ILP-based Synthesis for Sample Preparation Applications on Digital Microfluidic Biochips

Digital micro fluidic biochips have become one of the most promising technologies in many biomedical fields. In a digital micro fluidic biochip, a group of (adjacent) cells in the micro fluidic array can be configured to work as storage, functional operations, as well as for transporting fluid droplets dynamically. There has been proposed a very efficient Integer Linear Programming (ILP)-based method to schedule operations with considering this dynamic reconfigurability. However, the method cannot directly deal with a case having an operation with multiple successors, which is necessary in some bioassays, e.g., Sample preparation applications. Sample preparation is a crucial preprocessing step to produce droplets of the desired target concentrations from input reagents. In every optimized application graphs for sample preparation, there are many operations with more than one successors. Thus, the previous method cannot be used to get an optimal scheduling, and thus we propose a new framework to use another ILP formulation for scheduling operations in a digital micro fluidic biochips. Our formulation can successfully deal with a case where there is a node with multiple successors. Also, our method can find a binding solution at the same time unlike the above-mentioned previous one thanks to our new ILP formulation. Although our ILP formulation seems to be a bit more complex than the previous one, a preliminary experiment demonstrates that the proposed ILP formulation can successfully find the optimal scheduling for practical sample preparation applications within a reasonable time.