An Experimentally Verified Mechanistic Model for Predicting Quorum Sensing-Based Switches.

Area of Science:

• Synthetic biology
• Genetic circuit engineering
• Mathematical modeling

Background:

• Quorum sensing (QS) genetic circuits link population behavior to individual cell responses.
• Tuning QS circuits is challenging due to complex dynamics, especially in the Design-Build-Test-Learn (DBTL) cycle.
• The EsaI/EsaR system uses EsaI synthase and EsaR transcription factor for QS.

Purpose of the Study:

• To develop a mathematical model for predicting the response of the EsaI/EsaR QS system.
• To accelerate the tuning of QS-based genetic circuits in synthetic biology.
• To understand how transcription factor and synthase expression levels affect circuit output.

Main Methods:

• Developed a mathematical model to predict system response based on expression levels.
• Constructed a strain library for experimental validation.
• Optimized the model using experimental data from the EsaI/EsaR system.

Main Results:

• The model accurately predicted the effects of transcription factor and synthase expression on promoter response.
• The model visualized potential system outcomes and highlighted the transcription factor's tuning role.
• The model successfully differentiated expression level impacts on the bidirectional promoter.

Conclusions:

• The developed model is a valuable tool for fine-tuning EsaI/EsaR-based synthetic biology systems.
• This modeling approach can be foundational for tuning other LuxR-family QS systems.
• Mathematical modeling accelerates the DBTL cycle for QS circuit optimization.