title: Estimation of kinetic parameters of transcription from temporal single-RNA measurements.
abstract: Gene expression dynamics in prokaryotes is largely controlled by the multi-step process of transcription initiation whose kinetics is subject to regulation. Since the number and duration of these steps cannot be currently measured in vivo, we propose a novel method for estimating them from time series of RNA numbers in individual cells. We demonstrate the method's applicability on measurements of fluorescence-tagged RNA molecules in Escherichia coli cells, and compare with a previous method. We show that the results of the two methods agree for equal data. We also show that, when incorporating additional data, the new method produces significantly different estimates, which are in closer agreement with qPCR measurements. Unlike the previous method, the new method requires no preprocessing of the RNA numbers, using maximal information from the RNA time series. In addition, it can use data outside of the observed RNA productions. Overall, the new method characterizes the transcription initiation process with enhanced detail.
authors.affiliation: BIOMS, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. Electronic address: christoph.zimmer@bioquant.uni-heidelberg.de.
authors.forename: Christoph
authors.initials: C
authors.lastname: Zimmer
authors.affiliation: Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, 33720 Tampere, Finland.
authors.forename: Antti
authors.initials: A
authors.lastname: Häkkinen
authors.affiliation: Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Korkeakoulunkatu 1, 33720 Tampere, Finland.
authors.forename: Andre S
authors.initials: AS
authors.lastname: Ribeiro
mesh_terms: D004926:Escherichia coli / Q000235:genetics*; D007700:Kinetics; D008957:Models, Genetic; D012329:RNA, Bacterial / Q000235:genetics*
chemical_list: D012329:RNA, Bacterial
keywords: Parameter estimation; Single-RNA measurements; Stochastic modeling; Transcription dynamics