Tools for engineering and modeling bacterial populations dynamics

The last decade has seen bacteria at the forefront of biotechnological innovation, with applications including biomolecular computing, living therapeutics, microbiome engineering and microbial factories. These emerging applications are all united by the need to precisely control complex microbial dynamics in spatially extended environments, requiring tools that can bridge the gap between intracellular and population-level coordination. Therefore, in this thesis we propose genetic circuits for predictable control of microbial communities with potential applications in both healthcare and industry, ranging from therapeutics and drug delivery, to bioproduction and bioremediation. In Chapter 1 we focus on intra-population dynamics control by designing a genetic circuit which merges the benefits of the synchronization capabilities of quorum sensing to the advantages of external control of chemical inducers. This circuit design can provide more fine-tuned control of cargo release from bacterial carriers by enabling time and space regulation. Subsequently, in Chapter 2 we engineer and characterize inter-species dynamics of a three-strain bacterial community which interacts through cyclic competition, demonstrating the emergence of non-trivial dynamics. In particular, we found that intrinsic differences in the bacterial competitive strength lead to an unbalanced community that was, counterintuitively, dominated by the weakest strain. We used computational tools to model the three strains dynamics in 2D, providing a tool which accurately simulates the competition outcome of the community for any chosen parameter set. Finally, in Chapter 3 we focus on single cell dynamics and developed a bioinformatics pipeline to analyze dynamic transcriptomics data obtained using an innovative high throughput microfluidic device developed in our lab