於數位微流體生物晶片中共享稀釋操作之多反應物樣本製備技術

Abstract

樣本製備程序(sample preparation)為各種生化反應中不可或缺的步驟。多種原始生物樣本或反應試劑進行稀釋或混合，以達到反應所需的目標濃度(target concentration)。因為這些樣本或試劑在生化反應的成本上佔有重大的影響力，它們的用量應該盡可能的減少。本篇論文提出一個有效再利用混和反應生成物的演算法，簡稱CoDOS，來解決在數位微流體生物晶片(digital microfluidic biochips, DMFBs)上，多種原始生物樣本或反應試劑混和的樣本製備程序規劃(many-reactant sample preparation)的問題。CoDOS的主要概念為，將給定之目標濃度以一成分矩陣(recipe matrix)表示，然後在該成分矩陣上找出矩形(rectangle)的存在，這些矩形暗示著有可以減少樣本或試劑用量的機會。實驗結果顯示，與現行成效最好的多原始生物樣本或反應試劑混和的樣本製備程序演算法相比， CoDOS完成反應所需要的樣本或試劑用量最多可較其減少33%。而即使CoDOS並非專門為了多目標濃度樣本製備程序規劃(multi-target sample preparation)設計的演算法，當其用以執行多目標濃度樣本製備時，與現行最好演算法相比，CoDOS仍然有較為優秀的表現。所以可以確信對於多原始生物樣本或反應試劑混和的樣本製備程序規劃問題，CoDOS是值得考慮的方法，不論其目標濃度是單一或複數皆然。

Sample preparation is an essential processing step in most biochemical applications. In this step, various reactants are mixed together to produce a solution with the target concentration. Since the cost of reactants generally determines the cost of a bioassay, the reactant usage thus should be minimized whenever possible. In this paper, we propose a common dilution operation sharing algorithm, CoDOS, to prepare the target solution with many reactants using common dilution operation sharing on digital microfluidic biochips (DMFBs). CoDOS first represents the given target concentration as a recipe matrix, and then identifies rectangles, which indicates opportunities of dilution operation sharing, in the matrix for reactant minimization. Experimental results demonstrate that CoDOS can achieve up to 33% of reactant saving as compared with the bit-scanning method in single-target sample preparation. Moreover, even if CoDOS is not developed for multi-target sample preparation, it still outperforms the recent state-of-the-art algorithm, RSMA. Hence, it is convincing that CoDOS is a better alternative for many-reactant sample preparation.