Deciphering the Role of Global Transcriptional Noise During Rapid Adaptation to Reactive Oxygen Species.

Gene expression is inherently stochastic due to low copy numbers of genes and mRNAs and to extrinsic fluctuations. Genes that are involved in stress response are particularly noisy and the resulting population heterogeneity is thought to help cells to cope in uncertain environments. Although dynamic gene expression responses in cell populations have been widely studied, we know very little about the cell-to-cell variability in transcriptional programs launched by individual cells in response to environmental challenges, and even less about how this response depends on cell phenotypes such as size or cell-cycle stages.

Oxidative stress resulting from an excess of reactive oxygen species damages components of the cell and is implicated in human diseases such as Alzheimer and cancer. In free living organisms, survival after acute exposure to oxidative agents is ensured by a complex and coordinated gene expression program regulated at the transcriptional and post-transcriptional levels. The fission yeast Schizosaccharomyces pombe is a popular model to study the cellular responses to oxidative stress with many of the pathways involved conserved in multicellular organisms.

In order to understand better the interplay between environmental stress, signalling pathway regulators and stochasticity in gene expression, we offer an interdisciplinary PhD studentship shared between the groups of Dr Samuel Marguerat (Quantitative Gene Expression) at the MRC Clinical Sciences Centre and of Dr Vahid Shahrazaei (Biomathematics) at the department of Mathematics, both affiliated to Imperial College London. If awarded the studentship, you will analyse the dynamic response of fission yeast cells to oxidative stress and exploit the quantitative and single-cell genomics techniques being developed in the Quantitative Gene Expression group to generate measurements of 1) cellular phenotypes such as cell-size and cell-cycle stages, 2) mRNA levels in cell populations, and 3) transcriptional cell-to-cell variability in single cells. You will then use these datasets to develop, in the Shahrezaei group, stochastic mathematical models of oxidative stress signalling. This integrated experimental and computational project will shed light on how stochasticity and global noise in gene expression are affected as a function of cell phenotypes during a dynamic response.

Candidates from both biological and physical sciences would be considered but a clear motivation to develop the skills necessary to pursue both the theoretical and experimental aspects of the project is desired. Programming or computational experience would be an asset.

This project will involve techniques such as: yeast genetics and cell biology, RNA-seq, single-cell genomics, microscopy, stochastic modelling of biochemical networks and bioinformatics.