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A modular approach to the design, fabrication, and characterization of muscle-powered biological machines


Biological machines consisting of cells and biomaterials have the potential to dynamically sense, process, respond, and adapt to environmental signals in real time. As a first step toward the realization of such machines, which will require biological actuators that can generate force and perform mechanical work, we have developed a method of manufacturing modular skeletal muscle actuators that can generate up to 1.7 mN (3.2 kPa) of passive tension force and 300 μN (0.56 kPa) of active tension force in response to external stimulation. Such millimeter-scale biological actuators can be coupled to a wide variety of 3D-printed skeletons to power complex output behaviors such as controllable locomotion. This article provides a comprehensive protocol for forward engineering of biological actuators and 3D-printed skeletons for any design application. 3D printing of the injection molds and skeletons requires 3 h, seeding the muscle actuators takes 2 h, and differentiating the muscle takes 7 d.

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Figure 1: Bio-bot design process overview.
Figure 2: Cellular orientation and morphology in muscle rings versus strips.
Figure 3: Muscle differentiation protocol.
Figure 4: Calculation and finite element analysis (FEA) verification of passive and active tension force production.
Figure 5: Immunohistochemistry of engineered muscle tissues.
Figure 6: Muscle-ring force production.

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We thank J. Sinn-Hanlon at the University of Illinois at Urbana-Champaign (UIUC) for image rendering of Figure 1a, and the Core Facilities at the Carl R. Woese Institute for Genomic Biology at the University of Illinois at Urbana-Champaign for assistance with sample preparation and imaging. We also thank V. Chan, P. Bajaj, S. Uzel, P. Sengupta, T. Saif, and R. Kamm for insightful discussions regarding this work. This work was funded by National Science Foundation (NSF) Science and Technology Center Emergent Behavior of Integrated Cellular Systems (EBICS) Grant CBET – 0939511. R.R. was funded by NSF Graduate Research Fellowship Grant DGE – 1144245. R.R. and C.C. were funded by an NSF Cellular and Molecular Mechanics and Bionanotechnology (CMMB) Integrative Graduate Education and Research Traineeship (IGERT) at UIUC (grant 0965918).

Author information

Authors and Affiliations



R.R. designed the modular muscle-ring protocol; R.R. performed and analyzed muscle seeding and functional assessment experiments; C.C. performed and analyzed muscle staining experiments and protein quantification experiments; R.R., C.C., and R.B. wrote the manuscript.

Corresponding author

Correspondence to Rashid Bashir.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Cellular alignment and circularity analysis

Starting with a fluorescent image of cellular nuclei, convert the image to binary. To calculate circularity, apply a threshold to the binary image and use the Analyze Particles feature in ImageJ to bin and plot the circularity (1 = circular; 0 = linear) for each data set. To compute alignment, perform FFT analysis on similarly-oriented images and plot the results (radial sums) as a function of degrees. (See Supplementary Methods for more information.) Circularity plots represent all data points from Figure 2c (n=2312 total nuclei for muscle rings; n=2702 for muscle strips). Data from normal distributions represent mean values ± standard deviations; * = p < 0.05. FFT Alignment plots represent individual curves for muscle ring and strip samples; averaged data are shown in bold black lines on each plot (and plotted together for comparison in Figure 2b).

Supplementary Figure 2 External stimulation of muscle rings

(A) Stimulation setup for optical pulse stimulation of bio-bots. (B) Representative optical pulse train signal. (C) Stimulation setup for electrical pulse stimulation of bio-bots. (D) Representative electrical biphasic pulse signal.

Supplementary Figure 3 Muscle ring exercise training regimen

Protocol for stimulating bio-bots using a static mechanical stimulus (imposed by tethering the bio-bot to an underlying glass coverslip) starting Day 1, immediately after ring transfer, and a dynamic optical stimulus (imposed by the apparatus shown in Supplementary Figure 1 starting Day 4, after transferring the bio-bots to differentiation medium.

Supplementary Figure 4 Modulus as a Function of Energy Dose

Plot of Young’s Modulus for PEGDA 700 g mol-1 as a function of the UV energy dose imposed by the laser of the SLA during fabrication.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Methods 1–3. (PDF 879 kb)

Supplementary Data

CAD files of muscle ring and muscle strip injection molds and symmetric and asymmetric one-ring, two-ring, and four-ring bio-bot skeletons; and FEA template file of one-ring bio-bot skeleton for computational modeling. (ZIP 673 kb)

A modular approach to designing, fabricating, and controlling muscle-powered machines.

Overview of major steps in protocol, including CAD design, 3D printing, muscle seeding, muscle differentiation, and bio-bot functional assessment. (MP4 28823 kb)

Electrical stimulation of unconstrained muscle ring.

Electrical stimulation (1 Hz) controls contraction in a muscle ring that is untethered to a bio-bot skeleton. (MP4 2968 kb)

Directional locomotion in a bio-bot.

Optical stimulation (4 Hz) drives directional locomotion of a one-leg asymmetric bio-bot in the direction of the longer pillar. (MOV 20777 kb)

Rotational locomotion in a bio-bot.

Optical stimulation (2 Hz) of one half of one muscle ring in a two-leg symmetric bio-bot drives rotational locomotion. (MOV 29692 kb)

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Raman, R., Cvetkovic, C. & Bashir, R. A modular approach to the design, fabrication, and characterization of muscle-powered biological machines. Nat Protoc 12, 519–533 (2017).

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