Abstract
Cell-free biosensors hold a great potential as alternatives for traditional analytical chemistry methods providing low-cost low-resource measurement of specific chemicals. However, their large-scale use is limited by the complexity of their development.
In this chapter, we present a standard methodology based on computer-aided design (CAD ) tools that enables fast development of new cell-free biosensors based on target molecule information transduction and reporting through metabolic and genetic layers, respectively. Such systems can then be repurposed to represent complex computational problems, allowing defined multiplex sensing of various inputs and integration of artificial intelligence in synthetic biological systems.
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Change history
05 January 2022
The chapter authors name was incorrectly published and it has been updated as “Manish Kushwaha” now.
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Acknowledgments
PS and JLF are supported by the ANR SynBioDiag grant number ANR-18-CE33-0015. LF is supported by the French National Research Institute for Agriculture, Food, and Environment (INRAE), through the “Métaprogramme BIOLPREDICT”. MK acknowledges funding support from Ile-de-France region’s DIM-RFSI, INRAE’s MICA department and the University of Paris-Saclay.
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Soudier, P., Faure, L., Kushwaha, M., Faulon, JL. (2022). Cell-Free Biosensors and AI Integration. In: Karim, A.S., Jewett, M.C. (eds) Cell-Free Gene Expression. Methods in Molecular Biology, vol 2433. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1998-8_19
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DOI: https://doi.org/10.1007/978-1-0716-1998-8_19
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