Genetic circuits combined with machine learning provides fast responding living sensors

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buir.contributor.authorSaltepe, Behide
buir.contributor.authorBozkurt, Eray Ulaş
buir.contributor.authorGüngen, Murat Alp
buir.contributor.authorÇiçek, A. Ercüment
buir.contributor.authorŞeker, Urartu Özgür Şafak
buir.contributor.orcidSaltepe, Behide|0000-0002-4338-6463
buir.contributor.orcidBozkurt, Eray Ulaş|0000-0001-7787-9357
buir.contributor.orcidGüngen, Murat Alp|0000-0001-6381-5926
buir.contributor.orcidÇiçek, A. Ercüment|0000-0001-8613-6619
buir.contributor.orcidŞeker, Urartu Özgür Şafak|0000-0002-5272-1876
dc.citation.epage9en_US
dc.citation.spage1en_US
dc.citation.volumeNumber178en_US
dc.contributor.authorSaltepe, Behide
dc.contributor.authorBozkurt, Eray Ulaş
dc.contributor.authorGüngen, Murat Alp
dc.contributor.authorÇiçek, A. Ercüment
dc.contributor.authorŞeker, Urartu Özgür Şafak
dc.date.accessioned2022-03-03T08:01:46Z
dc.date.available2022-03-03T08:01:46Z
dc.date.issued2021-04-15
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentInstitute of Materials Science and Nanotechnology (UNAM)en_US
dc.description.abstractWhole 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.en_US
dc.description.provenanceSubmitted by Dilan Ayverdi (dilan.ayverdi@bilkent.edu.tr) on 2022-03-03T08:01:46Z No. of bitstreams: 1 Genetic_circuits_combined_with_machine_learning_provides_fast_responding_living_sensors.pdf: 4716845 bytes, checksum: 9749a3a12ec3fe3c304486b177e362e2 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-03-03T08:01:46Z (GMT). No. of bitstreams: 1 Genetic_circuits_combined_with_machine_learning_provides_fast_responding_living_sensors.pdf: 4716845 bytes, checksum: 9749a3a12ec3fe3c304486b177e362e2 (MD5) Previous issue date: 2021-04-15en
dc.identifier.doi10.1016/j.bios.2021.113028en_US
dc.identifier.eissn1873-4235
dc.identifier.issn0956-5663
dc.identifier.urihttp://hdl.handle.net/11693/77668
dc.language.isoEnglishen_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.bios.2021.113028en_US
dc.source.titleBiosensors and Bioelectronicsen_US
dc.subjectSynthetic biologyen_US
dc.subjectWhole-cell biosensorsen_US
dc.subjectLiving sensorsen_US
dc.subjectNeural networksen_US
dc.subjectMachine learningen_US
dc.titleGenetic circuits combined with machine learning provides fast responding living sensorsen_US
dc.typeArticleen_US

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Institute of Materials Science and Nanotechnology (UNAM)
Department of Computer Engineering