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
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:6588-6599, 2021.

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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning %A Tung-Che Liang %A Jin Zhou %A Yun-Sheng Chan %A Tsung-Yi Ho %A Krishnendu Chakrabarty %A Cy Lee %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-liang21c %I PMLR %P 6588--6599 %U https://proceedings.mlr.press/v139/liang21c.html %V 139 %X 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.
APA
Liang, T., Zhou, J., Chan, Y., Ho, T., Chakrabarty, K. & Lee, C.. (2021). Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6588-6599 Available from https://proceedings.mlr.press/v139/liang21c.html.

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