Predictive modelling and control of nutrients for the cost-effective production of cultivated meat

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With growing interest in cellular agriculture as a means to address public health, environmental and animal welfare challenges of animal agriculture, the concept of producing meat from cell- and tissue- cultures is emerging as an approach to address similar challenges. If proved to be commercially successful, cultivated meat has the potential to revolutionize the way in which we eat and interact with our environment. To achieve this potential, cost-effective production processes are necessary. The use of mathematical modelling can play an important role in this endeavor. Use of growth factors to activate cell proliferation signaling pathways is crucial to achieve required cell numbers and biomass for meat production. However, due to their high sensitivities to temperature, pH and several denaturants, frequent additions and high dosage concentrations of growth factors are often required within the bioreactor system to achieve target cell numbers in each experimental run. Bioreactor design configurations such as design type, size, stirrer type and stirrer speeds also contribute to the variabilities within the bioreactor which affect the growth factors' degradation and binding kinetics. Cost of these growth factors are very high and are currently the main bottlenecks in achieving efficient production costs required to make cultivated meat a commercially viable and competitively priced product to market. The goal of this project is to develop a process control system that accurately controls the growth factor addition strategy of a cultivated meat bioprocess. This is with the aims to facilitate process intensification of an in-house bioreactor, increase productivity, improve product quality and most importantly, lower cost. The two main objectives of this project include: quantifying and developing a mathematical model of growth factor degradation and bioactivity kinetics within a bioreactor, and developing process control algorithms that minimize the usage of growth factors and improve cost efficiency of the bioprocess as a whole. The strategies involved might include controlling the growth feeding schedule, concentrations and addition rates. The type of growth factors might also be included in the optimization strategy based on a given input, i.e. cell type and numbers and desired output, i.e. required biomass. The control system will refine growth factor utilization to engender a more cost-efficient process. Such an advance will play a significant role in making cultivated meat production commercially viable. This project falls within the EPSRC Mathematical Biology research area. Ivy Farm would be the industrial partner for this project.