Reactant Minimization for Multi-Target Sample Preparation on Digital Microfluidic Biochips Using Network Flow Models

Sample preparation is one of those fundamental processes in biochemical reactions. In order to obtain target concentrations properly, raw reactants are processed through a series of dilution operations. Since some rare reactants, such as infant blood or DNA evidence from a crime scene, are extremely difficult to acquire, it is important to minimize their consumption and waste during sample preparation. In this paper, we propose a multitarget sample preparation algorithm for reactant minimization on digital microfluidic biochips. Given a set of target concentrations, the proposed method first converts the reactant minimization problem into a network flow model, and then solves it through integer linear programming (ILP) accordingly. Experimental results demonstrate that our new algorithm can reduce the reactant consumption by up to 31% as compared with the current state-of-the-art.