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Computational capabilities of a multicellular reservoir computing system

The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However,...

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Autores principales: Nikolić, Vladimir, Echlin, Moriah, Aguilar, Boris, Shmulevich, Ilya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079015/
https://www.ncbi.nlm.nih.gov/pubmed/37023084
http://dx.doi.org/10.1371/journal.pone.0282122
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author Nikolić, Vladimir
Echlin, Moriah
Aguilar, Boris
Shmulevich, Ilya
author_facet Nikolić, Vladimir
Echlin, Moriah
Aguilar, Boris
Shmulevich, Ilya
author_sort Nikolić, Vladimir
collection PubMed
description The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions—computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems.
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spelling pubmed-100790152023-04-07 Computational capabilities of a multicellular reservoir computing system Nikolić, Vladimir Echlin, Moriah Aguilar, Boris Shmulevich, Ilya PLoS One Research Article The capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit. However, single cell engineering is limited by the necessary molecular complexity and the accompanying metabolic burden of synthetic circuits. To overcome these limitations, synthetic biologists have begun engineering multicellular systems that combine cells with designed subfunctions. To further advance information processing in synthetic multicellular systems, we introduce the application of reservoir computing. Reservoir computers (RCs) approximate a temporal signal processing task via a fixed-rule dynamic network (the reservoir) with a regression-based readout. Importantly, RCs eliminate the need of network rewiring, as different tasks can be approximated with the same reservoir. Previous work has already demonstrated the capacity of single cells, as well as populations of neurons, to act as reservoirs. In this work, we extend reservoir computing in multicellular populations with the widespread mechanism of diffusion-based cell-to-cell signaling. As a proof-of-concept, we simulated a reservoir made of a 3D community of cells communicating via diffusible molecules and used it to approximate a range of binary signal processing tasks, focusing on two benchmark functions—computing median and parity functions from binary input signals. We demonstrate that a diffusion-based multicellular reservoir is a feasible synthetic framework for performing complex temporal computing tasks that provides a computational advantage over single cell reservoirs. We also identified a number of biological properties that can affect the computational performance of these processing systems. Public Library of Science 2023-04-06 /pmc/articles/PMC10079015/ /pubmed/37023084 http://dx.doi.org/10.1371/journal.pone.0282122 Text en © 2023 Nikolić et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Nikolić, Vladimir
Echlin, Moriah
Aguilar, Boris
Shmulevich, Ilya
Computational capabilities of a multicellular reservoir computing system
title Computational capabilities of a multicellular reservoir computing system
title_full Computational capabilities of a multicellular reservoir computing system
title_fullStr Computational capabilities of a multicellular reservoir computing system
title_full_unstemmed Computational capabilities of a multicellular reservoir computing system
title_short Computational capabilities of a multicellular reservoir computing system
title_sort computational capabilities of a multicellular reservoir computing system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079015/
https://www.ncbi.nlm.nih.gov/pubmed/37023084
http://dx.doi.org/10.1371/journal.pone.0282122
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