Saltepe Behide, Bozkurt Eray Ulaş, Güngen Murat Alp, Çiçek A Ercüment, Şeker Urartu Özgür Şafak
UNAM - Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center, Bilkent University, 06800, Bilkent-Ankara, Turkey.
Computer Engineering Department, Bilkent University, 06800, Bilkent-Ankara, Turkey; Computer Biology Department, Carnegie Mellon University, 15213, Pittsburgh, PA, USA.
Biosens Bioelectron. 2021 Apr 15;178:113028. doi: 10.1016/j.bios.2021.113028. Epub 2021 Jan 23.
Whole cell biosensors (WCBs) have become prominent in many fields from environmental analysis to biomedical diagnostics thanks to advanced genetic circuit design principles. Despite increasing demand on cost effective and easy-to-use assessment methods, a considerable amount of WCBs retains certain drawbacks such as long response time, low precision and accuracy. Here, we utilized a neural network-based architecture to improve the features of WCBs and engineered a gold sensing WCB which has a long response time (18 h). Two Long-Short Term-Memory (LSTM)-based networks were integrated to assess both ON/OFF and concentration dependent states of the sensor output, respectively. We demonstrated that binary (ON/OFF) network was able to distinguish between ON/OFF states as early as 30 min with 78% accuracy and over 98% in 3 h. Furthermore, when analyzed in analog manner, we demonstrated that network can classify the raw fluorescence data into pre-defined analyte concentration groups with high precision (82%) in 3 h. This approach can be applied to a wide range of WCBs and improve rapidness, simplicity and accuracy which are the main challenges in synthetic biology enabled biosensing.
由于先进的基因电路设计原理，全细胞生物传感器（WCBs）在从环境分析到生物医学诊断的许多领域中变得日益突出。尽管对经济高效且易于使用的评估方法的需求不断增加，但相当数量的WCBs仍存在某些缺点，如响应时间长、精度和准确性低。在此，我们利用基于神经网络的架构来改善WCBs的特性，并设计了一种响应时间长（18小时）的金传感WCB。集成了两个基于长短期记忆（LSTM）的网络，分别用于评估传感器输出的开/关状态和浓度依赖状态。我们证明，二元（开/关）网络能够在30分钟时就以78%的准确率区分开/关状态，在3小时时准确率超过98%。此外，当以模拟方式进行分析时，我们证明该网络能够在3小时内将原始荧光数据高精度（82%）地分类到预定义的分析物浓度组中。这种方法可应用于广泛的WCBs，并改善合成生物学驱动的生物传感中的主要挑战——快速性、简单性和准确性。
