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Bioinformatics Advance Access published online on December 8, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl627
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 15, 2006
Revised December 5, 2006
Accepted December 5, 2006

Article

Quantitative performance metrics for robustness in circadian rhythms

Neda Bagheri 1, Jörg Stelling 2, and Francis J. Doyle III 3 *

1 Department of Electrical and Computer Engineering, University of California in Santa Barbara, California 93106-9560, USA
2 Institute of Computational Science, ETH Zurich, 8092 Zurich, Switzerland; Swiss Institute of Bioinformatics, ETH Zurich, 8092 Zurich, Switzerland
3 Department of Electrical and Computer Engineering, University of California in Santa Barbara, California 93106-9560, USA; Department of Chemical Engineering, University of California in Santa Barbara, California 93106-5080, USA

* To whom correspondence should be addressed.
Francis J. Doyle III, E-mail: frank.doyle{at}icb.ucsb.edu


  Abstract

Motivation: Sensitivity analysis provides key measures that aid in unraveling the design principles responsible for the robust performance of biological networks. Such metrics allow researchers to investigate comprehensively model performance, to develop more realistic models, and to design informative experiments. However, sensitivity analysis of oscillatory systems focuses on period and amplitude characteristics, while biologically relevant effects on phase are neglected.

Results: Here, we introduce a novel set of phase-based sensitivity metrics for performance: period, phase, corrected phase, and relative phase. Both state- and phase-based tools are applied to free-running Drosophila melanogaster and Mus musculus circadian models. Each metric produces unique sensitivity values used to rank parameters from least to most sensitive. Similarities among the resulting rank distributions strongly suggest a conservation of sensitivity with respect to parameter function and type. A consistent result, for instance, is that model performance of biological oscillators is more sensitive to global parameters than local (i.e. circadian specific) parameters. Discrepancies among these distributions highlight the individual metrics' definition of performance as specific parametric sensitivity values depend on the defined metric, or output.

Availability: An implementation of the algorithm in MATLAB (Mathworks, Inc.) is available from the authors.

Associate Editor: Martin Bishop
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