From a single fertilized egg, the human genome must regulate an incredible succession of cellular divisions and fate decisions to give rise to the adult human body and its ~30 trillion cells . The genome must also orchestrate highly diverse functions in these terminal cell types and in many instances allow for dynamic responses to a variety of stimuli - from white blood cells responding to stimulation  to hepatocytes responding to hormonal cues . Furthermore, developmental processes are asynchronous and continue for many cell types into adulthood. Fundamental to our understanding of the causal links in all of these processes is the concept of time. While time course studies have a long history in genomics , single-cell genomic technologies are providing unprecedented views into the temporal dynamics of cellular differentiation and response at a genomic scale . This will have widespread implications for our strategies of stem cell therapy, windows of intervention in disease progression, and our basic understanding of developmental biology. However, these inferences are to-date limited and rely on a concept called ‘pseudotime’ , which is difficult to validate and can be warped relative to real time. To truly understand how the genome coordinates development, differentiation, and disease we need new tools that allow us to better measure several key features of developmental trajectories: the ordering of regulatory cascades, the duration of the key genomic events in developmental processes, and the specific DNA sequences that can regulate temporal expression patterns. In order to address these concerns, we will develop a new suite of tools that leverage single-cell readouts to better understand the genomic regulation of time. In particular, we will focus on highly multiplexed assays to better understand the necessary and sufficient ordering of regulatory cascades in differentiation pathways, assays to convert pseudotime to real time, and genome scalable assays to identify and validate the exact regulatory sequences that define temporal patterns of gene expression.