Physical Biology of Transcriptional Control in Embryonic Development

Project summary/abstract The correct implementation of developmental programs depends on information encoded in an organism’s DNA. Mutations in these regions that control genes have been shown to be responsible for ailments such as developmental defects and cancer. While great progress has been made in mapping these regulatory regions and uncovering how their target genes interact with each other, it is still not possible to precisely predict patterns of gene expression in space and time from knowledge of the DNA regulatory sequence of multicellular organisms. The overarching goal of the proposed work is to leverage knowledge about these regulatory regions and the transcription factors that bind to them in order to reach a predictive understanding of the developmental program of the early embryo of the fruit fly Drosophila melanogaster. Armed with recent innovations in (1) theoretical models that predict the mean and variability in gene expression as a function of regulatory sequence and input transcription factor concentration dynamics, and (2) technology to visualize and quantify transcriptional initiation in real time in live, single cells in a fruit fly embryo, the proposed investigations will achieve significant progress toward predictive understanding of transcriptional regulation in development. First, through cycles of experiments and modeling, the proposed studies will uncover how pioneer transcription factors dictates transcriptional onset dynamics by regulating chromatin accessibility to activators and repressors. Second, an experiment–theory discourse that leverages synthetic biology will be used to reach a predictive understanding of how the number, placement and affinity of transcription factor binding sites dictates the rate of transcriptional initiation. Finally, we will focus on single-cell transcriptional dynamics and its characteristic transcriptional bursts in order to shed light on the molecular mechanisms underlying transcription and its control. Specifically, we will use our novel compound-state Hidden Markov model to determine whether Dorsal controls burst size, frequency, amplitude, or some combination thereof, in order to generate hypotheses about the mechanisms of action of this activator and determine whether stable clusters of high Dorsal concentration that we recently discovered play an active role in regulating transcriptional dynamics. These investigations will fuel the theory–experiment dialogue necessary for reaching a predictive understanding of developmental decision-making. I envision that, by revealing the dynamic molecular mechanisms underlying transcriptional control, we will be able to write governing equations for gene regulation and, ultimately, engineer cellular decision-making programs for bioengineering and therapeutic purposes.