Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning
Tung-Che Liang, Jin Zhou, Yun-Sheng Chan, Tsung-Yi Ho, Krishnendu Chakrabarty, Cy Lee
Abstract
Microfluidic biochips are being utilized for clinical diagnostics, including COVID-19 testing, because of they provide sample-to-result turnaround at low cost. Recently, microelectrode-dot-array (MEDA) biochips have been proposed to advance microfluidics technology. A MEDA biochip manipulates droplets of nano/picoliter volumes to automatically execute biochemical protocols. During bioassay execution, droplets are transported in parallel to achieve high-throughput outcomes. However, a major concern associated with the use of MEDA biochips is microelectrode degradation over time. Recent work has shown that formulating droplet transportation as a reinforcement-learning (RL) problem enables the training of policies to capture the underlying health conditions of microelectrodes and ensure reliable fluidic operations. However, the above RL-based approach suffers from two key limitations: 1) it cannot be used for concurrent transportation of multiple droplets; 2) it requires the availability of CCD cameras for monitoring droplet movement. To overcome these problems, we present a multi-agent reinforcement learning (MARL) droplet-routing solution that can be used for various sizes of MEDA biochips with integrated sensors, and we demonstrate the reliable execution of a serial-dilution bioassay with the MARL droplet router on a fabricated MEDA biochip. To facilitate further research, we also present a simulation environment based on the PettingZoo Gym Interface for MARL-guided droplet-routing problems on MEDA biochips.
BibTeX
@InProceedings{pmlr-v139-liang21c,
title = {Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning},
author = {Liang, Tung-Che and Zhou, Jin and Chan, Yun-Sheng and Ho, Tsung-Yi and Chakrabarty, Krishnendu and Lee, Cy},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {6588--6599},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/liang21c/liang21c.pdf},
url = {https://proceedings.mlr.press/v139/liang21c.html},
abstract = {Microfluidic biochips are being utilized for clinical diagnostics, including COVID-19 testing, because of they provide sample-to-result turnaround at low cost. Recently, microelectrode-dot-array (MEDA) biochips have been proposed to advance microfluidics technology. A MEDA biochip manipulates droplets of nano/picoliter volumes to automatically execute biochemical protocols. During bioassay execution, droplets are transported in parallel to achieve high-throughput outcomes. However, a major concern associated with the use of MEDA biochips is microelectrode degradation over time. Recent work has shown that formulating droplet transportation as a reinforcement-learning (RL) problem enables the training of policies to capture the underlying health conditions of microelectrodes and ensure reliable fluidic operations. However, the above RL-based approach suffers from two key limitations: 1) it cannot be used for concurrent transportation of multiple droplets; 2) it requires the availability of CCD cameras for monitoring droplet movement. To overcome these problems, we present a multi-agent reinforcement learning (MARL) droplet-routing solution that can be used for various sizes of MEDA biochips with integrated sensors, and we demonstrate the reliable execution of a serial-dilution bioassay with the MARL droplet router on a fabricated MEDA biochip. To facilitate further research, we also present a simulation environment based on the PettingZoo Gym Interface for MARL-guided droplet-routing problems on MEDA biochips.}
}