Cell-free biosensing is an exciting field with many applications. The MerR family of transcriptional factors (TF) present in <i>E. coli </i>are capable of sensing heavy metal ion concentrations in the cell. We aim to develop a portable biosensor using CueR, a copper sensing TF belonging to the MerR family, to detect copper in samples like wastewater, drinking water, and biological samples based on cell-free technology. To achieve this goal, we engineered linear transcription units with varied promoter regions designed to optimize copper sensing with CueR. Panox-SP was used as the cell free system in which the experiments were performed. Transcription dynamics of the wildtype T7 promoter were compared with those of mutant T7 promoters with single and multiple CueR operators placed in different regions relative to the promoter. A computational model based on ordinary differential equations was constructed to simulate the transcription-translation dynamics of the sensor circuit. The overall goal of the model was to explain the copper sensitivity observed for different transcription units. Model parameters were either estimated from literature values or experimental measurements of the concentrations of mRNA (using fluorescent aptamers) and protein (using fluorescent reporters). Unknown model parameters were estimated by minimizing the difference between model simulations and the experimental data generated from this study. Permissible parameter ranges were established from literature values. In conclusion, we constructed a metal ion sensor circuit based on the CueR repressor and a mathematical model to optimize circuit performance. A quantitative model to predict the biosensing of metal ions like copper could be used as a starting point to simulate sensor performance for copper and other ions that are detectable via the MerR family of transcription factors.

Quantitative modeling to predict copper detection in cell-free systems using CueR transcription factor

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