生物传感器
计算机科学
多路复用
稳健性(进化)
计算生物学
生物
基因
生物化学
电信
作者
Alexis Courbet,Drew Endy,Éric Renard,Franck Molina,J. Bonnet
出处
期刊:Science Translational Medicine
[American Association for the Advancement of Science (AAAS)]
日期:2015-05-27
卷期号:7 (289)
被引量:208
标识
DOI:10.1126/scitranslmed.aaa3601
摘要
Whole-cell biosensors have several advantages for the detection of biological substances and have proven to be useful analytical tools. However, several hurdles have limited whole-cell biosensor application in the clinic, primarily their unreliable operation in complex media and low signal-to-noise ratio. We report that bacterial biosensors with genetically encoded digital amplifying genetic switches can detect clinically relevant biomarkers in human urine and serum. These bactosensors perform signal digitization and amplification, multiplexed signal processing with the use of Boolean logic gates, and data storage. In addition, we provide a framework with which to quantify whole-cell biosensor robustness in clinical samples together with a method for easily reprogramming the sensor module for distinct medical detection agendas. Last, we demonstrate that bactosensors can be used to detect pathological glycosuria in urine from diabetic patients. These next-generation whole-cell biosensors with improved computing and amplification capacity could meet clinical requirements and should enable new approaches for medical diagnosis.
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