Microfluidic-based bacterial molecular computing on a chip
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Date
2022-07-25
Authors
Martins, Daniel P.
Taynnan Barros, Michael
O’Sullivan, Benjamin J.
Seymour, Ian
O’Riordan, Alan
Coffey, Lee
Sweeney, Joseph B.
Balasubramaniam, Sasitharan
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Biocomputing systems based on engineered bacteria can lead to novel tools for environmental monitoring and detection of metabolic diseases. In this paper, we propose a Bacterial Molecular Computing on a Chip (BMCoC) using microfluidic and electrochemical sensing technologies. The computing can be flexibly integrated into the chip, but we focus on engineered bacterial AND Boolean logic gate and ON-OFF switch sensors that produces secondary signals to change the pH and dissolved oxygen concentrations. We present a prototype with experimental results that shows the electrochemical sensors can detect small pH and dissolved oxygen concentration changes created by the engineered bacterial populations’ molecular signals. Additionally, we present a theoretical model analysis of the BMCoC computation reliability when subjected to unwanted effects, i.e., molecular signal delays and noise, and electrochemical sensors threshold settings that are based on either standard or blind detectors. Our numerical analysis found that the variations in the production delay and the molecular output signal concentration can impact on the computation reliability for the AND logic gate and ON-OFF switch. The molecular communications of synthetic engineered cells for logic gates integrated with sensing systems can lead to a new breed of biochips that can be used for numerous diagnostic applications.
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Keywords
Bacterial molecular computing , Biosensors , Electrochemical sensing , Microfluidics , Molecular communications , Synthetic logic gates
Citation
Daniel P. Martins; Barros, M. T., O’Sullivan, B. J., Seymour, I., O’Riordan, A., Coffey, L., Sweeney, J. B. and Balasubramaniam, S. (2022) 'Microfluidic-based bacterial molecular computing on a chip', IEEE Sensors Journal, 22(17), pp. 16772-16784. doi: 10.1109/JSEN.2022.3192511
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