Research Interests

Synthetic biology and microbial biotechnology offer sustainable routes to the manufacturer of commodity and high value chemicals from agricultural by-products instead of petrochemical feedstocks. However, at present the production of industrial strains is a time intensive and expensive process requiring multiple rounds of experimentation and redesign. A key cause of this is our difficulty in predicting the impact of the novel pathways on the host metabolism and wider physiology. Pathway enzyme production utilises the host gene expression machinery and drains host nucleotide and amino acid pools, while engineered pathways drain key metabolites from central carbon metabolism (as well as nucleotides and amino acids). These interactions perturb the host’s homeostasis, resulting in changes to resource supply which do not benefit either pathway performance or host growth. In addition to these internal constraints, these microbial ‘cell factories’ are subjected to industrial constraints including environmental heterogeneity, fermentation strategy, and cost.

Our group aims to tackle these roadblocks to industrialization of synthetic biology by developing quantitative mathematical models that can inform and guide the engineering of biological systems. We develop models which combine metabolism, gene expression and microbial growth to understand how these multiple dynamic constraints emerge over the course of population growth during industrial production processes and how they impact the function of engineered gene circuits and pathways. We are working with academic and industrial partners to extend these modelling frameworks beyond the lab workhorse E. coli into industrially relevant strains to optimise real world bioprocesses. Within this framework we embed real world industrially relevant metabolic pathways and use mathematical techniques to identify key bottlenecks which limit their performance. Using this knowledge, we design control strategies which dynamically balance growth and production to improve efficiency and yield of these pathways. Our group works closely with experimental colleagues to validate model predictions in vivo and implement the new design strategies we identify.