Analysis of Complex Circadian Time Series Data Using Wavelets

A new phase model of the spatiotemporal relationships between three circadian oscillators in the brainstem

Analysis of ex vivo Per2 bioluminescent rhythm previously recorded in the mouse dorsal vagal complex reveals a characteristic phase relationship between three distinct circadian oscillators. These signals represent core clock gene expression in the area postrema (AP), the nucleus of the solitary tract (NTS) and the ependymal cells surrounding the 4th ventricle (4Vep). Initially, the data suggests a consistent phasing in which the AP peaks first, followed shortly by the NTS, with the 4Vep peaking 8-9 h later. Wavelet analysis reveals that this pattern is not consistently maintained throughout a recording, however, the phase dynamics strongly imply that oscillator interactions are present. A simple phase model of the three oscillators is developed and it suggests that realistic phase dynamics occur between three model oscillators with coupling close to a synchronisation transition. The coupling topology suggests that the AP bidirectionally communicates phase information to the NTS and the 4Vep to synchronise the three structures. A comparison of the model with previous experimental manipulations demonstrates its feasibility to explain DVC circadian phasing. Finally, we show that simulating steadily decaying coupling improves the model's ability to capture experimental phase dynamics.

Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability

From biological to socio-technical systems, rhythmic processes are pervasive in our environment. However, methods for their comprehensive analysis are prevalent only in specific fields that limit the transfer of knowledge across scientific disciplines. This hinders interdisciplinary research and integrative analyses of rhythms across different domains and datasets. In this paper, we review recent developments in cross-disciplinary rhythmicity research, with a focus on the importance of rhythmic analyses in urban planning and biomedical research. Furthermore, we describe the current state of the art of (integrative) computational methods for the investigation of rhythmic data. Finally, we discuss the further potential and propose necessary future developments for cross-disciplinary rhythmicity analysis to foster integration of heterogeneous datasets across different domains, as well as guide data-driven decision making beyond the boundaries of traditional intradisciplinary research, especially in the context of sustainable and healthy cities.