Engineering a novel self-powering electrochemical biosensor |
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Authors: | X. Gu M. Trybiło S. Ramsay M. Jensen R. Fulton S. Rosser D. Gilbert |
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Affiliation: | (1) School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK;(2) University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK;; |
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Abstract: | This paper records the efforts of a multi-disciplinary team of undergraduate students from Glasgow University to collectively design and carry out a 10 week project in Synthetic Biology as part of the international Genetic Engineered Machine competition (iGEM). The aim of the project was to design and build a self-powering electrochemical biosensor called ‘ElectrEcoBlu’. The novelty of this engineered machine lies in coupling a biosensor with a microbial fuel cell to transduce a pollution input into an easily measurable electrical output signal. The device consists of two components; the sensor element which is modular, allowing for customisation to detect a range of input signals as required, and the universal reporter element which is responsible for generating an electrical signal as an output. The genetic components produce pyocyanin, a competitive electron mediator for microbial fuel cells, thus enabling the generation of an electrical current in the presence of target chemical pollutants. The pollutants tested in our implementation were toluene and salicylate. ElectrEcoBlu is expected to drive forward the development of a new generation of biosensors. Our approach exploited a range of state-of-the-art modelling techniques in a unified framework of qualitative, stochastic and continuous approaches to support the design and guide the construction of this novel biological machine. This work shows that integrating engineering techniques with scientific methodologies can provide new insights into genetic regulation and can be considered as a reference framework for the development of biochemical systems in synthetic biology. |
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Keywords: | Biosensor Synthetic biology Genetic engineering Computational modelling |
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