Cellular plasticity model for self-organized phenotypes in multi-cellular robots Article Swipe
Trevor Smith
,
Thomas G. Smith
,
Nicholas S. Szczecinski
,
Sergiy Yakovenko
,
Yu Gu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s44182-025-00039-y
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s44182-025-00039-y
Robotic systems often struggle to adapt to dynamic, unstructured environments due to top-down design constraints based on human assumptions. Inspired by biological morphogenesis, this study introduces a cellular plasticity model based on Turing patterns, enabling multi-cellular robots to self-organize their cell phenotypes in response to environmental stimuli. The model leverages reaction-diffusion dynamics to capture key cellular plasticity phenomena observed in muscle cells, neurons, and stem cells. Analytical analysis explores equilibrium points, stability, and conditions for emergent Turing patterns, while simulations examine parametric influences on system behavior. Physical experiments with the Loopy platform demonstrate that its cells dynamically self-organize mechanical properties in response to behavioral and environmental demands. This response enables Loopy to achieve similar performance to empirically optimized static parameters in obstacle-free environments and outperform the static configuration in an environment with limited space. This work advances morphogenetic robotics, presenting a scalable framework for decentralized, dynamic adaptation in unmodeled environments.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s44182-025-00039-y
- https://www.nature.com/articles/s44182-025-00039-y.pdf
- OA Status
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- References
- 40
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4412949097Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s44182-025-00039-yDigital Object Identifier
- Title
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Cellular plasticity model for self-organized phenotypes in multi-cellular robotsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-05Full publication date if available
- Authors
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Trevor Smith, Thomas G. Smith, Nicholas S. Szczecinski, Sergiy Yakovenko, Yu GuList of authors in order
- Landing page
-
https://doi.org/10.1038/s44182-025-00039-yPublisher landing page
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https://www.nature.com/articles/s44182-025-00039-y.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://www.nature.com/articles/s44182-025-00039-y.pdfDirect OA link when available
- Concepts
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Cellular automaton, Phenotype, Cellular model, Phenotypic plasticity, Computer science, Biology, Artificial intelligence, Genetics, Gene, Cell cultureTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.model | 30, 49 |
| abstract_inverted_index.often | 3 |
| abstract_inverted_index.study | 25 |
| abstract_inverted_index.their | 40 |
| abstract_inverted_index.while | 79 |
| abstract_inverted_index.Turing | 33, 77 |
| abstract_inverted_index.cells, | 62 |
| abstract_inverted_index.cells. | 66 |
| abstract_inverted_index.design | 14 |
| abstract_inverted_index.muscle | 61 |
| abstract_inverted_index.robots | 37 |
| abstract_inverted_index.space. | 134 |
| abstract_inverted_index.static | 119, 127 |
| abstract_inverted_index.system | 85 |
| abstract_inverted_index.Robotic | 1 |
| abstract_inverted_index.achieve | 113 |
| abstract_inverted_index.capture | 54 |
| abstract_inverted_index.dynamic | 146 |
| abstract_inverted_index.enables | 110 |
| abstract_inverted_index.examine | 81 |
| abstract_inverted_index.limited | 133 |
| abstract_inverted_index.points, | 71 |
| abstract_inverted_index.similar | 114 |
| abstract_inverted_index.systems | 2 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Inspired | 20 |
| abstract_inverted_index.Physical | 87 |
| abstract_inverted_index.advances | 137 |
| abstract_inverted_index.analysis | 68 |
| abstract_inverted_index.cellular | 28, 56 |
| abstract_inverted_index.demands. | 107 |
| abstract_inverted_index.dynamic, | 8 |
| abstract_inverted_index.dynamics | 52 |
| abstract_inverted_index.emergent | 76 |
| abstract_inverted_index.enabling | 35 |
| abstract_inverted_index.explores | 69 |
| abstract_inverted_index.neurons, | 63 |
| abstract_inverted_index.observed | 59 |
| abstract_inverted_index.platform | 92 |
| abstract_inverted_index.response | 44, 102, 109 |
| abstract_inverted_index.scalable | 142 |
| abstract_inverted_index.stimuli. | 47 |
| abstract_inverted_index.struggle | 4 |
| abstract_inverted_index.top-down | 13 |
| abstract_inverted_index.behavior. | 86 |
| abstract_inverted_index.framework | 143 |
| abstract_inverted_index.leverages | 50 |
| abstract_inverted_index.optimized | 118 |
| abstract_inverted_index.patterns, | 34, 78 |
| abstract_inverted_index.phenomena | 58 |
| abstract_inverted_index.robotics, | 139 |
| abstract_inverted_index.unmodeled | 149 |
| abstract_inverted_index.Analytical | 67 |
| abstract_inverted_index.adaptation | 147 |
| abstract_inverted_index.behavioral | 104 |
| abstract_inverted_index.biological | 22 |
| abstract_inverted_index.conditions | 74 |
| abstract_inverted_index.influences | 83 |
| abstract_inverted_index.introduces | 26 |
| abstract_inverted_index.mechanical | 99 |
| abstract_inverted_index.outperform | 125 |
| abstract_inverted_index.parameters | 120 |
| abstract_inverted_index.parametric | 82 |
| abstract_inverted_index.phenotypes | 42 |
| abstract_inverted_index.plasticity | 29, 57 |
| abstract_inverted_index.presenting | 140 |
| abstract_inverted_index.properties | 100 |
| abstract_inverted_index.stability, | 72 |
| abstract_inverted_index.constraints | 15 |
| abstract_inverted_index.demonstrate | 93 |
| abstract_inverted_index.dynamically | 97 |
| abstract_inverted_index.empirically | 117 |
| abstract_inverted_index.environment | 131 |
| abstract_inverted_index.equilibrium | 70 |
| abstract_inverted_index.experiments | 88 |
| abstract_inverted_index.performance | 115 |
| abstract_inverted_index.simulations | 80 |
| abstract_inverted_index.assumptions. | 19 |
| abstract_inverted_index.environments | 10, 123 |
| abstract_inverted_index.unstructured | 9 |
| abstract_inverted_index.configuration | 128 |
| abstract_inverted_index.environmental | 46, 106 |
| abstract_inverted_index.environments. | 150 |
| abstract_inverted_index.morphogenetic | 138 |
| abstract_inverted_index.obstacle-free | 122 |
| abstract_inverted_index.self-organize | 39, 98 |
| abstract_inverted_index.decentralized, | 145 |
| abstract_inverted_index.morphogenesis, | 23 |
| abstract_inverted_index.multi-cellular | 36 |
| abstract_inverted_index.reaction-diffusion | 51 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100659056 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I12097938 |
| citation_normalized_percentile.value | 0.20542377 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |