Precise gene regulation is essential for a wide variety of transient, developmental, and homeostatic processes. The majority of gene regulation is mediated by cis-regulatory elements, both distal (enhancers), and proximal (promoters & enhancers). Developments in biochemical assays, gene editing techniques, and sequencing technology have enabled genome-wide profiling of regulatory elements over a wide variety of \textit{in vivo} conditions. In this tripartite work, I present separate statistical frameworks for analyzing how these repertoires of regulatory elements work at both physiological, and evolutionary timescales. The first part describes the use of PRO-seq to characterize rapid changes in the transcriptional landscape of human cells to celastrol, a compound that has potent anti-inflammatory, tumor-inhibitory, and obesity-controlling effects. By exploiting the ability of PRO-seq to detect nascent RNAs, I characterize the transcriptional response at both genes and enhancers, and leverage statistical models to detect transcription factors that orchestrate it. I implicate several transcription factors in early transcriptional changes, including members of the E2F and RFX families. PRO-seq also allows us to detect an increase in transcription start site proximal pausing, suggesting that pause release may be a mechanism for inhibiting gene expression during the celastrol response. This work demonstrates that a thorough analysis of PRO-seq time-course data can provide novel insight into multiple aspects of a complex transcriptional response.The second part develops a statistical model for determining whether constituent enhancers of a super-enhancer'' exhibit synergy and thus address the question Is a super-enhancer greater than the sum of its parts?'' In this work I reconcile two works with seemingly opposing theses by finding that we cannot confidently reject synergy-free models for super-enhancers. Furthermore, I demonstrate that thoughtful consideration of null models for synergy in gene regulation is critical for furthering our understanding of ensembles of regulatory elements.In the final section, I develop evolutionary models for cis-regulatory function as quantified by genome-wide biochemical assays. I apply a noise-aware phylogenetic model to analyze the evolution of H3K27Ac and H3K4me3 histone marks as proxies of enhancer and promoter function. I estimate relative turnover rates for a variety of functional element categories and show that gene expression and sequence constraint correlate with turnover rate. I also propose that dosage sensitivity of target genes can explain the discrepancy between sequence and histone mark turnover rates of associated CREs.This work illustrates the important role statistical models play in understanding gene regulation at all levels and suggests a potential path towards unified models of gene regulation and evolution.