Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models

Treenut Saithong; Kevin J Painter; Andrew J Millar

doi:10.1371/journal.pone.0015589

Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models

Treenut Saithong, Kevin J Painter, Andrew J Millar

School of Biological Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Article number	e15589
Number of pages	11
Journal	PLoS ONE
Volume	5
Issue number	12
DOIs	https://doi.org/10.1371/journal.pone.0015589
Publication status	Published - Dec 2010

Keywords

Algorithms
Arabidopsis
Circadian Clocks
Circadian Rhythm
Computer Simulation
Gene Expression Regulation, Plant
Gene Regulatory Networks
Glycolysis
Models, Genetic
Models, Statistical
Models, Theoretical
Oscillometry
Signal Transduction

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SynthSys; formerly CSBE: Centre for Systems Biology at Edinburgh
Millar, A., Beggs, J., Ghazal, P., Goryanin, I., Hillston, J., Plotkin, G., Tollervey, D., Walton, A. & Robertson, K.
BBSRC
8/01/07 → 31/12/12
Project: Research

Cite this

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KW - Circadian Clocks

KW - Circadian Rhythm

KW - Computer Simulation

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KW - Glycolysis

KW - Models, Genetic

KW - Models, Statistical

KW - Models, Theoretical

KW - Oscillometry

KW - Signal Transduction

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Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network models

Abstract

Keywords

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SynthSys; formerly CSBE: Centre for Systems Biology at Edinburgh

Cite this