Our ability to regulate the transmission of information from genotype to phenotype underpins the field of synthetic biology. The inducible production of heterologous protein is a powerful tool for the construction of a variety of applications, including biosensing, bioprocessing and foundational research. However, the tools currently available are often limited due to challenges with high levels of basal protein expression, context dependencies, poor orthogonality and limited dynamic range. This study investigates methods for improving the performance of inducible protein production tools that regulate transcription or translation initiation. Through the application of directed evolution, rational design and statistical modelling methods, including Partial Least Squares and Design of Experiments, the modularity and performance of several gene expression tools were significantly improved. The tools engineered in this body of work include cis-repressed translation activating riboswitches and a protocatechuic acid inducible, allosteric transcription factor based biosensor. We present methods and approaches to understand context dependence, isolate riboswitch insulators and improve the maximal expression and dynamic range of these regulatory tools. These methods facilitate the robust and efficient discovery of device topologies with enhanced function and robustness. By using statistical, rather than mechanistic, modelling we are able to achieve significant gain of function in the absence of thorough part characterisation or complex structural analysis; demonstrating the power of objective reasoning for the optimisation of complex, multidimensional biological systems.
|Date of Award||1 Aug 2020|
- The University of Manchester
|Supervisor||Philip Day (Supervisor), Sam Hay (Supervisor) & Neil Dixon (Supervisor)|