Reliability Analysis of Mixing Tree for Automated Sample preparation on Digital Microfluidic Biochips

Abstract

Sample Preparation is the most necessary step in biochemical applications. Various biochemical reactants are mixed together to produce mixture with target concentration. Many algorithms have been proposed for reactant minimization and to reduce tranportation time during sample preparation on DMFBs in the recent years. In recent years, it have been seen that there is a fault in mixture hardware on DMFBs. Due to fault on mixture, droplets are not mixed homogeneously during sample preparation on DMFBs. Due to non-homogeneous mixing different part of mixed droplet may contain different concentration of reactants. There are various algorithm developed for sample preparation of biochemical assay but none of them are aiming reliability purpose due to hardware fault. We implemented new technique Monte Carlo Simulation aiming reliability during sample preparation by using different existing algorithm on DMFBs . We are using Monte Carlo Simulation in Min-Mix[1], RMA[8] and MTCS[2] algorithm for sample preparation if there is an inhomogeneous mixing on hardware mixture. It uses mixing tree constructed by different algorithm and propagate error on each mixing node then calculate actual concentration due to error. We observed that MTCS gives better performance than Min-Mix and RMA because it uses common sub-tree, which creates a re-convergent fan out in the tree, due to which it reduces error. Furthermore, Monte Carlo Simulation technique can be implemented to other single target sample preparation as well as multi target sample preparation. So this technique is very useful to know which particular algorithm is more reliable for a specific ratio if there is an inhomogeneous mixing in the hardware mixture on DMFBs.