102800Computational and synthetic biology approaches for optimised mammalian bioproductionClosedCollaborative R&DInnovate UKMany modern therapeutic treatments require products and drugs that must to be manufactured in human cells for them to work effectively. At present, the human cell based manufacturing industry is dominated by a few standard systems (normally CHO or HEK-293). These systems were first established for research use only, in some cases as far back as 1957 (CHO) and were eventually adapted for large scale manufacture in the late 1980's. Consequently, these systems are far from optimal, and were created at a time when genetic engineering was in its infancy. Hence, there are many areas of potential improvement in these systems that would significantly increase their productivity, also thereby decreasing the manufacturing costs of one the most expensive classes of new drugs. This project aims to take a whole systems approach to optimising these production approaches by improving the DNA that is used to encode the protein drugs, the cell lines used for their production, and develop predictive algorithms that can help to make key strategic decisions before a manufacturing process is initiated e.g. based on the protein drugs sequence, should we expect production problems? And how can these be mitigated before they are encounted in the manufaturing process? This proposal incorporates the state-of-the-art, and a range of innovations that use machine learning, gene editing, DNA analysis, and cell manipulation, to collectively improve the productivity of mammalian biology.