AbstractOver the past two decades, synthetic biology has yielded ever more complex genetic circuits able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a scale step to the design-build-test workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof-of-concept, we designed and tested a genetic circuit under different temperatures, mediums, inducer concentrations, and bacterial growth phases. We determined that the circuit’s performance is dramatically altered when these factors differ from the optimal lab conditions. Based on these results, a scaling effort, coupled with a learning process, proceeded to generate model predictions for the genetic circuit’s performance under untested conditions, which is currently lacking in synthetic biology application design. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on a scale step is needed to ensure correct and robust performances.
