Microfluidics for single-cell noise measurements

微流体和时间推移显微镜的结合是一个强大的组合，有可能产生时间序列数据，用于整个细胞种群的单细胞测量。该项目旨在利用这些工具开发技术和战略，以促进在单细胞水平上研究随机事件。这项工作的一个关键焦点是微流控和图像分析技术的发展。该项目中的这些发展正被应用于研究酵母GCN4转录调控基因表达中的噪音的具体例子。这是在氨基酸饥饿时通过一种新的转录后调控系统激活的。到目前为止，大多数关于基因表达噪声的研究都集中在转录噪声上，因此转录后调控对噪声的影响相对较少。生物系统在宏观水平上表现出许多明显的确定性行为，但其潜在的机制往往是随机的。基因表达中的噪声在克隆细胞群体中产生异质性，是许多细胞过程(如昼夜节律)的重要因素。即使在众所周知的“抑制物”合成基因电路中，噪声对电路输出也有很大影响。因此，对基因表达的随机性的认识对于完全理解生物学是至关重要的，对于未来合成生物系统的成功设计也是必不可少的。因此，该项目属于EPSRC合成生物学的研究领域。对活的有机体进行工程改造总是会产生不完全相同的细胞群体，任何对生物系统进行工程的尝试都必须考虑到这种异质性。通过利用微流控技术研究酿酒酵母(Saccharmyces Cerevisiae)中自然存在的和经工程改造的细胞间异质性，本项目旨在提供对这种真核生物中基因表达随机性的定量理解

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