Two-Component Biosensors: Unveiling the Mechanisms of Predictable Tunability.

Many studies have been devoted to the engineering of cellular biosensors by exploiting intrinsic natural sensors. However, biosensors rely not only on input detection but also on an adequate response range. It is therefore often necessary to tune natural systems to meet the demands of specific applications in a predictable manner. In this study, we explored the customizability of two-component bacterial biosensors by modulating the main biosensor component, i.e., the receptor protein. We developed a mathematical model that describes the functional relationship between receptor abundance and activation threshold, sensitivity, dynamic range, and operating range. The defined mathematical framework allows the design of the genetic architecture of a two-component biosensor that can perform as required with minimal genetic engineering. To experimentally validate the model and its predictions, a library of biosensors was constructed. The good agreement between theoretical designs and experimental results indicates that modulation of receptor protein abundance allows optimization of biosensor designs with minimal genetic engineering.