Concentration-Resilient Mixture Preparation with Digital Microfluidic Lab-on-Chip
Concentration-Resilient Mixture Preparation with Digital Microfluidic Lab-on-Chip
复制标题
DOI：
10.1145/3157094
复制
发表时间：
2018-01
期刊：
影响因子：
--
通讯作者：
Sukanta Bhattacharjee;Yi-Ling Chen;Juinn-Dar Huang;B. Bhattacharya
中科院分区：
文献类型：
--
作者：
Sukanta Bhattacharjee;Yi-Ling Chen;Juinn-Dar Huang;B. Bhattacharya
样品制备在几乎所有生化应用中都起着至关重要的作用，因为生化分析时间的主要部分与样本收集，运输和制剂有关。文献中提出了许多样本准备算法，适用于可编程数字微流体（DMF）平台执行。在大多数现有基于DMF的样品准备算法中，提供了固定的目标比例作为输入，并将相应的混合树作为输出生成。但是，在许多生化应用中，可能不需要具有精确成分比例的目标混合物。从生化的角度来看，可以准备一个混合物，其中输入试剂可能位于浓度因子范围内。但是，选择特定的有效比率会强烈影响解决方案预先准备成本和时间。为了解决这个问题，我们提出了一种来自输入比空间的浓度弹性比选择方法，以便将反应物成本降至最低。我们提出了一种基于整数线性编程的方法，该方法在产生最佳解决方案的同时非常快地终止，考虑试剂的均匀和加权成本。实验结果表明，该提出的方法可以与几种现有的样品预制作算法同时使用，以提高其性能。
Sample preparation plays a crucial role in almost all biochemical applications, since a predominant portion of biochemical analysis time is associated with sample collection, transportation, and preparation. Many sample-preparation algorithms are proposed in the literature that are suitable for execution on programmable digital microfluidic (DMF) platforms. In most of the existing DMF-based sample-preparation algorithms, a fixed target ratio is provided as input, and the corresponding mixing tree is generated as output. However, in many biochemical applications, target mixtures with exact component proportions may not be needed. From a biochemical perspective, it may be sufficient to prepare a mixture in which the input reagents may lie within a range of concentration factors. The choice of a particular valid ratio, however, strongly impacts solution-preparation cost and time. To address this problem, we propose a concentration-resilient ratio-selection method from the input ratio space so that the reactant cost is minimized. We propose an integer linear programming--based method that terminates very fast while producing the optimum solution, considering both uniform and weighted cost of reagents. Experimental results reveal that the proposed method can be used conveniently in tandem with several existing sample-preparation algorithms for improving their performance.