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Our group aims to tackle these roadblocks to industrialization of synthetic biology by developing quantitative mathematical models that can inform and guide the engineering of biological systems. We develop models which combine metabolism, gene expression and microbial growth to understand how these multiple dynamic constraints emerge over the course of population growth during industrial production processes and how they impact the function of engineered gene circuits and pathways. We are working with academic and industrial partners to extend these modelling frameworks beyond the lab workhorse E. coli into industrially relevant strains to optimise real world bioprocesses. Within this framework we embed real world industrially relevant metabolic pathways and use mathematical techniques to identify key bottlenecks which limit their performance. Using this knowledge, we design control strategies which dynamically balance growth and production to improve efficiency and yield of these pathways. Our group works closely with experimental colleagues to validate model predictions in vivo and implement the new design strategies we identify.
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Daniel P. Byrom,Alexander P. S. Darlington
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDCpp.8844-8850, (2023)
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