A model-based Bayesian tool to analyse stochastic time series: NF-kB transcriptional regulation in single mammalian cells as a case study

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 in a quantitative manner. The main goal of this PhD project will be to develop a general Bayesian framework to analysis biological time series based on a previous method established in the lab (Suter et al. 2011, Molina et al. 2014). This method integrates measurement noise models with stochastic models that describe the underlying dynamical process. The resulting algorithms will be able to: i) infer model parameters; ii) estimate hidden unobserved variables; and iii) discriminate between competing models. Ultimately, a user-friendly software package will be delivered, which potentially could be used by a broad non-expert community.

We will apply the method to analyze data generated by our collaborators Dr. Samuel Zambrano and Dr. Davide Mazza at the San Raffaele Scientific Institute. These researchers have developed an experimental set up that allows them to monitor at the single cell level the translocation dynamics of NF-kB, a key transcription factor of the regulation of the immune response (Zambrano et al. 2014). Moreover, using a NanoLuc® reporter they are able to record transcription in vivo driven by NF-kB. Thus, for a given gene, we are able to measure at the same time the regulatory input and the transcriptional output. The experimental data plus the computational method will allow us to study the role of NF-kB dynamics in transcriptional bursting.

The ideal candidate should have a strong mathematical background and excellent programming skills in C/C++ and Matlab/Python. Knowledge of Bayesian statistics will be desirable.

The University of Edinburgh and the SynthSys Centre offer a unique dynamic and multidisciplinary environment ideal to develop a research career.