Microfluidics for single-cell noise measurements

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The combination of microfluidics and time-lapse microscopy is a powerful one, with the potential for the generation of time-series data for single-cell measurements across a whole population of cells. This project aims to employ these tools for the development of technologies and strategies that facilitate the study of stochastic events at the single-cell level. A key focus of the work is the development of microfluidic and image analysis techniques.Such developments within this project are being applied to study the specific example of noise within the expression of the yeast GCN4 transcriptional regulator. This is activated during amino acid starvation, through a novel post-transcriptional regulatory system. The majority of research into gene expression noise thus far has focused on transcriptional noise, so the impact of post-transcriptional regulation on noise is relatively poorly understood.Biological systems show many apparently deterministic behaviours at the macroscopic level, but their underlying mechanisms are often stochastic in nature. Noise in gene expression generates heterogeneity across clonal cell populations, and is an important factor in many cellular processes (e.g. circadian rhythms). Even within the well-known "repressilator" synthetic gene circuit, noise had a significant impact on the circuit output. An appreciation of stochasticity in gene expression is therefore crucial for a complete understanding of biology, and will also be essential for the successful design of future synthetic biological systems. Consequently, this project falls within the EPSRC research area of synthetic biology. Engineering of living organisms will always create populations of cells that are not all identical, and any attempt to engineer biological systems must take this heterogeneity into account. By utilising microfluidics to study both naturally occurring and engineered cell-to-cell heterogeneity in baker's yeast (Saccharomyces cerevisiae), this project aims to provide a quantitative understanding of gene expression stochasticity in this eukaryotic organism