ABSTRACTFrom a single fertilized egg the human genome must regulate an incredible succession of cellular divisions andfate decisions to give rise to the adult human body and its ~30 trillion cells [1]. The genome must also orchestratehighly diverse functions in these terminal cell types and in many instances allow for dynamic responses to avariety of stimuli - from white blood cells responding to stimulation [2] to hepatocytes responding to hormonalcues [3]. Furthermore developmental processes are asynchronous and continue for many cell types intoadulthood. 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 [4] single-cell genomic technologies are providingunprecedented views into the temporal dynamics of cellular differentiation and response at a genomic scale [5].This will have widespread implications for our strategies of stem cell therapy windows of intervention in diseaseprogression and our basic understanding of developmental biology. However these inferences are to-datelimited and rely on a concept called pseudotime [5] which is difficult to validate and can be warped relative toreal time. To truly understand how the genome coordinates development differentiation and disease we neednew tools that allow us to better measure several key features of developmental trajectories: the orderingof regulatory cascades the duration of the key genomic events in developmental processes and thespecific DNA sequences that can regulate temporal expression patterns. In order to address theseconcerns we will develop a new suite of tools that leverage single-cell readouts to better understand thegenomic regulation of time. In particular we will focus on highly multiplexed assays to better understand thenecessary and sufficient ordering of regulatory cascades in differentiation pathways assays to convertpseudotime to real time and genome scalable assays to identify and validate the exact regulatorysequences that define temporal patterns of gene expression.