UCLA

RNA Polymerase (RNAP) is a crucial protein molecule that decodes the information stored in DNA as base pair sequences, through a process called transcription. The study of the mechanics and composition of RNAP in a cell is crucial to understanding the complex and arduous process of life. Most biological systems respond to a change by adding heterogeneity to its population states. One of the most effective ways of studying a biological system is to compare the population states of proteins as a function of time or chemical environment. Unfortunately, some of the subpopulations induced by external stimulation are too small and are hidden under ensemble averaging when using traditional methods like gel-electrophoresis. I have developed single-molecule methods capable of resolving subpopulations and monitoring the transition of individual molecules to its various function states. Using these methods we can (1) rapidly screen RNAP activity in a large reagent space and (2) map the transcription factor binding sites on DNA with ultrahigh accuracy (ó < 300base pairs) in the genomic scale with the ability to differentiate single cell variations.