PROJECT SUMMARY/ABSTRACT Circadian rhythms are near 24-hour cycles in physiology that are fundamental to human health. Diseases such as breast cancer and other metabolic disorders are associated with circadian rhythm disruptions because of shiftwork, 24-hour lighting, chronic jet lag, and other factors of modern-day life. Circadian rhythms are created by endogenous molecular oscillators, or clocks, that regulate transcriptional rhythms. Communication between these clocks is integral to their function as a well-coordinated system. Despite their importance, circadian inter- tissue communication mechanisms are poorly understood and properties that govern circadian coordination are unknown. Yet, studies suggest that circulating endocrine factors, such as insulin and glucocorticoids, may play a role in synchronization as they can phase shift core genes of the clock machinery. Thus, the hypothesis is that endocrine pathways are sources of circadian inter-tissue communication and are affected by circadian misalignment and disruption. Defining factors of inter-tissue communication between clocks and characterizing temporal interactions in genome-scale data is critical to explain how tissues and organs orchestrate critical daily programs such as metabolism. To investigate circadian function and gene regulation, studies use complex time-series designs to manipulate clock genes and environmental conditions across one or multiple tissues. Yet, statistical and analytical methods do not reliably quantify rhythmic effects (amplitude and phase) nor interaction effects with conditions tested in such large-scale data. Yet, rhythmic properties such as amplitude and phase can fully describe a gene’s rhythm and change to that rhythm (differential rhythmicity) as a result of perturbation in complex experiments. The goal of this project is to identify factors of inter-tissue communication by addressing these statistical challenges to circadian gene expression analysis. I will build a statistical framework to estimate effects for amplitude and phase of genes in wild type experiments, as well as changes in amplitude and phase in experiments with two or more conditions (differential rhythmicity). Properly quantifying these properties will provide a detailed characterization of rhythmic features in genome-scale data. With these properties, I will leverage publicly available circadian transcriptome data to analyze phase similarities in signaling pathways between tissues and construct a map of potential inter-tissue signaling interactions across the day. This work will identify components that define relationships between tissue clocks at the gene expression level. These factors can be targeted as therapeutic agents for conditions caused by circadian disruption and dysregulation in our everyday life.