Current industrial bio-production processes exclusively uses yeast and bacteria as the microorganism hosts to synthesise valuable commercial compounds. However, the raw material to product conversion efficiency using these hosts is extremely low (10% to 20%), breaching the concept of circular economy and sustainable production. Therefore, cyanobacteria, a more advanced type of microorganism hosts, have received great attention in recent years for disruptive industrial biotechnology development. Despite the rapid growth of low-cost genetic engineering tools, cyanobacterial metabolism is more complex than yeasts or bacteria, and intracellular metabolic constraints restricting the synthesis of targeted bioproduct are known to shift under different process operational scales.
This project aims to integrate cutting-edge modelling tools including dynamic flux balance analysis, hybrid process modelling (physical and data-driven), and computational fluid dynamics to construct a multiscale dynamic modelling framework that can accurately visualise the effect of important operating factors and bioreactor configurations on cyanobacterial metabolism activities (e.g. biomass growth, product synthesis) throughout the entire process time course. By optimising this modelling framework, primary metabolic constraints limiting bioproduct synthesis at industrial scale bioreaction systems will be identified directly, hence discovering new biological understandings which are of immediate industrial relevance. This information will be applied to direct the design of new strains and verified via experimentation (in collaboration with other UK universities). A brief introduction to the research group can be found through the following link:
Candidates are expected to hold or achieve a first class or 2:1 honours degree (or equivalent) in Chemical Engineering, Process Systems Engineering, Biochemical Engineering, Industrial Engineering, Mathematics, Computer Science or other related area. Students with a solid mathematical background are particularly welcome. A prior knowledge/experience in mathematical programming and optimisation theory is desirable.