Contribution

As part of our 2025 project, our team contributed to the iGEM community by improving the quantitative characterization of the pLux promoter (BBa_K2656028) through a Design–Build–Test–Learn (DBTL) cycle.

While this promoter and its associated devices already existed in the Registry, their quantitative behavior under varying AHL induction levels had not been fully modeled or parameterized. To address this, we developed and validated a mathematical model that describes pLux activation dynamics, conducted systematic experiments, and performed parameter identification using the Bayesian Adaptive Direct Search (BADS) algorithm.

Our work adds value to the iGEM Registry in three main ways:

Importantly, this refined model was used to design and predict the behavior of a new composite part based on the same promoter but a different RBS (BBa_B0032). When built and tested, the new part behaved exactly as predicted, validating the model and confirming the success of our engineering approach.

Through this contribution, we aim to support future iGEM teams who use the pLux promoter or related quorum-sensing components by offering a deeper, model-based understanding of their dynamics and by promoting the adoption of DBTL principles in part characterization.

The device, its components, and functioning are shown in the following SBOL schematic:

We performed a "partial" DBTL cycle including Design (via modeling), Test, and Learn (to relate the data and the model) to improve the characterization of this part as a contribution to future iGEMers.

The following sections detail each phase of our DBTL approach, demonstrating how we systematically improved the understanding and predictability of the pLux promoter system.

First, we developed the mathematical model, which results in the following set of differential equations:

Using this model with the following initial parameter estimates:

We can simulate the system for different concentrations of AHL, obtaining the DESIGN 1º Iteration curve shown in the results plot below.

Experimental Setup

Experiments were performed using Escherichia coli E. cloni 10G (Invitrogen) transformed with the plasmid following the part creator's guidelines and experimental setup. Overnight cultures were grown in LB medium with the appropriate antibiotics.

Culture Normalization

After the overnight, a normalization of the culture initial OD was performed for each sample, following the procedure Engineering Committee protocols and materials, in Minimal Media M9 with appropriate antibiotics.

Induction and Measurement

After normalization, 200 µL of each dilution was distributed in a 96-well plate. Samples were incubated for 20 hours at 37 °C and 230 rpm under double orbital shaking, with absorbance at 600 nm and fluorescence (excitation: 488 nm, emission: 530 nm) recorded every 5 minutes. After one hour of incubation, appropriate inductions were made to reach 10 different AHL concentrations (0, 5.7, 27, 41.4, 48.8, 75, 112.5, 135, 195, 300, 500 µM), each in triplicate.

Calibration

Calibration of OD and fluorescence followed the Engineering Committee protocols and materials, ensuring reproducibility and comparability of results.

Results

The resulting plot (TEST 1º Iteration) corresponds to a time average of the 2 hours after the expression stabilizes for each AHL concentration. The plot represents the mean steady state expression of GFP for each of the AHL concentrations. The squares are the mean GFP expression in MEFL/Particle, and error bars show the sample standard deviation.

Parameter Identification

For this stage, we focused on identifying the quantitative parameters that best describe the experimental behavior of the device containing the RBS B0034.

Objective Function

The parameter estimation was performed by minimizing the following cost function:

This objective function measures the root mean squared error between the experimental and simulated (predicted) GFP fluorescence levels in log scale, ensuring balanced weighting across induction ranges.

Optimization Algorithm

The optimization was carried out using the Bayesian Adaptive Direct Search (BADS) algorithm, which efficiently explores the parameter space to locate global optima. The process yielded a set of optimal parameter values representing both the system dynamics and promoter behavior and a prediction that can be seen in the figure (LEARN 1º Iteration).

Optimized Parameters

Using this model, we obtained the following optimized parameters:

Dual Construct Optimization

Importantly, this optimization was performed simultaneously for two constructs:

• The AHL-induced GFP expression device containing RBS BBa_B0034 (current iteration)
• Our new part BBa_253I2NAK, which includes RBS BBa_B0032 and resulted from a second DBTL cycle

This double iteration of the DBTL cycle represents our major engineering success, demonstrating the integration of model-driven learning and experimental validation within the iGEM framework.

Using the parameters learned from our model, we were able to predict the behavior of a new composite device that used the same promoter but a different RBS context. When built and tested experimentally, the new part performed exactly as predicted, validating the model's reliability and demonstrating the strength of our DBTL workflow.

This represents our first major engineering success, showing how modeling, experimentation, and learning can be combined to design functional synthetic devices predictively — a core principle of engineering in synthetic biology.