The mammalian suprachiasmatic nucleus (SCN), required for daily cycles in behavior and physiology. How the cells of the SCN synchronize to coordinate behavior is largely unknown. We have established a collaborative program combining experimental and computational methods to study large numbers of circadian oscillators, their connections, and the real-time kinetics by which they self-synchronize and respond to perturbations in environmental timing cues. To understand circadian regulation within the brain, we must understand the topology and types of interactions between circadian neurons.
Aim 1 will monitor the network of SCN oscillators as they synchronize during fetal development, during entrainment, following a phase shift, and after restoration of cell-cell communication in the adult SCN. Using novel wavelet-based time series analyses, we will estimate the strength and direction of individual connections in the SCN.
Aim 2 will use graph theory and spatial statistics to quantify network features of the developing and adult SCN. These analyses will define the mechanisms of synchronization during normal development and following environmental perturbations and the relative contributions of local, regional or global coupling which contribute to period precision.
Aim 3 will compare the performance of the SCN networks under the four conditions with both deterministic and stochastic model networks. The computational models will investigate the effects of intrinsic noise and cell-cell heterogeneity on circadian synchronization. Revealing how circadian oscillators interact to generate a coherent rhythmic output will have important clinical implications for prevention and treatment of circadian rhythm disruptions, including mood and sleep disorders.
Daily rhythms in behavior, physiology and cognitive performance are driven by circadian clocks in the brain. This project examines the role of network connections and noise in the synchronization of circadian oscillators during normal development and following environmental perturbations using novel modeling, statistical and network analysis tools.
|Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel et al. (2016) Functional network inference of the suprachiasmatic nucleus. Proc Natl Acad Sci U S A 113:4512-7|
|Kingsbury, Nathaniel J; Taylor, Stephanie R; Henson, Michael A (2016) Inhibitory and excitatory networks balance cell coupling in the suprachiasmatic nucleus: A modeling approach. J Theor Biol 397:135-44|
|St John, Peter C; Doyle 3rd, Francis J (2015) Quantifying Stochastic Noise in Cultured Circadian Reporter Cells. PLoS Comput Biol 11:e1004451|
|Herzog, Erik D; Kiss, IstvÃ¡n Z; Mazuski, Cristina (2015) Measuring synchrony in the mammalian central circadian circuit. Methods Enzymol 552:3-22|
|Henson, Michael A (2015) Understanding environmental adaptation of the fungal circadian clock with mathematical modeling. Biophys J 108:1580-2|
|Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R (2015) On the Inference of Functional Circadian Networks Using Granger Causality. PLoS One 10:e0137540|
|Wang, Yongqiang; Hori, Yutaka; Hara, Shinji et al. (2014) Intercellular delay regulates the collective period of repressively coupled gene regulatory oscillator networks. IEEE Trans Automat Contr 59:211-216|
|Taylor, Stephanie R (2014) How to get oscillators in a multicellular clock to agree on the right period. Biophys J 106:1839-40|
|St John, Peter C; Hirota, Tsuyoshi; Kay, Steve A et al. (2014) Spatiotemporal separation of PER and CRY posttranslational regulation in the mammalian circadian clock. Proc Natl Acad Sci U S A 111:2040-5|
|Taylor, Stephanie R; Cheever, Allyson; Harmon, Sarah M (2014) Velocity response curves demonstrate the complexity of modeling entrainable clocks. J Theor Biol 363:307-17|
Showing the most recent 10 out of 21 publications