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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.
Afiliação
  • Wang G; Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China.
  • Phan TV; Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520.
  • Li S; School of Physics, Georgia Institute of Technology, Atlanta, GA 30332.
  • Wang J; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China.
  • Peng Y; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China.
  • Chen G; Research Institute of USV Engineering, Shanghai University, Shanghai 200444, China.
  • Qu J; Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China.
  • Goldman DI; College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
  • Levin SA; School of Physics, Georgia Institute of Technology, Atlanta, GA 30332.
  • Pienta K; Department of Environmental and Evolutionary Biology, Princeton University, Princeton, NJ 08544.
  • Amend S; The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD 21287.
  • Austin RH; The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD 21287.
  • Liu L; Department of Physics, Princeton University, Princeton, NJ 08544.
Proc Natl Acad Sci U S A ; 119(12): e2120019119, 2022 03 22.
Article em En | MEDLINE | ID: mdl-35298335
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Robótica Limite: Animals Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Robótica Limite: Animals Idioma: En Revista: Proc Natl Acad Sci U S A Ano de publicação: 2022 Tipo de documento: Article