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

Treenut Saithong, Kevin J. Painter, Andrew J. Millar

Research output: Contribution to journalArticle

5 Citations (Scopus)

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. © 2010 Saithong et al.

Original languageEnglish
Article numbere15589
Pages (from-to)1-11
Number of pages11
JournalPLoS ONE
Volume5
Issue number12
DOIs
Publication statusPublished - 2010

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