Lab-on-chip (LoC) technology has emerged as one of the major driving forces behind the recent surge in biochemical protocol automation. Dilution and mixture preparation with fluids in a desired ratio, constitute basic steps in sample preparation for which several LoC-based architectures and algorithms are known. The optimization of cost and time for such protocols requires proper sequencing of fluidic mix-and-split steps, and storage-units for holding intermediate-fluids to be reused in the later steps. However, practical design constraints often limit the amount of on-chip storage in microfluidic LoC architectures and thus can badly affect the performance of the algorithms. Consequently, results generated by previous work may not be useful (in the case they require more storage-units than available) or more expensive than necessary (in the case when storage-units are available but not used, e.g., to further reduce the number of mix/split operations or reactant-cost). In this paper, we propose new algorithms for dilution and mixing with continuous-flow-based LoCs that explicitly take care of storage constraints while optimizing reactant-cost and time of sample preparation. We present a symbolic formulation of the problem that captures the degree of freedom in algorithmic steps satisfying the specified storage constraints. Solvers based on Boolean satisfiability are used to achieve the optimization goals. The experimental results show the efficiency and effectiveness of the solution as well as a variety of applications where the proposed methods would prove beneficial.

中文翻译：

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使用基于流的芯片实验室进行稀释和混合物制备的存储感知算法

芯片实验室 ​​(LoC) 技术已成为近期生化协议自动化激增背后的主要驱动力之一。使用所需比例的流体进行稀释和混合制备，构成了样品制备的基本步骤，已知几种基于 LoC 的架构和算法。此类协议的成本和时间优化需要对流体混合和拆分步骤进行适当的排序，以及用于保存中间流体以在后续步骤中重复使用的存储单元。然而，实际的设计约束通常会限制微流体 LoC 架构中的片上存储量，从而严重影响算法的性能。最后，以前工作产生的结果可能没有用（如果它们需要比可用的更多的存储单元）或比必要的更昂贵（在存储单元可用但未使用的情况下，例如，为了进一步减少混合数量/拆分操作或反应物成本）。在本文中，我们提出了与基于连续流的 LoC 进行稀释和混合的新算法，该算法明确考虑了存储限制，同时优化了反应物成本和样品制备时间。我们提出了问题的符号公式，该公式捕获满足指定存储约束的算法步骤中的自由度。基于布尔可满足性的求解器用于实现优化目标。