Multi-Objective Sample Preparation Algorithm for Microfluidic Biochips Supporting Various Mixing Models

Sample preparation is one of the most crucial processes in most biochemical applications. Reagents are repeatedly diluted in an appropriate sequence to get a target solution with a specific concentration value. For flow-based microfluidic biochips (FMFBs), several research works have been proposed for reactant minimization. In this paper, we propose the first sample preparation algorithm for microfluidic biochips with various mixing models that can perform multi-objective optimization simultaneously. It first formulates the problem in a network-flow model and then solves it through integer linear programming (ILP). Experimental results show that the proposed method can provide better solutions (in terms of reactant, waste, and operation jointly) as compared with the prior art.