title: Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
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.
authors.affiliation: Department of Biological Sciences, Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Treenut
authors.initials: T
authors.lastname: Saithong
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Painter
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Millar
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics*; D057906:Circadian Clocks; D002940:Circadian Rhythm; D003198:Computer Simulation; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D006019:Glycolysis; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D009991:Oscillometry; D015398:Signal Transduction