Abstract

Complex biological systems sense, process, and respond to their surroundings in real time. The ability of such systems to adapt their behavioral response to suit a range of dynamic environmental signals motivates the use of biological materials for other engineering applications. As a step toward forward engineering biological machines (bio-bots) capable of nonnatural functional behaviors, we created a modular light-controlled skeletal musclepowered bioactuator that can generate up to 300 μN (0.56 kPa) of active tension force in response to a noninvasive optical stimulus. When coupled to a 3D printed flexible bio-bot skeleton, these actuators drive directional locomotion (310 μm/s or 1.3 body lengths/min) and 2D rotational steering (2°/s) in a precisely targeted and controllable manner. The muscle actuators dynamically adapt to their surroundings by adjusting performance in response to "exercise" training stimuli. This demonstration sets the stage for developing multicellular bio-integrated machines and systems for a range of applications.

Original languageEnglish (US)
Pages (from-to)3497-3502
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number13
DOIs
StatePublished - Mar 29 2016

Fingerprint

Optogenetics
Bioengineering
Locomotion
Skeleton
Skeletal Muscle
Exercise
Light
Muscles

Keywords

  • Bioactuator
  • Soft robotics
  • Stereolithography
  • Tissue engineering

ASJC Scopus subject areas

  • General

Cite this

Optogenetic skeletal muscle-powered adaptive biological machines. / Raman, Ritu; Cvetkovic, Caroline; Uzel, Sebastien G.M.; Platt, Randall J.; Sengupta, Parijat; Kamm, Roger D.; Bashir, Rashid.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 113, No. 13, 29.03.2016, p. 3497-3502.

Research output: Contribution to journalArticle

Raman, Ritu ; Cvetkovic, Caroline ; Uzel, Sebastien G.M. ; Platt, Randall J. ; Sengupta, Parijat ; Kamm, Roger D. ; Bashir, Rashid. / Optogenetic skeletal muscle-powered adaptive biological machines. In: Proceedings of the National Academy of Sciences of the United States of America. 2016 ; Vol. 113, No. 13. pp. 3497-3502.
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