ArticlePloS one2010
Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models.
Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Causal drift, robust signaling, and complex disease.PloS one · 2015Article
- Velocity response curves demonstrate the complexity of modeling entrainable clocks.Journal of theoretical biology · 2014Article
- Model Misinterpretation within Biology: Phenotypes, Statistics, Networks, and Inference.Frontiers in plant science · 2012Article
- Efficient characterization of high-dimensional parameter spaces for systems biology.BMC systems biology · 2011Article
- The contributions of interlocking loops and extensive nonlinearity to the properties of circadian clock models.PloS one · 2010Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundA 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.
resultsHere, 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.
conclusionsConsistent Robustness Analysis can indicate both the relative plausibility of different models and also the critical components and processes controlling each model.
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