Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Glucose sensing is vital for managing diabetes. However, current sensors are invasive and secrete enzymatic byproducts that are inflammatory and toxic. Though DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) emit fluorescence that is ideal for enzyme-free optical sensing, existing approaches have yet to identify a DNA sequence that can elicit a fluorescence response to glucose. We develop an approach based on clustering to design a diverse library of 90 DNA sequences to screen for a glucose response. The most responsive sequence was further improved based on favorable mutations predicted by deep learning and pattern recognition. This combination of experimental screening and supervised and unsupervised machine learning represents a generalizable approach to developing DNA-SWCNT sensors for even the most elusive analytes.