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• W2051913360 abstract "Many biomolecular networks are still widely unknown. With high structural uncertainties, bottom-up identification based on model discrimination tends to fail because of combinatorially many possible model structures. Top-down approaches, in contrast, describe general signal processing properties, rather than specific mechanisms. Most of these approaches originated in engineering disciplines where linearity is commonly (approximately) given, but naturally evolved networks exhibit a rather high degree of nonlinearity. Here, we present a top-down structural identification method that is not only applicable to nonlinear biomolecular networks, but indeed requires the network under study to be nonlinear. Similar to traditional frequency domain analysis, we apply an oscillatory input signal to a biomolecular network. However, instead of varying the frequency, we vary the mean value of the oscillatory signal to detect, discriminate, and partly characterize single feedback and feedforward loops in nonlinear networks." @default.
• W2051913360 created "2016-06-24" @default.
• W2051913360 creator A5021804106 @default.
• W2051913360 creator A5085247534 @default.
• W2051913360 date "2014-01-01" @default.
• W2051913360 modified "2023-10-16" @default.
• W2051913360 title "Structural Identification of Nonlinear Dynamic Biomolecular Feedback and Feedforward Loops" @default.
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• W2051913360 doi "https://doi.org/10.3182/20140824-6-za-1003.00357" @default.
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