Reactant Minimization in Sample Preparation on Digital Microfluidic Biochips

Abstract

Sample preparation plays an essential role in most biochemical reactions. Raw reactants are diluted to solutions with desirable concentration values in this process. Since the reactants, like infant\'s blood, DNA evidence collected from crime scenes, or costly reagents, are extremely valuable, their usage should be minimized whenever possible. In this paper, we propose a two-phased reactant minimization algorithm (REMIA), for sample preparation on digital microfluidic biochips. In the former phase, REMIA builds a reactant-minimized interpolated dilution tree with specific leaf nodes for a target concentration. Two approaches are developed for tree construction; one is based on integer linear programming (ILP) and the other is heuristic. The ILP one guarantees to produce an optimal dilution tree with minimal reactant consumption, whereas the heuristic one ensures runtime efficiency. Then, REMIA constructs a forest consisting of exponential dilution trees to produce those aforementioned specific leaf nodes with minimal reactant consumption in the latter phase. Experimental results show that REMIA achieves a reduction of reactant usage by 32%-52% as compared with three existing state-of-the-art sample preparation approaches. Besides, REMIA can be easily extended to solve the sample preparation problem with multiple target concentrations, and the extended version also effectively lowers the reactant consumption further.