Developing a pipeline for rapid optimisation of transcriptional expression regulation

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A key challenge in engineering biological systems is the development of re-usable, precise and tunable gene expression control elements. Currently, developing these systems requires expensive and time-consuming experiments based on trial-and-error. This is particularly problematic for applications requiring complex systems with multiple co-regulated components or systems that use components obtained from non-model microorganisms. This project will develop tools to systematically characterise transcriptional control elements, uncovering their design rules to enable re-use of these control elements in alternative hosts and genetic contexts. Transcriptional regulation in prokaryotes is achieved primarily through the binding of transcription factors (TFs) to a promoter. Each TF has a specific binding preference, which determines its function (activator or repressor) and the location of its binding site(s) within a promoter. Knowing the biophysical binding preferences of TFs, hence, enables computational prediction of the regulatory logic and the transcriptional activity of any given promoter1,2. Currently, however, the biophysical binding preferences are known for only a handful of TFs, meaning that we cannot easily exploit heterologous TFs in bioengineering. In this interdisciplinary project, the student will utilize a range of molecular/synthetic biology, microbiology, and biophysical approaches to: (i) develop an experimental and data analysis pipeline that can determine the binding preferences of any given TF; (ii) unravel design rules of activators; (iii) use the pipeline to study the evolutionary rules governing how TFs adapt to heterologous hosts. As such the student will acquire skills that are highly relevant for future careers in the molecular biology and microbiology, biotechnology, biopharmaceutical, industrial biotechnology and related industries.Achieving these objectives will enable researchers to rapidly characterize the function (i.e. binding preference) of any desired TF, incorporate that TF into any desired regulatory network, and computationally predict promoter sequences that would contain a desired regulatory logic and result in desired transcriptional expression levels.