Synthetic Biology for Biomedical Applications Group, Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), E-08003 Barcelona, Spain.
Imperial College London, Gradpad Wood Lane, 80 Wood Lane Flat B417, London SW7 2AZ, U.K.
ACS Synth Biol. 2020 Jun 19;9(6):1328-1335. doi: 10.1021/acssynbio.0c00010. Epub 2020 May 14.
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, , 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.
许多研究致力于通过利用内在的天然传感器来设计细胞生物传感器。然而，生物传感器不仅依赖于输入检测，而且还依赖于足够的响应范围。因此，通常需要以可预测的方式调整自然系统，以满足特定应用的需求。在这项研究中，我们通过调节主要生物传感器组件，即受体蛋白，探索了双组分细菌生物传感器的可定制性。我们开发了一个数学模型，该模型描述了受体丰度与激活阈值、灵敏度、动态范围和工作范围之间的功能关系。所定义的数学框架允许设计具有最小遗传工程要求的双组分生物传感器的遗传结构。为了实验验证模型及其预测，构建了生物传感器文库。理论设计与实验结果之间的良好一致性表明，受体蛋白丰度的调节允许在最小遗传工程的情况下优化生物传感器设计。
