Advances in synthetic biology and metabolic engineering have expanded the potential of engineered cell-based systems. However, engineering cells to carry out multiple functions or produce products with extensive pathways leads to new challenges. These include bottlenecks, inefficient use of cell resources, and increased metabolic burden on individual cells. An emerging area of focus is on using microbial co-cultures or small consortia wherein individual populations work together to accomplish a desired output in cooperation with the rest of the consortia. In this way, cells can be engineered to carry out specific tasks allowing for specialization and division of labor. However, this strategy requires not only regulation of gene transcription but also regulation of the cell populations. Here, we design a platform for regulating the composition of individual populations within a co-culture based on an input signal that can be either user-applied or an existing environmental cue, in our example the autoinducer-2 (AI-2) quorum sensing (QS) signal. The former case allows for operator fine-tuning and control of the system, while the latter case allows for pre-programmed and autonomous regulation. Our system consists of two <i>E. coli</i> cell populations. The first population produces an AI-1 QS signal in response to the level of the AI-2 QS signal. The level of the AI-1 signal regulates the growth rate of a second strain resulting in a change in co-culture composition, measured using constitutively expressed fluorescent proteins. We characterized the individual strains and show that co-culture composition over time is modulated by the initial level of AI-2 in the environment. We further developed a simple mathematical model to predict behavior of the co-culture for a range of inducer or AI-2 concentrations and initial cell populations. We envision this or similar strategies could be used to regulate multi-strain microbial systems for use in batch or continuous operations.

Programming bacterial consortia: Platforms for user or autonomously regulated population compositions

2019 Spring National Meeting

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