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Open Access Highly Accessed Methodology article

Signal analysis of behavioral and molecular cycles

Joel D Levine1, Pablo Funes1, Harold B Dowse23 and Jeffrey C Hall1*

Author Affiliations

1 Department of Biology, Brandeis University and NSF Center for Biological Timing, Waltham, MA, 02454 USA

2 Department of Biological Sciences, University of Maine, Orono, ME 04469 USA

3 Department of Mathematics and Statistics, University of Maine, Orono, ME 04469 USA

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BMC Neuroscience 2002, 3:1  doi:10.1186/1471-2202-3-1

Published: 18 January 2002



Circadian clocks are biological oscillators that regulate molecular, physiological, and behavioral rhythms in a wide variety of organisms. While behavioral rhythms are typically monitored over many cycles, a similar approach to molecular rhythms was not possible until recently; the advent of real-time analysis using transgenic reporters now permits the observations of molecular rhythms over many cycles as well. This development suggests that new details about the relationship between molecular and behavioral rhythms may be revealed. Even so, behavioral and molecular rhythmicity have been analyzed using different methods, making such comparisons difficult to achieve. To address this shortcoming, among others, we developed a set of integrated analytical tools to unify the analysis of biological rhythms across modalities.


We demonstrate an adaptation of digital signal analysis that allows similar treatment of both behavioral and molecular data from our studies of Drosophila. For both types of data, we apply digital filters to extract and clarify details of interest; we employ methods of autocorrelation and spectral analysis to assess rhythmicity and estimate the period; we evaluate phase shifts using crosscorrelation; and we use circular statistics to extract information about phase.


Using data generated by our investigation of rhythms in Drosophila we demonstrate how a unique aggregation of analytical tools may be used to analyze and compare behavioral and molecular rhythms. These methods are shown to be versatile and will also be adaptable to further experiments, owing in part to the non-proprietary nature of the code we have developed.