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Signal analysis of behavioral and molecular cycles.

Levine JD, Funes P, Dowse HB, Hall JC - BMC Neurosci (2002)

Bottom Line: We demonstrate an adaptation of digital signal analysis that allows similar treatment of both behavioral and molecular data from our studies of Drosophila.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, Brandeis University and NSF Center for Biological Timing, Waltham, MA 02454 USA. jlev@brandeis.edu

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

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Peak phase determination for a tim-luc timecourse. The fluctuating luminescence data were collected from a series of isolated, cultured fly wings in 12-h:12-h LD cycles. (a) The enzyme-reported molecular timecourse for each of 15 specimens. The appearance of synchronous waves suggests that the several heads exhibited similarly phased clock-gene-expression rhythmicities. (b) Scatter plot of mean peak phase values (per day) for each specimen. The zero-hour phase corresponds to the times of lights on, so 12 of the 15 specimens gave average peak luminescence in a 2 hour window preceding lights-on. (c) Unit-circle representation of the head-rhythm phase data. The circle defines a polar coordinate plot with radians transformed to hours. The inner dotted circle is the unit one and the dotted lines cross at the origin. The magnitude of a mean vector in this plot describes the coherence of the various phases of the head samples. The phase points taken from each specimen are plotted around the circle just beyond the unit circumference. The mean vector extends from the origin in the direction of -0.3 hours and has a magnitude that nearly reaches 1, indicating strong phase coherence.. The coefficient is a z-score and was determined to be signifiant at p < 0.01 by Rayleigh's test (see text).
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Figure 11: Peak phase determination for a tim-luc timecourse. The fluctuating luminescence data were collected from a series of isolated, cultured fly wings in 12-h:12-h LD cycles. (a) The enzyme-reported molecular timecourse for each of 15 specimens. The appearance of synchronous waves suggests that the several heads exhibited similarly phased clock-gene-expression rhythmicities. (b) Scatter plot of mean peak phase values (per day) for each specimen. The zero-hour phase corresponds to the times of lights on, so 12 of the 15 specimens gave average peak luminescence in a 2 hour window preceding lights-on. (c) Unit-circle representation of the head-rhythm phase data. The circle defines a polar coordinate plot with radians transformed to hours. The inner dotted circle is the unit one and the dotted lines cross at the origin. The magnitude of a mean vector in this plot describes the coherence of the various phases of the head samples. The phase points taken from each specimen are plotted around the circle just beyond the unit circumference. The mean vector extends from the origin in the direction of -0.3 hours and has a magnitude that nearly reaches 1, indicating strong phase coherence.. The coefficient is a z-score and was determined to be signifiant at p < 0.01 by Rayleigh's test (see text).

Mentions: The period of a rhythm does not predict its phase. For example, one might wish to determine the phase of a luciferase-reported molecular rhythm, with respect to the peak of tim-luc expression in a group of cultured tissue specimens maintained in LD12:12. As shown in Figure 11, the approach to this problem involves plotting the peak (the mean peak time within an experiment) for each individual specimen (isolated fly wings in this case) that has been examined on a unit circle using polar coordinates. A group mean vector is then determined. The direction of the vector indicates the phase of the group (by convention phase 0 corresponds to lights on), and the magnitude of this vector indicates the coherence of the group. Thus, in the extreme, if all the points were uniformly dispersed around the circle, the magnitude of the vector would be zero; whereas if they all occurred precisely in the same location, the magnitude of the vector would be 1. A statistic, Rayleigh's test, provides a z-score that makes it possible to assess whether the magnitude of the average vector is significant for the group, i.e., whether the individual phase values are clustered tightly enough to provide a significant estimate of the mean peak time [63].


Signal analysis of behavioral and molecular cycles.

Levine JD, Funes P, Dowse HB, Hall JC - BMC Neurosci (2002)

Peak phase determination for a tim-luc timecourse. The fluctuating luminescence data were collected from a series of isolated, cultured fly wings in 12-h:12-h LD cycles. (a) The enzyme-reported molecular timecourse for each of 15 specimens. The appearance of synchronous waves suggests that the several heads exhibited similarly phased clock-gene-expression rhythmicities. (b) Scatter plot of mean peak phase values (per day) for each specimen. The zero-hour phase corresponds to the times of lights on, so 12 of the 15 specimens gave average peak luminescence in a 2 hour window preceding lights-on. (c) Unit-circle representation of the head-rhythm phase data. The circle defines a polar coordinate plot with radians transformed to hours. The inner dotted circle is the unit one and the dotted lines cross at the origin. The magnitude of a mean vector in this plot describes the coherence of the various phases of the head samples. The phase points taken from each specimen are plotted around the circle just beyond the unit circumference. The mean vector extends from the origin in the direction of -0.3 hours and has a magnitude that nearly reaches 1, indicating strong phase coherence.. The coefficient is a z-score and was determined to be signifiant at p < 0.01 by Rayleigh's test (see text).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC65508&req=5

Figure 11: Peak phase determination for a tim-luc timecourse. The fluctuating luminescence data were collected from a series of isolated, cultured fly wings in 12-h:12-h LD cycles. (a) The enzyme-reported molecular timecourse for each of 15 specimens. The appearance of synchronous waves suggests that the several heads exhibited similarly phased clock-gene-expression rhythmicities. (b) Scatter plot of mean peak phase values (per day) for each specimen. The zero-hour phase corresponds to the times of lights on, so 12 of the 15 specimens gave average peak luminescence in a 2 hour window preceding lights-on. (c) Unit-circle representation of the head-rhythm phase data. The circle defines a polar coordinate plot with radians transformed to hours. The inner dotted circle is the unit one and the dotted lines cross at the origin. The magnitude of a mean vector in this plot describes the coherence of the various phases of the head samples. The phase points taken from each specimen are plotted around the circle just beyond the unit circumference. The mean vector extends from the origin in the direction of -0.3 hours and has a magnitude that nearly reaches 1, indicating strong phase coherence.. The coefficient is a z-score and was determined to be signifiant at p < 0.01 by Rayleigh's test (see text).
Mentions: The period of a rhythm does not predict its phase. For example, one might wish to determine the phase of a luciferase-reported molecular rhythm, with respect to the peak of tim-luc expression in a group of cultured tissue specimens maintained in LD12:12. As shown in Figure 11, the approach to this problem involves plotting the peak (the mean peak time within an experiment) for each individual specimen (isolated fly wings in this case) that has been examined on a unit circle using polar coordinates. A group mean vector is then determined. The direction of the vector indicates the phase of the group (by convention phase 0 corresponds to lights on), and the magnitude of this vector indicates the coherence of the group. Thus, in the extreme, if all the points were uniformly dispersed around the circle, the magnitude of the vector would be zero; whereas if they all occurred precisely in the same location, the magnitude of the vector would be 1. A statistic, Rayleigh's test, provides a z-score that makes it possible to assess whether the magnitude of the average vector is significant for the group, i.e., whether the individual phase values are clustered tightly enough to provide a significant estimate of the mean peak time [63].

Bottom Line: We demonstrate an adaptation of digital signal analysis that allows similar treatment of both behavioral and molecular data from our studies of Drosophila.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, Brandeis University and NSF Center for Biological Timing, Waltham, MA 02454 USA. jlev@brandeis.edu

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus