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

Lead Research Organisation: University of Warwick
Department Name: School of Life Sciences


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. 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. 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.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/M01116X/1 30/09/2015 29/09/2023
1790816 Studentship BB/M01116X/1 02/10/2016 30/03/2021 Gurdamanjit Singh