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Robots as models of evolving systems

Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state survivin...

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Autores principales: Wang, Gao, Phan, Trung V., Li, Shengkai, Wang, Jing, Peng, Yan, Chen, Guo, Qu, Junle, Goldman, Daniel I., Levin, Simon A., Pienta, Kenneth, Amend, Sarah, Austin, Robert H., Liu, Liyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944266/
https://www.ncbi.nlm.nih.gov/pubmed/35298335
http://dx.doi.org/10.1073/pnas.2120019119
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author Wang, Gao
Phan, Trung V.
Li, Shengkai
Wang, Jing
Peng, Yan
Chen, Guo
Qu, Junle
Goldman, Daniel I.
Levin, Simon A.
Pienta, Kenneth
Amend, Sarah
Austin, Robert H.
Liu, Liyu
author_facet Wang, Gao
Phan, Trung V.
Li, Shengkai
Wang, Jing
Peng, Yan
Chen, Guo
Qu, Junle
Goldman, Daniel I.
Levin, Simon A.
Pienta, Kenneth
Amend, Sarah
Austin, Robert H.
Liu, Liyu
author_sort Wang, Gao
collection PubMed
description Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive.
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spelling pubmed-89442662022-09-17 Robots as models of evolving systems Wang, Gao Phan, Trung V. Li, Shengkai Wang, Jing Peng, Yan Chen, Guo Qu, Junle Goldman, Daniel I. Levin, Simon A. Pienta, Kenneth Amend, Sarah Austin, Robert H. Liu, Liyu Proc Natl Acad Sci U S A Physical Sciences Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive. National Academy of Sciences 2022-03-17 2022-03-22 /pmc/articles/PMC8944266/ /pubmed/35298335 http://dx.doi.org/10.1073/pnas.2120019119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Wang, Gao
Phan, Trung V.
Li, Shengkai
Wang, Jing
Peng, Yan
Chen, Guo
Qu, Junle
Goldman, Daniel I.
Levin, Simon A.
Pienta, Kenneth
Amend, Sarah
Austin, Robert H.
Liu, Liyu
Robots as models of evolving systems
title Robots as models of evolving systems
title_full Robots as models of evolving systems
title_fullStr Robots as models of evolving systems
title_full_unstemmed Robots as models of evolving systems
title_short Robots as models of evolving systems
title_sort robots as models of evolving systems
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944266/
https://www.ncbi.nlm.nih.gov/pubmed/35298335
http://dx.doi.org/10.1073/pnas.2120019119
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