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SIMOP-A fast tool for generating optimum dilutions of microfluidic samples using simulation

机译:SIMOP-A快速工具,可通过仿真生成最佳的微流体样品稀释液

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摘要

The technology of digital microfluidic (DMF) biochips now offers viable replacement of expensive healthcare and bio-, chemical laboratory procedures with low-cost, fully-automated, miniaturized integrated systems. Preparing dilution of a fluid sample that optimizes various parameters such as reagent-cost, mixing time, waste production, is a basic problem in the domain of algorithmic microfluidics. Most of the existing dilution algorithms used in droplet-based microfluidic systems deploy a sequence of (1 : 1) mix-split steps, where two unit-volume droplets of different concentrations are mixed, followed by a balanced split operation to obtain two equal-sized droplets. In this paper, we introduce a simulation-guided optimization procedure (SIMOP) for achieving the target concentrations with a sequence of (1 : 1) mix-split steps while optimizing multiple factors according to user-specified priority levels. The SIMOP-algorithm produces a given concentration while optimizing each criterion as desired. Experimental results favorably demonstrate the performance of the proposed method compared to BS and DMRW algorithms. The proposed procedure may find many potential applications to microfluidics such as in' biomedical engineering and healthcare services.
机译:数字微流控(DMF)生物芯片技术现在可以用低成本,全自动,小型化的集成系统来替代昂贵的医疗保健和生物,化学实验室程序。在算法微流控技术领域,准备优化样本的稀释液以优化各种参数(例如试剂成本,混合时间,废物产生)是一个基本问题。在基于液滴的微流体系统中使用的大多数现有稀释算法都采用一系列(1:1)混合-分离步骤,其中将两个不同浓度的单位体积的液滴混合,然后进行平衡的分离操作以获得两个等分-大小的液滴。在本文中,我们介绍了一种模拟指导的优化程序(SIMOP),该过程可通过一系列(1:1)混合拆分步骤实现目标浓度,同时根据用户指定的优先级对多个因素进行优化。 SIMOP算法产生给定的浓度,同时根据需要优化每个标准。实验结果与BS和DMRW算法相比,证明了该方法的性能。所提出的程序可能会发现微流体的许多潜在应用,例如生物医学工程和医疗保健服务。

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