Cell-Free Biosensors and AI integration

Paul Soudier (1) , Léon Faure (1) , Manish Kushwaha (1) , Jean-Loup Faulon (1, 2, 3)

1 MICALIS - MICrobiologie de l'ALImentation au Service de la Santé
2 ISSB - Institut de biologie systémique et synthétique
3 University of Manchester [Manchester]

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.

Mots clés: Metabolite biosensors; Transcription factors; Machine learning CAD; Artificial neural networks; Perceptron

Domaines: Sciences du Vivant [q-bio]

Paul Soudier, Léon Faure, Manish Kushwaha, Jean-Loup Faulon. Cell-Free Biosensors and AI integration. Cell-Free Gene Expression, 2433, Springer US, pp.303-323, 2022, Methods in Molecular Biology, ⟨10.1007/978-1-0716-1998-8_19⟩. ⟨hal-03545753⟩