Machine learning transforms mini biohybrid ray design, doubling swimming efficiency

A new study shows an application of machine-learning directed optimization (ML-DO) that efficiently searches for high-performance design configurations in the context of biohybrid robots. Applying a machine learning approach, the researchers created mini biohybrid rays made of cardiomyocytes (heart muscle cells) and rubber with a wingspan of about 10 mm that are approximately two times more efficient at swimming than those recently developed under a conventional biomimetic approach.

A team led by Harvard SEAS Postdoctoral Fellow John Zimmerman and including NTT Research Medical and Health Informatics Scientist Ryoma Ishii, Harvard SEAS Tarr Family Professor of Bioengineering and Applied Physics Kevin Kit Parker, and members of the Harvard SEAS Disease Biophysics Group led by Parker demonstrated this research in a new paper published in Science Robotics titled, “Bioinspired Design of a Tissue Engineered Ray with Machine Learning.”

“This research seeks to answer a fundamental question in the development of biohybrid robots, in this case the marine ray: How do we select fin geometries to operate under novel working environments while preserving natural scaling laws in terms of swimming speed and efficiency,” said Ishii, who also works as a visiting scientist for Harvard University.

“Our research indicates the application of ML-DO, inspired by protein engineering, offers a more efficient and less computationally intensive path forward in automating the creation of muscular structure-function relationships.”

Limitations of the biomimetic approach

In biomimetic design, the conventional approach to biohybrids, engineers form functional devices by recreating existing biological structures. That approach, however, has limits. For biohybrid lifeforms that resemble batoid fishes (skates and rays), for example, there is a wide range of natural aspect ratios and fin morphologies. Which ones do you mimic?

Also, biomimetics may neglect the natural biomechanical and hydrodynamic forces that govern how fast an organism can swim based on its size and body kinematics, leading to inefficient muscle mass and limited swimming speeds.

In that light, the motivating question in this study became: How to select fin geometries that operate under novel working environments while preserving natural scaling laws in terms of swimming speed and efficiency?

The design breakthroughs of machine learning

The multi-disciplinary and iterative nature of the problem required computationally intensive modeling, but the team believed that directed optimization by machine learning (ML-DO) would enable an efficient search for fin designs that maximized their relative swimming speeds.

They based their hypothesis in part on a trial function that demonstrated an approximately 40 percent improvement of ML-DO over other leading methods in recognizing known high-rank sequences. Testing the assumption involved three steps: 1) developing an algorithm for expressing a multitude of different fin geometries; 2) describing a generalized ML-DO approach for searching within a large discontinuous configuration space; and 3) using this methodology to identify biohybrid fin geometries for high-performance swimming with smooth and orderly flow.

The ML-DO-driven results included a quantitative exploration of fin structure-function relationships and reconstruction of general trends in open-sea batoid morphology, as well as a winning design: Fins with large aspect ratios and fine tapered tips, which preserved their utility across multiple length-scales of swimming.

On that basis, the team built biohybrid mini-rays out of engineered cardiac muscle tissue, which were capable of self-propelled swimming at the millimeter length scale and demonstrated improved swimming efficiencies approximately two times greater than observed in previous biomimetic designs.

Looking ahead

While promising, researchers note that additional work is needed to completely match natural scaling laws. While the devices presented in this study demonstrated greater efficiency than other recent biomimetic designs, they were still slightly less efficient on average than naturally occurring marine lifeforms.

In the future, researchers expect to continue the development of biohybrid robotics for use cases, including remote sensors, probes for dangerous working environments and as therapeutic delivery vehicles. Researchers believe that the ML-DO-informed approach better mimics the selective pressures of evolution, enabling them to better understand how biological tissues are shaped—both in a healthy physiology as well as the maladaptive pathophysiology of disease. Additionally, this research advances scientific understanding of 3D organ biofabrication, such as a biohybrid heart.