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. 2022:2433:303-323.
doi: 10.1007/978-1-0716-1998-8_19.

Cell-Free Biosensors and AI Integration

Paul Soudier  1 , Léon Faure  1 , Manish Kushwaha  1 , Jean-Loup Faulon  2   3   4
Affiliations

Cell-Free Biosensors and AI Integration

Paul Soudier et al. Methods Mol Biol. 2022.

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.

Keywords: Artificial neural networks; CAD; Machine learning; Metabolite biosensors; Perceptron; Transcription factors.

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