Evidence map›Paper›PMID 35298335›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2022

Robots as models of evolving systems.

Gao Wang, Trung V Phan, Shengkai Li, Jing Wang, Yan Peng, Guo Chen, Junle Qu, Daniel I Goldman, Simon A Levin, Kenneth Pienta and 3 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Let the robotic games begin.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 8 institutions in 2 countries.

Gao WangChongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China.
Trung V PhanDepartment of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520.
Shengkai LiSchool of Physics, Georgia Institute of Technology, Atlanta, GA 30332.ORCID 0000-0002-3052-410X
Jing WangWenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China.
Yan PengResearch Institute of USV Engineering, Shanghai University, Shanghai 200444, China.
Guo ChenChongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China.ORCID 0000-0003-1524-4334
Junle QuCollege of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0001-7833-4711
Daniel I GoldmanSchool of Physics, Georgia Institute of Technology, Atlanta, GA 30332.ORCID 0000-0002-6954-9857
Simon A LevinDepartment of Environmental and Evolutionary Biology, Princeton University, Princeton, NJ 08544.ORCID 0000-0002-8216-5639
Kenneth PientaThe Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD 21287.ORCID 0000-0002-4138-2186
Sarah AmendThe Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD 21287.ORCID 0000-0002-5606-1262
Robert H AustinDepartment of Physics, Princeton University, Princeton, NJ 08544.ORCID 0000-0002-4269-6793
Liyu LiuChongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China.
Chongqing University · CNGeorgia Institute of Technology · USJohns Hopkins University · USPrinceton University · USShanghai University of Engineering Science · CNShenzhen University · CNYale University · USZhejiang Lab · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

RoboticsAnimalsMammalsModels, GeneticMutationPopulation DynamicsProbabilitySelection, Geneticadaptable landscapesevolutionrobotic biologystochastic dynamics

Identifiers

PMID35298335
PMCPMC8944266
OpenAlexW4221121717

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.