Demand-Driven Multi-Target Sample Preparation on Resource-Constrained Digital Microfluidic Biochips

Microfluidic lab-on-chips offer promising technology for the automation of various biochemical laboratory protocols on a minuscule chip. Sample preparation (SP) is an essential part of any biochemical experiments, which aims to produce dilution of a sample or a mixture of multiple reagents in a certain ratio. One major objective in this area is to prepare dilutions of a given fluid with different concentration factors, each with certain volume, which is referred to as the demand-driven multiple-target (DDMT) generation problem. SP with microfluidic biochips requires proper sequencing of mix-split steps on fluid volumes and needs storage units to save intermediate fluids while producing the desired target ratio. The performance of SP depends on the underlying mixing algorithm and the availability of on-chip storage, and the latter is often limited by the constraints imposed during physical design. Since DDMT involves several target ratios, solving it under storage constraints becomes even harder. Furthermore, reduction of mix-split steps is desirable from the viewpoint of accuracy of SP, as every such step is a potential source of volumetric split error. In this article, we propose a storage-aware DDMT algorithm that reduces the number of mix-split operations on a digital microfluidic lab-on-chip. We also present the layout of the biochip with -storage cells and their allocation technique for . Simulation results reveal the superiority of the proposed method compared to the state-of-the-art multi-target SP algorithms.

Study of the Fabrication Technology of Hybrid Microfluidic Biochips for Label-Free Detection of Proteins

A study of the peculiarities and a comparative analysis of the technologies used for the fabrication of elements of novel hybrid microfluidic biochips for express biomedical analysis have been carried out. The biochips were designed with an incorporated microfluidic system, which enabled an accumulation of the target compounds in a biological fluid to be achieved, thus increasing the biochip system’s sensitivity and even implementing a label-free design of the detection unit. The multilevel process of manufacturing a microfluidic system of a given topology for label-free fluorometric detection of protein structures is presented. The technological process included the chemical modification of the working surface of glass substrates by silanization using (3-aminopropyl) trimethoxysilane (APTMS), formation of the microchannels, for which SU-8 technologies and a last generation dry film photoresist were studied and compared. The solid-state phosphor layers were deposited using three methods: drop application; airbrushing; and mechanical spraying onto the adhesive surface. The processes of sealing the system, installing input ports, and packaging using micro-assembly technologies are described. The technological process has been optimized and the biochip was implemented and tested. The presented system can be used to design novel high-performance diagnostic tools that implement the function of express detection of protein markers of diseases and create low-power multimodal, highly intelligent portable analytical decision-making systems in medicine.

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.