Consistent Robustness Analysis (CRA) Identifies Biologically Relevant Properties of Regulatory Network Models
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Author list: Saithong T., Painter K.J., Millar A.J.
Publisher: Public Library of Science
Publication year: 2010
Journal: PLoS ONE (1932-6203)
Volume number: 5
Issue number: 12
Start page: 1
End page: 11
Number of pages: 11
ISSN: 1932-6203
eISSN: 1932-6203
Languages: English-Great Britain (EN-GB)
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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. ฉ 2010 Saithong et al.
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