Concentration-Resilient Mixture Preparation with Digital Microfluidic Lab-on-Chip

Abstract

Sample preparation plays a crucial role in almost all biochemical applications, since a predominant portion of biochemical analysis time is associated with sample collection, transportation, and preparation. Many sample-preparation algorithms are proposed in the literature that are suitable for execution on programmable digital microfluidic (DMF) platforms. In most of the existing DMF-based sample-preparation algorithms, a fixed target ratio is provided as input, and the corresponding mixing tree is generated as output. However, in many biochemical applications, target mixtures with exact component proportions may not be needed. From a biochemical perspective, it may be sufficient to prepare a mixture in which the input reagents may lie within a range of concentration factors. The choice of a particular valid ratio, however, strongly impacts solution-preparation cost and time. To address this problem, we propose a concentration-resilient ratioselection method from the input ratio space so that the reactant cost is minimized. We propose an integer linear programming-based method that terminates very fast while producing the optimum solution, considering both uniform and weighted cost of reagents. Experimental results reveal that the proposed method can be used conveniently in tandem with several existing sample-preparation algorithms for improving their performance.