A Digital Engineering Approach to Accelerate Bioprocess Development for Cellular Agriculture

The agrifood system accounts for about a third of global greenhouse gas emissions [1], uses 70% of freshwater [2] and about 50% of arable land [3]. To limit global warming to no more than 1.5 deg. C, the United Nations state that we need to reach net zero emissions by 2050 [4]. However, global meat, dairy and egg production is projected to increase further: by 17% over the next decade [5]. To avert the worst impacts of climate change, novel low emission food production systems are imperative. Cellular agriculture has emerged as an alternative protein source that can increase our food systems resilience and lower its environmental impact. To fulfil the positive impacts of this burgeoning industry, productive, scalable and cost-efficient processes are necessary to commercialise novel cellular agriculture products. Through greater predictive power and lower development costs, digital engineering and scale-down models have the potential to increase the speed to market, by reducing the process development lifecycle of cellular agriculture. Enabling a paradigm shift in our approach to bioprocess characterisation, optimisation and scale-up. Goal: Develop highly predictive digital twins and novel scale-down models for precision fermentation processes (e.g. a yeast strain producing oils/fats), that correlates the cellular experience to process performance. This PhD project will combine fermenter digital twins with wet-lab fermentations, to generate insights that correlate cell performance with their simulated physiochemical environment. These insights will inform the development of bespoke scale-down models: enabling high-throughput methods for screening early microbial strain candidates. Focus areas: 1. In Silico Models of Microbial Fermenters: The successful candidate will use computer-aided design (CAD) to translate existing fermenter equipment into digital models and employ computational fluid dynamics (CFD) to create predictive digital twins. 2. Bench-Scale Fermentations: The doctoral researcher will conduct bench-scale fermentations (using a range of operating modes), employing Design of Experiments (DoE) to determine the optimal operating environments for cell growth, productivity and product quality. 3. Novel Scale-Down Models: This will involve the design, fabrication and operation of bespoke scale-down cultivation systems that mimic the most pertinent physical and biochemical environments that impact the precision fermentation key performance indicators.