Studying stochasticity in eukaryotic gene expression using novel tools of synthetic biology modelling and analytical science

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The information contained in the genes of living cells has to be converted into cellular components that form structures and enable biochemical reactions to take place. This process is called gene expression and it is vital to all known life. Gene expression comprises two main synthetic steps, called transcription and translation. In transcription, the information in the DNA sequences of the genes is converted into equivalent sequences in so-called messenger RNA (mRNA) molecules. In translation, the mRNA molecules are 'read' by a large molecular structure called the ribosome, which uses the information to dictate the synthesis of proteins. The proteins synthesised in this way have to fold into a number of types of three-dimensional structure in order to become active. However, processes like gene expression and signalling in biology are not entirely accurate at the molecular level. Indeed, we now realise that one of the wonders of cellular systems is how they cope with the fluctuations in the functioning of their molecular processes. Indeed, these fluctuations actually play important roles in regulation, stress responses and other processes in biology. Our project will study how the fluctuations that occur at different steps in gene expression affect the performance of the system. To do this, we will use a gene expression pathway that we have partly built ourselves using synthetic biology techniques. This pathway has been made to interact in only a very limited way with other cellular processes so that when we manipulate it, this has no adverse effects on the cells and the responses are limited to the pathway itself. We will examine how each of the steps of gene expression outlined above contribute to the fluctuations in the gene expression pathway, and also how these respective contributions add up overall. We will do this using novel analytical methods and imaging. Microfluidics will be used to separate individual cells so that we can study changes in gene expression in each cell over time. In parallel to the experimental work, we will use computational modelling to help us understand how the system works and to help us design experiments (to test our models). Finally, we will investigate whether the fluctuations in the functioning of the system can be suppressed using synthetic genetic circuits that are known to be commonly used in natural biological systems. In this way, we will learn how noise at the molecular level can be prevented from becoming damaging to the cell. The theoretical and experimental technologies developed and applied in this project will be of value to a wide spectrum of researchers in academia and industry.