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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-IFCN-L (02))
Program Officer
Tompkins, Laurie
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Washington University
Schools of Arts and Sciences
Saint Louis
United States
Zip Code
Herzog, Erik D; Hermanstyne, Tracey; Smyllie, Nicola J et al. (2017) Regulating the Suprachiasmatic Nucleus (SCN) Circadian Clockwork: Interplay between Cell-Autonomous and Circuit-Level Mechanisms. Cold Spring Harb Perspect Biol 9:
Taylor, Stephanie R; Wang, Thomas J; Granados-Fuentes, Daniel et al. (2017) Resynchronization Dynamics Reveal that the Ventral Entrains the Dorsal Suprachiasmatic Nucleus. J Biol Rhythms 32:35-47
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
Abel, John H; Doyle 3rd, Francis J (2016) A systems theoretic approach to analysis and control of mammalian circadian dynamics. Chem Eng Res Des 116:48-60
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
Abel, John H; Drawert, Brian; Hellander, Andreas et al. (2016) GillesPy: A Python Package for Stochastic Model Building and Simulation. IEEE Life Sci Lett 2:35-38
Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R (2015) On the Inference of Functional Circadian Networks Using Granger Causality. PLoS One 10:e0137540
Mazuski, Cristina; Herzog, Erik D (2015) Circadian rhythms: to sync or not to sync. Curr Biol 25:R337-9
Su, Jing; Henson, Michael A (2015) Circadian Gating of the Mammalian Cell Cycle Restriction Point: A Mathematical Analysis. IEEE Life Sci Lett 1:11-14
Henson, Michael A (2015) Understanding environmental adaptation of the fungal circadian clock with mathematical modeling. Biophys J 108:1580-1582

Showing the most recent 10 out of 27 publications