A Digital Twin (DT) of a bioprocess is a detailed, predictive, systems-level computational model of a cell culture or fermentation process. Such DTs can make the design of media and feeding strategies for a bioproduction process cheap, fast, and reliable. Currently, media and feeding strategy design is a major challenge in industrial biotechnology despite having advances in high-throughput screening and multivariate data analysis techniques.
It is particularly challenging to understand the complex interactions and impact of more than 50 media components on product titre, yield, or product quality. These interactions lead to a high-dimensional design space that cannot be explored adequately using trial-and-error or even statistically designed experiments. Our project aims to develop an artificial intelligence (AI)-enabled DT to revolutionise the design of media and feeding strategies for bioprocesses; thereby, significantly reducing the operational burden and cost of optimizing industrial cell culture processes.
Design, development, and validation of DTs require real-life bioprocess data, including standard measurements for process performance and media consumption, as well as genetic, genomic, biochemical, and physiological data of an organism. The genome-based, systems-level metabolic model of an organism will be combined with an artificial neural network-based bioreactor process model to determine process parameters, establishing the dynamic interactions between the intracellular metabolic fluxes and overall process variables. Mechanistic know-how is thus combined with data-driven know-how to achieve a state-of-the-art mathematical model that can predict the cell culture process performance with unprecedented accuracy.
This project, funded by a renowned biopharmaceutical company, provide an excellent opportunity to work with Dr Ahsan Islam’s research group within the Chemical Engineering Department at Loughborough University. You will be joining a vibrant and multidisciplinary research community, focusing on designing and developing sustainable processes for a NET Zero future through harnessing the power of machine learning, systems biology, synthetic biology, and metabolic engineering. Due to industrial collaboration, the project offers the candidate a unique opportunity not only to learn and master cutting-edge research tools but also to obtain hands-on training in the industry.
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Additional Funding Information
The studentship is for 3 years and provides a tax-free stipend of £18,622 per annum for the duration of the studentship plus university tuition fees.
Tuition fees cover the cost of your teaching, assessment and operating University facilities such as the library, IT equipment and other support services. University fees and charges can be paid in advance and there are several methods of payment, including online payments and payment by instalment. Fees are reviewed annually and are likely to increase to take into account inflationary pressures.