Simons Foundation Lectures are free public colloquia related to basic science and mathematics. These high-level talks are intended for professors, students, postdocs and business professionals, but interested people from the metropolitan area are welcome as well.
Transcriptional networks operate dynamically in vivo, but capturing and modeling these dynamics is an experimental and computational challenge. This presentation focuses on time — building predictive network models based on time-series transcriptome data, and perturbing transcription networks in time. The outcome is a dynamic hit-and-run transcription model with relevance across eukaryotes.
In this lecture, Dr. Gloria Coruzzi will probe dynamic transcription networks, computationally and experimentally. Using a machine-learning approach called Dynamic Factor Graph, fine-scale time-series transcriptome data is used to infer network models that were validated both in silico using left-out data, and experimentally. To explore the molecular basis for underlying dynamic transcription, a cell-based assay was developed to follow the mode of action of a transcription factor (TF) within one minute of nuclear entry. This uncovered genome-wide support for a hit-and-run mechanism of transcription, in which de novo transcription initiated by a transient TF “hit” persists after the TF has “run.”