Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology.
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Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the P(luxI) promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of "hidden interactions" on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context.
Published Version (Please cite this version)10.1371/journal.pone.0011909
Publication InfoMarguet, Philippe; Tanouchi, Yu; Spitz, Eric; Smith, Cameron; & You, Lingchong (2010). Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology. PLoS One, 5(7). pp. e11909. 10.1371/journal.pone.0011909. Retrieved from https://hdl.handle.net/10161/4558.
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Professor of Biomedical Engineering
The You lab uses a combination of mathematical modeling, machine learning, and quantitative experiments to elucidate principles underlying the dynamics of microbial communities in time and space and to control these dynamics for applications in computation, engineering, and medicine.