Emergent population-level genetic oscillations in a synthetic bacterial microconsortium

Ye Chen1,*, Jae Kyoung Kim2,*, Andrew J. Hirning1, KreÅ¡imir JosiÄ?3,4 and
Matthew R. Bennett1,5

1 Department of Biosciences, Rice University, Houston, TX 77005, USA

2 Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA

3 Department of Mathematics, University of Houston, Houston, TX 77204, USA

4 Department of Biology and Biochemistry, University of Houston, Houston, TX 77204, USA

5 Institute of Biosciences and Bioengineering, Rice University, Houston, TX 77005, USA

* These authors contributed equally

To date, the majority of synthetic gene circuits have been constructed to operate within single, isogenic cellular populations. It has been proposed that synthetic microconsortia will provide a means of engineering novel population-level phenotypes that are difficult to obtain with single strains. While circuits that use multiple strains simultaneously to achieve population-level phenotypes have been reported, they generally use a single intercellular signaling molecule and hence the variety of their phenotypes is limited. Here, we use two genetically distinct populations of Escherichia coli and two different signaling mechanisms to engineer a bacterial microconsortium that exhibits robust oscillations in gene transcription. Specifically, we used two different bacterial quorum sensing systems to construct an â??activatorâ? strain and a â??repressorâ? strain that respectively up- and down-regulate gene expression in both strains. When co-cultured in a microfluidic device, the two strains form coupled positive and negative feedback loops at the population-level. The interacting strains exhibit robust, synchronized oscillations that are absent if either strain is cultured in isolation. We used a combination of mathematical modeling and targeted genetic perturbations to better understand the roles of circuit topology and regulatory promoter strengths in generating and maintaining these oscillations. We found that the dual-feedback topology was robust to changes in promoter strengths and fluctuations in the population ratio of the two strains. These findings demonstrate that one can program population-level dynamics through the genetic engineering of multiple cooperative strains.