Synthetic biology and machine learning for next generation biofuels

Propane (C3H8) is a volatile hydrocarbon with highly favourable physicochemical properties as a fuel, in addition to existing global markets and infrastructure for storage, distribution and utilization in a wide range of applications. Consequently, propane is an attractive target product in research aimed at developing new renewable alternatives to complement currently used petroleum-derived fuels. This project focuses on the construction and evaluation of alternative microbial biosynthetic pathways for the production of renewable propane. This study will expand the metabolic toolbox for renewable propane production and provides new insight and understanding for the development of next-generation biofuel platforms.

This project will focus on new biocatalytic parts for metabolic engineering. Based on our crystal structures of ADO we have already identified residue hotspots within the active channel that when mutated give rise to improved variants (i.e. faster propane synthesis). We will assemble enzyme libraries in which we increase the frequency of residue changes throughout the enzyme by constructing synthetic DNA libraries of ADO. We will use Manchester's in-house SpeedyGenes and GeneGenie methodologies, which enable high fidelity gene synthesis and efficient production of error-corrected synthetic protein libraries at residues throughout the protein for directed evolution studies. Importantly, SpeedyGenes can accommodate multiple and (statistically) controlled combinatorial variant sequences while maintaining efficient enzymatic error correction. We will couple this new approach of making synthetic libraries to (i) machine learning approaches for active learning of sequence-activity relationships, and (ii) HTP single cell screening approaches that we have/are developing at Manchester.

The project will be based in the new BBSRC/EPSRC Synthetic Biology Centre in MIB (http://synbiochem.co.uk) providing state-of-the art infrastructure and training in synthetic biology methods.