Kavli Affiliate: Carlos Bustamante
| First 5 Authors: Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler
| Summary:
There is a trend in research towards more automation using smart systems
powered by artificial
intelligence. While experiments are often challenging to automate, they can
greatly benefit from
automation by reducing labor and increasing reproducibility. For example,
optical tweezers are
widely employed in single-molecule biophysics, cell biomechanics, and soft
matter physics, but they
still require a human operator, resulting in low throughput and limited
repeatability. Here, we
present a smart optical tweezers platform, which we name SmartTrap, capable
of performing complex
experiments completely autonomously. SmartTrap integrates real-time 3D
particle tracking using
deep learning, custom electronics for precise feedback control, and a
microfluidic setup for particle
handling. We demonstrate the ability of SmartTrap to operate continuously,
acquiring high-precision
data over extended periods of time, through a series of experiments. By
bridging the gap between
manual experimentation and autonomous operation, SmartTrap establishes a
robust and open source
framework for the next generation of optical tweezers research, capable of
performing large-scale
studies in single-molecule biophysics, cell mechanics, and colloidal science
with reduced experimental
overhead and operator bias.
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