應用多種混合模型於微流體生物晶片以達到反應物最小化之樣本製備程序

Abstract

樣本製備程序(sample preparation)為各種生化反應中不可或缺的步驟。原始生物樣本或反應試劑必須在此程序中進行稀釋或混合，以達到反應所需的目標濃度(target concentration)。現有的樣本製備程序演算法皆是為數位微流體生物晶片所發展而成，因此僅使用(1:1)之混合模型。但至今，可同時支援多種混合模型的通道式微流體生物晶片的樣本製備程序演算法卻仍未被發展出來。因此，本篇論文將提出全球第一篇使用多種混合模型之樣本製備程序演算法，簡稱TPG演算法。TPG以一棵由(1:1)混合模型所建立的樹做為起始解，接著藉由樹的剪枝及透過由下而上之動態規劃(dynamic programming)進行樹的嫁接，最後再藉由共享葉節點使樣本使用量最小化。實驗結果顯示，當使用4段混合器在生物晶片上進行樣本製備程序時，與現今最為人所知的位元掃描演算法(bit-scanning)相比，TPG的樣本使用量較其減少了69%，而就算是與目前最先進的演算法(REMIA)相比，TPG仍減少了37%的樣本使用量。因此，在使用的生物晶片可支援多種混合模型時，TPG是一個較佳的樣本製備程序之演算法。

Sample preparation is an essential processes for most on-chip biochemical applications. During this process, raw reactants are diluted to specific concentration values. Current sample preparation algorithms are generally created for digital microfluidic biochips with the (1:1) mixing model. For other biochip architectures supporting multiple mixing models, such as flow-based microfluidic biochips, there is still no dedicated solution yet. Hence, in this thesis, we propose the first sample preparation method dedicated to microfluidic biochips with various mixing models, named TPG algorithm. TPG starts with a dilution tree created by regarding the (1:1) mixing model only, and then applies tree pruning, tree grafting through a bottom-up dynamic programming strategy, and GCV sharing to obtain a solution with minimal reactant consumption. Experimental results show that TPG can save reactant amount by up to 69% against the well-known bit-scanning method on a biochip with a 4-segment mixer. Even compared to the state-of-the-art algorithm REMIA, TPG still achieves a reactant reduction of 37%. Therefore, it is convincing that TPG is a promising sample preparation solution for biochip architectures that support various mixing models.