IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Droplet-based microfluidics is a colloidal and interfacial system that has rapidly progressed in the past decade because of the advantages of low fabrication costs, small sample volumes, reduced analysis durations, high-throughput analysis with exceptional sensitivity, enhanced operational flexibility, and facile automation. This technology has emerged as a new tool for many recently used applications in molecular detection, imaging, drug delivery, diagnostics, cell biology and other fields. Herein, we review recent applications of droplet microfluidics proposed since 2013.

Recent Advances in Applications of Droplet Microfluidics

Sample preparation is one of essential processes in biochemical reactions. Raw reactants are diluted in this process to achieve given target concentrations. A bioassay may require several different target concentrations of a reactant. Both the dilution operation count and the reactant usage can be minimized if multiple target concentrations are considered simultaneously during sample preparation. Hence, in this paper, we propose a multitarget sample preparation algorithm that extensively exploits the ideas of waste recycling and intermediate droplet sharing to reduce both reactant usage and waste amount for digital microfluidic biochips. Experimental results show that our waste recycling algorithm can reduce the waste and operation count by 48% and 37%, respectively, as compared to an existing state-of-the-art multitarget sample preparation method if the number of target concentrations is ten. The reduction can be up to 97% and 73% when the number of target concentrations goes even higher.

Reactant and Waste Minimization in Multitarget Sample Preparation on Digital Microfluidic Biochips

An open-source compiler and PCB synthesis tool for digital microfluidic biochips

Sample preparation, which is a front-end process to produce droplets of the desired target concentrations from input reagents, plays a pivotal role in every assay, laboratory, and application in biomedical engineering and life science. The consumption of sample/buffer/waste is usually used to evaluate the effectiveness of a sample preparation process. In this paper, for the first time, we present an optimal sample preparation algorithm based on a minimum-cost maximum-flow model. By using the proposed model, we can obtain both the optimal cost of sample and buffer usage and the waste amount even for multiple-target concentrations. Experiments demonstrate that we can consistently achieve much better results not only in the consumption of sample and buffer but also the waste amount when compared with all the state-of-the-art of the previous approaches.

A network-flow-based optimal sample preparation algorithm for digital microfluidic biochips

Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.

Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees

Sample preparation is an essential step in biochemical reactions. Reactants must be diluted to achieve given target concentrations in sample preparation. Since some reactants like costly reagents and infant's blood are valuable, their usage should be minimized during dilution. In this paper, we propose an optimal reactant minimization algorithm, GORMA, for sample preparation on digital microfluidic biochips. GORMA adopts a systematic method to exhaustively check all possible dilution solutions and then identifies the one with minimal reactant usage and waste through maximal droplet sharing. Experimental results show that GORMA outperforms all the existing methods in reactant usage. Meanwhile, the waste amount is reduced up to 30% as compared with existing waste minimization methods. Moreover, GORMA requires only 0.6% more operations on average when compared with an operation-minimal dilution method.

Graph-based optimal reactant minimization for sample preparation on digital microfluidic biochips

Sample preparation is an essential processing step in most biochemical applications. Various reactants are mixed together to produce a solution with the target concentration. Since reactants generally take a notable part of the cost in a bioassay, their usage should be minimized whenever possible. In this paper, we propose an algorithm, CoDOS, to prepare the target solution with many reactants using common dilution operation sharing on digital microfluidic biochips (DMFBs). CoDOS first represents the given target concentration as a recipe matrix, and then identifies rectangles in the matrix, where each rectangle indicates an opportunity of dilution operation sharing for reactant minimization. Experimental results demonstrate that CoDOS can achieve up to 27% of reactant saving as compared with the bit-scanning method in single-target sample preparation. Moreover, even if CoDOS is not developed for multi-target sample preparation, it still outperforms the recent state-of-the-art algorithm, RSMA. Hence, it is convincing that CoDOS is a better alternative for many-reactant sample preparation.

Sample preparation for many-reactant bioassay on DMFBs using common dilution operation sharing

Sample preparation plays an essential role in most biochemical reactions. Raw reactants are diluted to solutions with desirable concentration values in this process. Since the reactants, like infant’s blood, DNA evidence collected from crime scenes, or costly reagents, are extremely valuable, their usage should be minimized whenever possible. In this paper, we propose a two-phased reactant minimization algorithm (REMIA), for sample preparation on digital microfluidic biochips. In the former phase, REMIA builds a reactant-minimized interpolated dilution tree with specific leaf nodes for a target concentration. Two approaches are developed for tree construction; one is based on integer linear programming (ILP) and the other is heuristic. The ILP one guarantees to produce an optimal dilution tree with minimal reactant consumption, whereas the heuristic one ensures runtime efficiency. Then, REMIA constructs a forest consisting of exponential dilution trees to produce those aforementioned specific leaf nodes with minimal reactant consumption in the latter phase. Experimental results show that REMIA achieves a reduction of reactant usage by 32%–52% as compared with three existing state-of-the-art sample preparation approaches. Besides, REMIA can be easily extended to solve the sample preparation problem with multiple target concentrations, and the extended version also effectively lowers the reactant consumption further.

Chia-Hung Liu

Papers

Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees

Reactant and Waste Minimization in Multitarget Sample Preparation on Digital Microfluidic Biochips

Graph-based optimal reactant minimization for sample preparation on digital microfluidic biochips

Sample preparation for many-reactant bioassay on DMFBs using common dilution operation sharing

Reactant Minimization in Sample Preparation on Digital Microfluidic Biochips