Nacho Molina abstract
Nacho Molina, SynthSys, School of Biological Sciences, UoE
Model-based Bayesian analysis of time series: stochastic gene expression as a case study.
Abstract: Recently, new experimental techniques allow us to monitor a wide variety of biological processes at high temporal and special resolutions: from gene expression in single-cells to enzymatic activity of single molecules. It is, therefore, becoming essential to develop general and optimal methodologies to analyze such data. I will present a Bayesian framework to analysis biological time series, which combines measurement noise models with stochastic models that describe the underlying dynamical process. This approach allows us to infer model parameters and, more importantly, to discriminate between competing models. I will show a particular application of this methodology to study the kinetics of gene expression. Recent single cell studies showed that most genes appear to be transcribed during short periods called transcriptional bursts, interspersed by silent intervals. Our analysis demonstrated that transcriptional bursting kinetics is highly gene-specific, reflecting refractory periods during which genes stay inactive for a certain time before switching on again.