Cell-Free Biosensors and AI integration
2022
Soudier, Paul | Faure, Léon | Kushwaha, Manish | Faulon, Jean-Loup | MICrobiologie de l'ALImentation au Service de la Santé (MICALIS) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Institut de biologie systémique et synthétique (ISSB) ; Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS) | University of Manchester [Manchester] | ANR-18-CE33-0015,VR-MARS,Réalité Virtuelle, Assistance et Secours Médical pour Spationautes(2018)
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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Показать больше [+] Меньше [-]Английский. 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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Эту запись предоставил Institut national de la recherche agronomique