Quantifying in Vitro Transcription

Abstract

Gene regulation is of major interest in molecular biology as it the first step in a complex multi-step process which eventually leads to gene expression. Understanding gene regulation means being able to better understand how certain genes come to expression and what exactly affects gene expression. In biological cells, genes code for proteins. The production of proteins is regulated by a complex regulatory network involving regulatory proteins (transcription factors). Each gene competes for transcription factors with a complex array of binding sites on the genome. Genes often exist in multiple identical copies, for example in the process of chromosomal replication during the cell cycle. Predicting the quantitative effect of this competition on the regulation of gene expression is of great interest. In this Project we use a model to quantitatively predict the transcription rate. In this model the grand canonical ensemble is used to construct a statistical mechanical probability that a transcription factor is bound to its specific site. To measure transcription we make use of a sequence called Broccoli. Broccoli allows us to measure transcription in vitro quantitatively in real time. The model is then adapted to the experimental system and compared to the experimental measurements. RNA polymerase and the amount of DNA were varied and the corresponding change in transcription rate was observed. The experimental results were compared with the grand canonical model with which we calculated the occupation of the promoter sites. The model can be adapted to account for unexpected competition by plastic surfaces by adding a competitive term. This competition can be prevented by adding BSA to the system, simplifying the system. With this method and model we have a strong foundation to study transcription quantitatively. This will allow for systematic studies of gene regulation.