Genetic circuits combined with machine learning provides fast responding living sensors

Saltepe B., Bozkurt E. U., Gungen M. A., Cicek A. E., ŞEKER U. Ö. Ş.

BIOSENSORS & BIOELECTRONICS, cilt.178, 2021 (SCI-Expanded, Scopus) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 178
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.bios.2021.113028
  • Dergi Adı: BIOSENSORS & BIOELECTRONICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, EMBASE, INSPEC, MEDLINE, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.