Two major societal challenges exist today i) how can we consumable more sustainably and ii) how can we minimise environmental contamination. As modern society is hugely dependent on finite oil reserves for the supply of fuels and chemicals, moving our dependence away from these unsustainable oil-based feedstocks to renewable ones is therefore a critical factor towards the development of a low carbon bioeconomy. Lignin derived from biomass feedstocks offers great potential as a renewable source of aromatic compounds if methods for its effective valorization can be developed. In parallel environmental contamination with non-biodegradable material such as plastic, are both a negative human impact that needs to be resolved but in itself offers an alternative source of carbon, for growth of engineered microbes, if the stored metabolic energy can be released.
Synthetic biology and metabolic engineering offer the potential to synergistically enable the development of cell factories with novel biosynthetic routes to valuable chemicals from both of these biomass and plastic sources. Pathway design and optimization is however a major bottleneck due to the lack of high-throughput methods capable of screening large libraries of genetic variants and the metabolic burden associated with bioproduction. Genetically encoded biosensors can provide a solution by transducing the target metabolite concentration into detectable signals to provide high-throughput phenotypic read-outs and allow dynamic pathway regulation. The development and application of biosensors in the discovery and engineering of efficient biocatalytic processes for the degradation, conversion and valorization of lignin is paving the way towards a sustainable and economically viable biorefinery.
The PhD student based in Manchester Institute of Biotechnology will be co-supervised by Dr. Neil Dixon, and Prof Nigel Scrutton, and Dr Antony Green, and will be focused on the development of whole cell biocatalysts for biomass and plastic degradation using genetically encoded biosensors. Specifically the programme will aim to discover, optimise and apply microbial transporter, responsible for the import and export of substrates and products from whole biocatalysts.
The student will be trained in broad aspects of biotechnology, microbial gene expression regulation, use of synthetic biology tools and principles, biocatalysis, directed evolution, microbial fermentation, molecular biology and bio-analytical methods such GC-MS. This project would suit individuals interested in future careers in biotechnology, biocatalysis and bioprocessing. http://www.manchester.ac.uk/research/neil.dixon/
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
1) Machado, L., Currin, A. & Dixon, N. Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes. bioRxiv. 689232 (2019). doi:10.1101/689232
2) Moraes EC, Alvarez TM, Persinoti GF, Tomazetto G, Brenelli LB, Paixão DAA, Ematsu GC, Aricetti JA, Caldana C, Dixon N, Bugg TDH, Squina FM Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization Biotechnol Biofuels (2018) doi: 10.1186/s13068-018-1073-4
3) Leopoldo F. M. Machado, Neil Dixon Development and substrate specificity screening of an in vivo biosensor for the detection of biomass derived aromatic chemical building blocks Chemical Communications (2016), DOI: 10.1039/C6CC04559F
4) Burke, AJ, Lovelock, SL, Frese, A., Crawshaw, R., Ortmayer, M., Dunstan, M. Levy, C., Green, AP Design and evolution of an enzyme with a non-canonical organocatalytic mechanism. Nature 2019, 570, 219.
5) Jervis, A.J., Carbonell, P., Vinaxia, M., Dunstan, M., Hollywood, K., Robinson, C. J., Rattray, N., Yan, C., Swainston, N., Currin, A., Sung, H.R., Toogood, H. S., Taylor, S., Faulon, J-L., Breitling, R., Takano, E. & Scrutton, N. S. Machine learning of designed translational control allows predictive pathway optimisation in Escherichia coli. ACS Syn Bio 2019, 8, 127