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Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning

机译:使用深度加强学习的数字微流体生物芯片中的自适应液滴路由

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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 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.
机译:我们展示并调查了深度加强学习(RL)的新应用领域:数字微流体生物芯片(DMFB)上的液滴路由。由二维电极阵列组成的DMFB操纵离散流体液滴以自动执行生物化学协议,例如护理点临床诊断。然而,与使用DMFB相关的主要问题是生物芯片中的电极会随着时间的推移而降低。与降解电极相关的液滴运输操作可能会失效,从而损害生物测定结果的完整性。我们表明,作为RL问题的铸造液滴运输使得能够培训深网络政策,以捕获电极的底层健康状况并提供可靠的流体操作。我们提出了一种新的基于RL的液滴 - 路由流动,可用于各种尺寸的DMFB,并证明在制造的DMFB上用RL液滴路由器的表观遗传生物测定的可靠执行。为了促进进一步的研究,我们还基于Openai健身房界面的仿真环境,用于DMFBS上的RL-Buidoving Drop-Routing问题。

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