ArticlePLoS computational biology2009
'Glocal' robustness analysis and model discrimination for circadian oscillators.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- Design centering enables robustness screening of pattern formation models.Bioinformatics (Oxford, England) · 2022Article
- Coupling protocol of interlocked feedback oscillators in circadian clocks.Journal of the Royal Society, Interface · 2020Article
- Parameter estimation in models of biological oscillators: an automated regularised estimation approach.BMC bioinformatics · 2019Article
- Computational analysis of viable parameter regions in models of synthetic biological systems.Journal of biological engineering · 2019Article
- Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data.Royal Society open science · 2017Article
- LScientific reports · 2017Article
- Feedback, Mass Conservation and Reaction Kinetics Impact the Robustness of Cellular Oscillations.PLoS computational biology · 2016Article
- Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.BMC systems biology · 2015Article
- Causal drift, robust signaling, and complex disease.PloS one · 2015Article
- How informative is your kinetic model?: using resampling methods for model invalidation.BMC systems biology · 2014Article
- Data-driven quantification of the robustness and sensitivity of cell signaling networks.Physical biology · 2013Article
- Functional tunability of biological circuits from additional toggle switches.IET systems biology · 2013Article
- Inverse problems from biomedicine: inference of putative disease mechanisms and robust therapeutic strategies.Journal of mathematical biology · 2013Article
- Architecture-dependent robustness in a class of multiple positive feedback loops.IET systems biology · 2013Article
- Mapping behavioral specifications to model parameters in synthetic biology.BMC bioinformatics · 2013Article
- Effect of network architecture on synchronization and entrainment properties of the circadian oscillations in the suprachiasmatic nucleus.PLoS computational biology · 2012Article
- Article
- Efficient characterization of high-dimensional parameter spaces for systems biology.BMC systems biology · 2011Article
- Article
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4 authors.
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Abstract
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.
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