Counting RNA and Protein Copy Numbers in Single Cells of Bacillus Subtilis
Presentation Date
7-2015
Abstract
Using novel imaging techniques, we will generate information on molecular mechanisms of repression in the central carbon metabolism of Bacilus subtillis. By absolute quantification of protein and RNA copy numbers at the single cell level, we will test previous hypotheses about two molecular mechanisms of repression(Ferguson et al. 2012). Utilizing exact models of gene expression which include transcriptional bursting and feedback(Kumar, Platini, and Kulkarni 2014), we will reconcile gene expression data with in vivo transcription factor dynamics as measured by Raster Image Correlation Spectroscopy(Digman and Gratton 2009) and improved characterization of gene expression by flow cytometry. We intend to determine RNA and Protein copy numbers from GFP promoter fusions: PcggR, PgapB and PccpN in both glycolytic and neoglugogenic conditions. This technique may be useful for quantitative analysis of other stochastic gene regulatory networks. The project described was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant #P20GM103408.
Counting RNA and Protein Copy Numbers in Single Cells of Bacillus Subtilis
Using novel imaging techniques, we will generate information on molecular mechanisms of repression in the central carbon metabolism of Bacilus subtillis. By absolute quantification of protein and RNA copy numbers at the single cell level, we will test previous hypotheses about two molecular mechanisms of repression(Ferguson et al. 2012). Utilizing exact models of gene expression which include transcriptional bursting and feedback(Kumar, Platini, and Kulkarni 2014), we will reconcile gene expression data with in vivo transcription factor dynamics as measured by Raster Image Correlation Spectroscopy(Digman and Gratton 2009) and improved characterization of gene expression by flow cytometry. We intend to determine RNA and Protein copy numbers from GFP promoter fusions: PcggR, PgapB and PccpN in both glycolytic and neoglugogenic conditions. This technique may be useful for quantitative analysis of other stochastic gene regulatory networks. The project described was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant #P20GM103408.