Lucia Bandiera, lucia.bandiera@ed.ac.uk
Filippo Menolascina, filippo.menolascina@ed.ac.uk
Project Description
Biomanufacturing plays a critical role across modern healthcare and industry. Recently, the potential of the field to deliver solutions to unmet clinical needs was underscored by the rapid advancement and application of mRNA vaccine technology.
However, the robustness of conventional biomanufacturing processes is undermined by their reliance on macro-level variables (e.g., temperature, dissolved oxygen, pH) to monitor and control production. Macro-level variables can delay detection of the emergence of undesired cell physiological states until it is too late for effective intervention.
Here we aim to address this challenge and advance biomanufacturing by developing hybrid biological/digital twins, termed “cybergenetic twins”. Using an innovative combination of Scientific Machine Learning (SciML), Constraint-Based Modelling, and Optimal Control principles, the candidate will develop a strategy for real-time, precise adjustments to bioprocesses, that promises to enhance their productivity and reliability.
