevant Properties of Regulatory Network Models," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-11, December.
    • Handle: RePEc:plo:pone00:0015589
    DOI: 10.1371/journal.pone.0015589
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    References listed on IDEAS

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    1. John J. Tyson, 2007. "Bringing cartoons to life," Nature, Nature, vol. 445(7130), pages 823-823, February.
    2. Albert Goldbeter, 2002. "Computational approaches to cellular rhythms," Nature, Nature, vol. 420(6912), pages 238-245, November.
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    Cited by:

    1. Malesios, C., 2016. "Measuring the robustness of the journal h-index with respect to publication and citation values: A Bayesian sensitivity analysis," Journal of Informetrics, Elsevier, vol. 10(3), pages 719-731.
    2. Andreas Wagner, 2015. "Causal Drift, Robust Signaling, and Complex Disease," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-29, March.

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