An experimentally verified mechanistic model for predicting quorum sensing-based switches

Abstract

Quorum sensing-based genetic circuits are gaining traction in synthetic biology as they link population-level behaviour to individual cell responses. However, tuning these circuits remains challenging due to complex dynamics, particularly during the Learn phase of the Design-Build-Test-Learn (DBTL) cycle. To accelerate this process, we developed a mathematical model to predict how varying expression levels of the transcription factor and synthase affect the response of the EsaI/EsaR quorum sensing system. A strain library was constructed, and experimental data were used to optimize the model. The final model could successfully differentiate between the effects of these expression levels on the response of the bidirectional promoter. It allowed visualization of all potential system outcomes and emphasized the transcription factors critical role in tuning the circuit. This model offers a valuable tool for fine-tuning EsaI/EsaR-based systems for synthetic biology applications. Moreover, given the homology within the LuxR-family quorum sensing systems, this modelling approach may serve as a foundation for model-based tuning of other quorum sensing systems.