Combinatorial Biosynthetic Pathway Engineering

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Bioactive natural products are of great biotechnological, biomedical, environmental and economic importance. For this reason, a large number of companies and academic groups are interested in using biosynthetic pathways derived from microorganisms to manufacture natural products. However, the production and purification of such products can be expensive, sometimes prohibitively so, and therefore maximising the productivity of the pathways is a major priority.The general aim of this project is to develop a combined computational and experimental strategy for optimising the productivity of microbial biosynthetic pathways, focusing on the Generally Regarded As Safe (GRAS) organism S cerevisiae, i.e. baker's yeast. A wide range of microbial pathways that make useful bioactive molecules can be imported at a genetic level into yeast. However, these pathways often have poor yields, which is a problem for a company that wants to manufacture a valuable product via an economically viable process. We therefore want to establish a set of rules how to optimise biosynthetic pathways in yeast, thus enabling the wider scientific and commercial community to maximise the productivity of these pathways in an efficient and predictable way. We are particularly interested in creating easy-to-use 'packages' of experimental and computational tools that can be utilised by small- to medium- sized commercial entities, since these are often constrained in terms of the amount of resources that they can dedicate to implementing new technologies. At the same time, we believe that our overall strategy will potentially benefit any industrial or academic team interested in producing bioactive molecules efficiently. In order to develop and apply our methods, we will focus on a model pathway, in this case a pathway that makes the polyketide molecule bikaverin. We will assemble a very large number of variants of the gene cluster that codes for the bikaverin pathway enzymes using synthetic DNA fragments. The performance of the resulting gene cluster variants will be analysed on a robotic platform once the variants have been introduced into yeast. This automation will greatly accelerate the process of comparing the performance of the different variants. The analytical data obtained through the above experimental procedures will be fed into an advanced new computational model, thus enhancing our understanding of how best to maximise the overall activity of the bikaverin pathway. The model will accordingly evolve as a result of this strategy, ultimately acting as a source of insight into how to design other biosynthetic pathways in order to maximise performance in the future.