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

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.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.