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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-24921

Hafner, M; Koeppl, H; Hasler, M; Wagner, A (2009). 'Glocal' robustness analysis and model discrimination for circadian oscillators. PLoS Computational Biology, 5(10):e1000534.

Accepted Version


To characterize the behavior and robustness of cellular circuits with many unknown parameters is a major challenge for systems biology. Its difficulty rises exponentially with the number of circuit components. We here propose a novel analysis method to meet this challenge. Our method identifies the region of a high-dimensional parameter space where a circuit displays an experimentally observed behavior. It does so via a Monte Carlo approach guided by principal component analysis, in order to allow efficient sampling of this space. This 'global' analysis is then supplemented by a 'local' analysis, in which circuit robustness is determined for each of the thousands of parameter sets sampled in the global analysis. We apply this method to two prominent, recent models of the cyanobacterial circadian oscillator, an autocatalytic model, and a model centered on consecutive phosphorylation at two sites of the KaiC protein, a key circadian regulator. For these models, we find that the two-sites architecture is much more robust than the autocatalytic one, both globally and locally, based on five different quantifiers of robustness, including robustness to parameter perturbations and to molecular noise. Our 'glocal' combination of global and local analyses can also identify key causes of high or low robustness. In doing so, our approach helps to unravel the architectural origin of robust circuit behavior. Complementarily, identifying fragile aspects of system behavior can aid in designing perturbation experiments that may discriminate between competing mechanisms and different parameter sets.

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Department of Biochemistry
07 Faculty of Science > Department of Biochemistry

Special Collections > SystemsX.ch
Special Collections > SystemsX.ch > Research, Technology and Development Projects > YeastX
DDC:570 Life sciences; biology
Deposited On:01 Dec 2009 13:29
Last Modified:28 Nov 2013 01:05
Publisher:Public Library of Science
Publisher DOI:10.1371/journal.pcbi.1000534
PubMed ID:19834597
Citations:Web of Science®. Times Cited: 21
Google Scholar™
Scopus®. Citation Count: 27

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