A cellular platform for the development of synthetic living machines.

Journal: Science robotics
PMID:

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 () 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.

Authors

  • Douglas Blackiston
    Allen Discovery Center at Tufts University, Medford, MA 02155, USA.
  • Emma Lederer
    Allen Discovery Center at Tufts University, Medford, MA 02155, USA.
  • Sam Kriegman
    Department of Computer Science, University of Vermont, E428 Innovation Hall, Burlington, VT, 05405, USA.
  • Simon Garnier
    Federated Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.
  • Joshua Bongard
    Department of Computer Science, University of Vermont, Burlington, VT 05405, USA.
  • Michael Levin
    Department of Biology, Allen Discovery Center at Tufts University, Tufts University, 200 Boston Ave. Suite 4604, Medford, MA, 02155, USA.