BF Finkenstadt, DJ Woodcock, M Komorowski, CV Harper, JRE Davis, MRH White and DA Rand
Quantifying intrinsic and extrinsic noise in gene transcription: an application to single cell imaging data
Date: March 2012
Abstract: A central challenge in computational modelling of dynamic biological systems is parameter inference from experimental time course measurements. One would like not only to measure mean parameter values but also estimate the uncertainty of single cell values and the variability from cell to cell. Here we focus on the case where single-cell uorescent protein imaging time series data is available for a population of cells. We present a two-dimensional continuous-time Bayesian hierarchical model based on van Kampen's linear noise approximation. This model has the potential to address the different sources of variability that are relevant to transcriptional and translational processes at the molecular level, namely, intrinsic noise due to the stochastic nature of the birth and deaths processes involved in chemical reactions, and extrinsic noise arising from the cell-to-cell variation of kinetic parameters associated with these processes. We also consider noise associated with the measurement process. The availability of multiple single cell data provides a unique opportunity to estimate such a model and explicitly quantify the sources of variation from experimental data.