Optimising microbial protein biotechnology using machine learning and mathematical optimisation for protein sustainability

To apply for this project please visit the LIDo website: https://www.lido-dtp.ac.uk/apply

The plant-sourced and animal-sourced proteins are carbon-intensive, resource-demanding, and vulnerable to pandemic impacts due to long-production cycles. This combined with increasing protein demands and pandemic of hunger highlight the challenges on providing sustainable protein solutions. Microbial proteins produced under controlled fermentation with advanced optimisation-aided design enable rapid biotechnology advancement for future protein security and sustainability.

This cross-disciplinary project aims to couple machine learning and advanced optimisation approach to enable step-change in microbial protein biotechnologies to achieve zero-waste, zero-emissions, where sustainable Mycoprotein innovation will be tested as a representative study.

Specifically, underpinned by process control, optimisation theory and machine learning techniques, this project will develop a learning-based controller to harness real-time data and optimise mycoprotein fermentation to achieve maximised resource efficiency and minimised waste; such fermentation represents an advanced industrial biotechnology with multivariable dynamics, nonlinearities, and constraints. The data-driven machine learning techniques will play a significant role to deal with the highly-nonlinear and complex process of mycoprotein biotechnology that the analytical methods are difficult to be applied. The data-driven approach offers a feasible alternative to reveal undercovered characteristics through machine learning. The developed learning-based model predictive control approach will advance microbial protein technologies and enable autonomous fermentation with waste recovery and optimised resource utilisation.

Coupling data value chains and advanced computational modelling has the potential to bring tremendous opportunities to protein biotechnology to enable real-time data acquisition, analysis and data-informed responsive optimisation to achieve sustainability across supply chains. Underpinned by this new concept, this project will further couple simulation, life cycle sustainability, mathematical optimisation and learning algorithms to develop a novel data-driven optimisation with hybrid solution search algorithms to bring real-time supply chain data (e.g. sensor data) to precision decision-support; the optimisation approach will be tested on Mycorpotein supply chains to achieve zero-emissions.

This exciting PhD programme offers a unique research opportunity at the interface of Engineering and Life Sciences. The PhD research will be well supported by complementary expertise offered by supervisors from the Department of Engineering at King’s College London and project partner Quorn Foods. PhD student will be exposed to cross-disciplinary and collaborative research environment. Student will receive training opportunities not only by academic collaboration but also through placement at Quorn; PhD will have access to the state-of-the-art facilities (e.g. analytical research lab and scaling-up facilities) and extensive expertise at both King’s College London and Quorn Foods. Our supervision team has committed to mentor and engage the PhD student in external collaboration involving both exploratory research and industry-relevant activities.

We invite talented students with interests in cross-disciplinary research to apply for this PhD position starting from Oct 2022.

If you are interested in applying, please email Dr Miao Guo, [[Email Address Removed]], for pre-submission enquiry and discussion.

To apply for this project please visit the LIDo website: https://www.lido-dtp.ac.uk/apply