Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning

Tung-Che Liang, Zhanwei Zhong, Yaas Bigdeli, Tsung-Yi Ho, Krishnendu Chakrabarty, Richard Fair
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6050-6060, 2020.

Abstract

We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB, composed of a two-dimensional electrode array, manipulates discrete fluid droplets to automatically execute biochemical protocols such as point-of-care clinical diagnosis. However, a major concern associated with the use of DMFBs is that electrodes in a biochip can degrade over time. Droplet-transportation operations associated with the degraded electrodes can fail, thereby compromising the integrity of the bioassay outcome. We show that casting droplet transportation as an RL problem enables the training of deep network policies to capture the underlying health conditions of electrodes and to provide reliable fluidic operations. We propose a new RL-based droplet-routing flow that can be used for various sizes of DMFBs, and demonstrate reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. To facilitate further research, we also present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet-routing problems on DMFBs.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-liang20c, title = {Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning}, author = {Liang, Tung-Che and Zhong, Zhanwei and Bigdeli, Yaas and Ho, Tsung-Yi and Chakrabarty, Krishnendu and Fair, Richard}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {6050--6060}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/liang20c/liang20c.pdf}, url = {https://proceedings.mlr.press/v119/liang20c.html}, abstract = {We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB, composed of a two-dimensional electrode array, manipulates discrete fluid droplets to automatically execute biochemical protocols such as point-of-care clinical diagnosis. However, a major concern associated with the use of DMFBs is that electrodes in a biochip can degrade over time. Droplet-transportation operations associated with the degraded electrodes can fail, thereby compromising the integrity of the bioassay outcome. We show that casting droplet transportation as an RL problem enables the training of deep network policies to capture the underlying health conditions of electrodes and to provide reliable fluidic operations. We propose a new RL-based droplet-routing flow that can be used for various sizes of DMFBs, and demonstrate reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. To facilitate further research, we also present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet-routing problems on DMFBs.} }
Endnote
%0 Conference Paper %T Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning %A Tung-Che Liang %A Zhanwei Zhong %A Yaas Bigdeli %A Tsung-Yi Ho %A Krishnendu Chakrabarty %A Richard Fair %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-liang20c %I PMLR %P 6050--6060 %U https://proceedings.mlr.press/v119/liang20c.html %V 119 %X We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB, composed of a two-dimensional electrode array, manipulates discrete fluid droplets to automatically execute biochemical protocols such as point-of-care clinical diagnosis. However, a major concern associated with the use of DMFBs is that electrodes in a biochip can degrade over time. Droplet-transportation operations associated with the degraded electrodes can fail, thereby compromising the integrity of the bioassay outcome. We show that casting droplet transportation as an RL problem enables the training of deep network policies to capture the underlying health conditions of electrodes and to provide reliable fluidic operations. We propose a new RL-based droplet-routing flow that can be used for various sizes of DMFBs, and demonstrate reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. To facilitate further research, we also present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet-routing problems on DMFBs.
APA
Liang, T., Zhong, Z., Bigdeli, Y., Ho, T., Chakrabarty, K. & Fair, R.. (2020). Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:6050-6060 Available from https://proceedings.mlr.press/v119/liang20c.html.

Related Material