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A combinatorial assembly strategy to optimise biosynthetic pathway performance in yeast - a synthetic biology

(This project has been filled - no further applications will be accepted)

Project supervisors: Prof John McCarthy, Warwick Integrative Synthetic Biology centre (WISB), School of Life Sciences

Non-Academic partner: Dr Franck Escalettes, Ingenza

Project title: A combinatorial assembly strategy to optimise biosynthetic pathway performance in yeast - a synthetic biology project supported by an academic-industrial consortium 

Project outline:

This is a project for a young scientist who wishes to pursue a project that utilizes state-of-the-art technologies from synthetic biology to address an exciting challenge relevant to biotechnology and bioenergy. Microbial biosynthetic pathways are an important source of high value products, including industrial precursors, agrochemicals, drugs, food additives, biofuels and biopharmaceuticals, and can also be applied in situ for bioremediation, biofiltration, biomass production and other processes. However, a considerable amount of time and effort generally have to be invested in ‘pathway development’ in order to optimise productivity. Since the productivity of any given biosynthetic pathway is influenced by genetic factors, host physiology, metabolic dynamics and rate control characteristics, the optimization of biosynthetic pathways by traditional means can be a hit-and-miss process that wastes time and resources.

You will be able to utilize cutting-edge experimental and computational tools provided by the latest technologies developed in synthetic biology and systems biology to provide a more efficient approach to biosynthetic pathway optimization. Here, we focus on Saccharomyces cerevisiae, which is a GRAS (generally regarded as safe) organism with a proven biotechnological pedigree, but our strategy could equally be applied to other microbes. The strategy that we will follow in this project has only very recently become feasible by virtue of rapid progress in experimental and theoretical methods in the fields of synthetic and systems biology and also due to the rapidly decreasing cost of DNA synthesis. For example, we can now explore very large numbers of biosynthetic pathway variants in parallel utilizing computational design algorithms, robotics, DNA synthesis and rapid multi-component DNA assembly technologies. Now that these new opportunities are available to us, we can use them to bypass the traditional trial-and-error approach to pathway development.

The development of synthetic biology strategies for the optimization of rate control and noise in the expression of this biosynthetic pathway will (i) add to our understanding of the evolution of pathway control in microbial systems; (ii) provide a platform for improving a diverse range of biotech processes. You will develop semi-automated protocols that allow exploration of a broad rate control space for a chosen ‘model’ biosynthetic pathway consisting of cellulose-degrading enzymes for the efficient conversion of cellulose into glucose, an important Ingenza pathway target for biofuel applications. Overlap extension PCR and Gibson-based assembly (via robotic protocols) will allow semi-automated exploration of a large number of combinations of synthetic gene expression elements (that provide a wide range of transcription and translation rates) and ORFs. Computational modelling (in collaboration with Pedro Mendes, Univ Manchester) will help us guide and interpret the flux optimization work.

Demonstration of cost-effective production of the target molecule (EtOH) through construction and optimisation of the model (cellulose-degrading enzyme) pathway will represent a commercial opportunity in the biofuel field for Ingenza. In addition, more efficient feedstock conversion into valuable chemical(s) could result in mid- to long-term environmental benefits. Regular interactions and placement within Ingenza will allow you to understand the challenges and priorities of an industrial biotechnology company. Overall, you will benefit from a unique combination of training experiences.

Interview date: Tuesday 26th January 2016