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Survival strategies of artificial active agents
Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. b...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079664/ https://www.ncbi.nlm.nih.gov/pubmed/37024516 http://dx.doi.org/10.1038/s41598-023-32267-3 |
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author | Zanovello, Luigi Löffler, Richard J. G. Caraglio, Michele Franosch, Thomas Hanczyc, Martin M. Faccioli, Pietro |
author_facet | Zanovello, Luigi Löffler, Richard J. G. Caraglio, Michele Franosch, Thomas Hanczyc, Martin M. Faccioli, Pietro |
author_sort | Zanovello, Luigi |
collection | PubMed |
description | Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. by moving towards regions with higher fuel concentration. In spite of the intense experimental activity aiming at investigating this fascinating behavior, a rigorous definition and characterization of such “survival strategies” from a statistical physics perspective is still missing. In this work, we take a first step in this direction by adapting and applying to active systems the theoretical framework of Transition Path Theory, which was originally introduced to investigate rare thermally activated transitions in passive systems. We perform experiments on camphor disks navigating Petri dishes and perform simulations in the paradigmatic active Brownian particle model to show how the notions of transition probability density and committor function provide the pivotal concepts to identify survival strategies, improve modeling, and obtain and validate experimentally testable predictions. The definition of survival in these artificial systems paves the way to move beyond simple observation and to formally characterize, design and predict complex life-like behaviors. |
format | Online Article Text |
id | pubmed-10079664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100796642023-04-08 Survival strategies of artificial active agents Zanovello, Luigi Löffler, Richard J. G. Caraglio, Michele Franosch, Thomas Hanczyc, Martin M. Faccioli, Pietro Sci Rep Article Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. by moving towards regions with higher fuel concentration. In spite of the intense experimental activity aiming at investigating this fascinating behavior, a rigorous definition and characterization of such “survival strategies” from a statistical physics perspective is still missing. In this work, we take a first step in this direction by adapting and applying to active systems the theoretical framework of Transition Path Theory, which was originally introduced to investigate rare thermally activated transitions in passive systems. We perform experiments on camphor disks navigating Petri dishes and perform simulations in the paradigmatic active Brownian particle model to show how the notions of transition probability density and committor function provide the pivotal concepts to identify survival strategies, improve modeling, and obtain and validate experimentally testable predictions. The definition of survival in these artificial systems paves the way to move beyond simple observation and to formally characterize, design and predict complex life-like behaviors. Nature Publishing Group UK 2023-04-06 /pmc/articles/PMC10079664/ /pubmed/37024516 http://dx.doi.org/10.1038/s41598-023-32267-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zanovello, Luigi Löffler, Richard J. G. Caraglio, Michele Franosch, Thomas Hanczyc, Martin M. Faccioli, Pietro Survival strategies of artificial active agents |
title | Survival strategies of artificial active agents |
title_full | Survival strategies of artificial active agents |
title_fullStr | Survival strategies of artificial active agents |
title_full_unstemmed | Survival strategies of artificial active agents |
title_short | Survival strategies of artificial active agents |
title_sort | survival strategies of artificial active agents |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079664/ https://www.ncbi.nlm.nih.gov/pubmed/37024516 http://dx.doi.org/10.1038/s41598-023-32267-3 |
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