A cellular platform for the development of synthetic living machines

Douglas Blackiston, Emma Lederer, Sam Kriegman, Simon Garnier, Joshua Bongard, Michael Levin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Robot swarms have, to date, been constructed from artificial materials. Motile biological constructs have been created from muscle cells grown on precisely shaped scaffolds. However, the exploitation of emergent self-organization and functional plasticity into a self-directed living machine has remained a major challenge. We report here a method for generation of in vitro biological robots from frog (Xenopus laevis) cells. These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects. The biological robots arise by cellular self-organization and do not require scaffolds or microprinting; the amphibian cells are highly amenable to surgical, genetic, chemical, and optical stimulation during the self-assembly process. We show that the xenobots can navigate aqueous environments in diverse ways, heal after damage, and show emergent group behaviors. We constructed a computational model to predict useful collective behaviors that can be elicited from a xenobot swarm. In addition, we provide proof of principle for a writable molecular memory using a photoconvertible protein that can record exposure to a specific wavelength of light. Together, these results introduce a platform that can be used to study many aspects of self-assembly, swarm behavior, and synthetic bioengineering, as well as provide versatile, soft-body living machines for numerous practical applications in biomedicine and the environment.

Original languageEnglish (US)
Article numbereabf1571
JournalScience Robotics
Volume6
Issue number52
DOIs
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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