| Literature DB >> 34985753 |
Paul Soudier1, Léon Faure1, Manish Kushwaha1, Jean-Loup Faulon2,3,4.
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.Entities:
Keywords: Artificial neural networks; CAD; Machine learning; Metabolite biosensors; Perceptron; Transcription factors
Mesh:
Year: 2022 PMID: 34985753 DOI: 10.1007/978-1-0716-1998-8_19
Source DB: PubMed Journal: Methods Mol Biol ISSN: 1064-3745