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Lookup Table-Based Fast Reliability-Aware Sample Preparation Using Digital Microfluidic Biochips

机译:查找基于表的快速可靠性 - 使用数字微流体生物芯片的样品准备

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摘要

Reliability of the prepared fluidic samples is a major concern for automated sample preparation using microfluidic biochips, where induced errors in the resultant concentration values severely affect the assay outcome. However, the existing design automation techniques have not thoroughly considered the reliability model to reduce the induced concentration errors during sample preparation. This article proposes a fast reliability-aware sample preparation (RASP) method for determining the optimized sequence of mixing steps (mixing process) with the enhanced reliability. In RASP, a probabilistic concentration prediction model is proposed for analyzing the reliability of a given mixing process. Based on this probabilistic model, a lookup table construction algorithm along with the table query method is proposed to obtain the optimized mixing process. The simulation results show that for any user-specified target concentration, RASP can effectively determine the optimized mixing process, which generates the droplets with target concentration within the error tolerance of 0.1%. Compared with the state-of-the-art sample preparation algorithm, RASP improves the reliability-related accuracy by 91.4% on average via 2048 testcases.
机译:制备的流体样品的可靠性是使用微流体生物芯片自动样品制备的主要关注,其中所得浓度值的诱导误差严重影响测定结果。然而,现有的设计自动化技术尚未彻底地认为可靠性模型以减少样品制备期间诱导浓度误差。本文提出了一种快速可靠性感知的样品制备(RASP)方法,用于确定具有增强的可靠性的混合步骤(混合过程)的优化序列。在RASP中,提出了一种用于分析给定混合过程的可靠性的概率浓度预测模型。基于该概率模型,提出了一种查找表构造算法以及表查询方法,以获得优化的混合过程。仿真结果表明,对于任何用户指定的目标浓度,RASP可以有效地确定优化的混合过程,其在0.1%的误差容差内产生具有目标浓度的液滴。与最先进的样品制备算法相比,RASP平均通过2048试验酶将可靠性相关的精度提高了91.4%。

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