As the size and complexity of synthetic genetic circuits increase, they progressively become too burdensome for a single cell. Consequently, many toy-model genetic circuits are easily lost to negative selection when the engineered organisms are exposed to less controlled environments. In contrast, natural systems are able to carry much larger genetic programs by using complex regulatory mechanisms to keep the costs of expression under check. They do this by a combination of temporal control over gene expression and additionally by spatial distribution of functions in multicellular systems. In this project, we plan to develop experimental and theoretical methods to measure the cost of maintaining and executing synthetic genetic circuits inside cells. Quantitative models will be built to calculate the cost of expression, and the accompanying growth effect, of single proteins and multi-protein circuits over time. The designs will be studied under different growth and stress conditions to assess the relationship between the calculated costs and the long-term evolutionary stability of the circuits. The analyses will inform design choices relevant for building the most cost-efficient unicellular circuits, and determine the upper size-limit at which it would be more efficient to distribute them across multiple cells in a consortium. We expect the results of this work to have key implications not only for the scale-up of synthetic genetic circuits but also for the understanding of ecosystem functions in microbial communities and division of labour in multicellular organisms.