Using a Single Fluorescent Reporter Gene to Infer Half-Life of Extrinsic Noise and Other Parameters of Gene Expression

Abstract

Fluorescent and luminescent proteins are often used as reporters of transcriptional activity. Given the prevalence of noise in biochemical systems, the time-series data arising from these is of significant interest in efforts to calibrate stochastic models of gene expression and obtain information about sources of nongenetic variability. We present a statistical inference framework that can be used to estimate kinetic parameters of gene expression, as well as the strength and half-life of extrinsic noise from single fluorescent-reporter-gene time-series data. The method takes into account stochastic variability in a fluorescent signal resulting from intrinsic noise of gene expression, kinetics of fluorescent protein maturation, and extrinsic noise, which is assumed to arise at transcriptional level. We use the linear noise approximation and derive an explicit formula for the likelihood of observed fluorescent data. The method is embedded in a Bayesian paradigm, so that certain parameters can be informed from other experiments allowing portability of results across different studies. Inference is performed using Markov chain Monte Carlo. Fluorescent reporters are primary tools to observe dynamics of gene expression and the correct interpretation of fluorescent data is crucial to investigating these fundamental processes of cellular life. As both magnitude and frequency of the noise may have a dramatic effect on the cell fitness, the quantification of stochastic fluctuation is essential to the understanding of how genes are regulated. Our method provides a framework that addresses this important question.

Bayesian inference of reaction kinetics from single-cell recordings across a heterogeneous cell population

“…In [73], a method based on the LNA is presented that allows the inclusion of time-varying heterogeneity of the transcription rate in a gene expression model. Here an Ornstein-Uhlenbeck process is assumed to be the source of extrinsic noise in the transcription rate, which when combined with the LNA results in Gaussian transition probabilities for the state sequence at measurement time points, which makes inference tractable.…”

Overview of Micro- and Nano-Technology Tools for Stem Cell Applications: Micropatterned and Microelectronic Devices

“…The advent of the Green Fluorescent Protein (GFP) allowed the development of tools for live imaging of cells [15]. It was used to estimate rates of gene expression [16], detect specific cells in vivo [17] or as biomarker or biosensor [18]. Even if the discovery of GFP can be considered as a breakthrough for the development of live-cell imaging, quantifications of time-lapse images still need corrections for the auto-fluorescence of culture medium, and methods to track objects and cell movement to identify individual cells without using endpoint nuclear staining or similar.…”

Stochastic reaction networks with input processes: Analysis and application to gene expression inference

“…In the stochastic setup, the problem is nontrivial, and has been addressed only indirectly. Most approaches are based on parameter estimation or model selection [29,28,20,36]. Here we take a nonparametric approach, that is, we propose a method to reconstruct statistics of an arbitrary input process in absence of a parametric model governing its laws.…”