Model-guided gene circuit design for engineering genetically stable cell populations in diverse applications

Journal article

Maintaining engineered cell populations' genetic stability is a key challenge in synthetic biology. Synthetic genetic constructs compete with a host cell's native genes for expression resources, burdening the cell and impairing its growth. This creates a selective pressure favouring mutations which alleviate this growth defect by removing synthetic gene expression. Non-functional mutants thus spread in cell populations, eventually making them lose engineered functions. Past work has attempted to limit mutation spread by coupling synthetic gene expression to survival. However, these approaches are highly context-dependent and must be tailor-made for each particular synthetic gene circuit to be retained. By contrast, we develop and analyse a biomolecular controller which depresses mutant cell growth independently of the mutated synthetic gene's identity. Modelling shows how our design can be deployed alongside various synthetic circuits without any re-engineering of its genetic components, outperforming extant gene-specific mutation spread mitigation strategies. Our controller's performance is evaluated using a novel simulation approach which leverages resource-aware cell modelling to directly link a circuit's design parameters to its population-level behaviour. Our design's adaptability promises to mitigate mutation spread in an expanded range of applications, while our analyses provide a blueprint for using resource-aware cell models in circuit design.

Authors: Sechkar, K; Steel, H

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Royal Society
• Journal: Journal of the Royal Society Interface
• Volume: 22
• Issue: 223
• Article number: 20240602
• Place of publication: England
• Publication date: 2025-02-12
• Acceptance date: 2024-11-13
• DOI: 10.1098/rsif.2024.0602
• EISSN: 1742-5662
• ISSN: 1742-5689
• Pmid: 39933591
• Language: English
• Keywords: synthetic biology; gene circuit design; burden; resource competition; mathematical model; population