Cell-Free Biosensors and AI Integration.

Autor:	Soudier P; Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France., Faure L; Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France., Kushwaha M; Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France., Faulon JL; Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France. jean-loup.faulon@inrae.fr.; Université Paris-Saclay, Systems & Synthetic Biology Lab (iSSB), UMR, Evry, France. jean-loup.faulon@inrae.fr.; Manchester Institute of Biotechnology, SYNBIOCHEM Center, School of Chemistry, The University of Manchester, Manchester, UK. jean-loup.faulon@inrae.fr.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2022; Vol. 2433, pp. 303-323.
DOI:	10.1007/978-1-0716-1998-8_19
Abstrakt:	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. (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
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