Alexander Darlington – 12/11/2026

Engineering synthetic gene circuits in light of host constraints: integrating stresses, controlling metabolism and creating evolutionary robustness

Engineered microbial systems have a range of potential applications in healthcare, the chemicals industry and environmental science. However, host microbe constraints often lead to degraded functionality or even complete failure of genetic devices. Here we developed a unified dynamic mathematical framework of E. coli  which integrates including metabolic constraints, stress-responses, ribosomal limitations and growth mediated feedback. We use this framework to design new control strategies which act to enhance both yield and growth of microbial cell factories and show that at by accounting for host constraints we can actually simplify the genetic controller designs by taking advantage of the cell’s natural feedback mechanisms. We couple our host design framework with evolutionary simulations to investigate how to enhance long term performance of gene circuits. We will discuss our recent work developing similar frameworks for S. cerevisiae and P. putida.

 

Short bio

Dr Alexander Darlington’s research lies at the interface of systems engineering and biotechnology. After completing degrees in Genetics and Systems Biology, he undertook a PhD at the University of Warwick focused on the design of translational control systems. He subsequently held a postdoctoral position applying control-theoretic approaches to improve the performance of synthetic gene circuits, followed by an Innovation Fellowship with Ingenza Ltd, where he developed methods for optimising engineered metabolic pathways. In 2021, he won a Royal Academy of Engineering Research Fellowship to develop new design frameworks for engineering biological systems within both cellular and industrial constraints. Now based at the Manchester Institute of Biotechnology, his research group develops quantitative methods for engineering biology, including approaches for deriving predictive models from sparse data, designing metabolic control systems, enhancing recombinant protein production, and improving the robustness of engineered gene circuits in the face of evolutionary change.

 

Laboratory of the speaker

Darlington group at the Manchester Institute of Biotechnology

 

Invited by

Manish Kushwaha

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