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Synthetic neural-like computing in microbial consortia for pattern recognition

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted...

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Autores principales: Li, Ximing, Rizik, Luna, Kravchik, Valeriia, Khoury, Maria, Korin, Netanel, Daniel, Ramez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8149857/
https://www.ncbi.nlm.nih.gov/pubmed/34035266
http://dx.doi.org/10.1038/s41467-021-23336-0
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author Li, Ximing
Rizik, Luna
Kravchik, Valeriia
Khoury, Maria
Korin, Netanel
Daniel, Ramez
author_facet Li, Ximing
Rizik, Luna
Kravchik, Valeriia
Khoury, Maria
Korin, Netanel
Daniel, Ramez
author_sort Li, Ximing
collection PubMed
description Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.
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spelling pubmed-81498572021-06-11 Synthetic neural-like computing in microbial consortia for pattern recognition Li, Ximing Rizik, Luna Kravchik, Valeriia Khoury, Maria Korin, Netanel Daniel, Ramez Nat Commun Article Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern. Nature Publishing Group UK 2021-05-25 /pmc/articles/PMC8149857/ /pubmed/34035266 http://dx.doi.org/10.1038/s41467-021-23336-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Li, Ximing
Rizik, Luna
Kravchik, Valeriia
Khoury, Maria
Korin, Netanel
Daniel, Ramez
Synthetic neural-like computing in microbial consortia for pattern recognition
title Synthetic neural-like computing in microbial consortia for pattern recognition
title_full Synthetic neural-like computing in microbial consortia for pattern recognition
title_fullStr Synthetic neural-like computing in microbial consortia for pattern recognition
title_full_unstemmed Synthetic neural-like computing in microbial consortia for pattern recognition
title_short Synthetic neural-like computing in microbial consortia for pattern recognition
title_sort synthetic neural-like computing in microbial consortia for pattern recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8149857/
https://www.ncbi.nlm.nih.gov/pubmed/34035266
http://dx.doi.org/10.1038/s41467-021-23336-0
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