Reactant Minimization for Sample Preparation on Microfluidic Biochips With Various Mixing Models

TLDR: This paper proposes the first sample preparation method dedicated to microfluidic biochips with various mixing models, named tree pruning and grafting (TPG) algorithm, which starts with a dilution tree created by regarding the (1:1) mixing model only, and applies TPG through a bottom-up dynamic programming strategy to obtain a solution with minimal reactant consumption.

Abstract: Sample preparation is one of the 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 paper, we propose the first sample preparation method dedicated to microfluidic biochips with various mixing models, named tree pruning and grafting (TPG) algorithm. It starts with a dilution tree created by regarding the (1:1) mixing model only, and then applies TPG through a bottom-up dynamic programming strategy to obtain a solution with minimal reactant consumption. Experimental results show that our algorithm can save reactant amount by up to 69% against the well-known bit-scanning method on a biochip with a four-segment mixer. Even compared with the state-of-the-art reactant minimization algorithm, it still achieves a reactant reduction of 37%. Therefore, it is convincing that the TPG algorithm is a promising sample preparation solution for biochip architectures that support various mixing models.