Digital microfluidic biochips (DMFBs) integrated with processors and arrays of sensors form cyberphysical systems and consequently face a variety of unique, recently described security threats. It has been noted that techniques used for error recovery can provide some assurance of integrity when a cyberphysical DMFB is under attack. This paper proposes the use of such hardware for security purposes through the randomization of checkpoints in both space and time, and provides design guidelines for designers of such systems. We define security metrics and present techniques for improving performance through static checkpoint maps, and describe performance tradeoffs associated with static and random checkpoints. We also provide detailed classification of attack models and demonstrate the feasibility of our techniques with case studies on assays implemented in typical DMFB hardware.

Secure Randomized Checkpointing for Digital Microfluidic Biochips

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 ratio-selection 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.

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

Microfluidic technologies enable replacement of time-consuming and complex steps of biochemical laboratory protocols with a tiny chip. Sample preparation (i.e., dilution or mixing of fluids) is one of the primary tasks of any bioprotocol. In real-life applications where several assays need to be executed for different diagnostic purposes, the same sample fluid is often required with different target concentration factors (CFs). Although several multi-target dilution algorithms have been developed for digital microfluidic biochips, they are not efficient for implementation with continuous-flow-based microfluidic chips, which are preferred in the laboratories. In this article, we present a multi-target dilution algorithm (MTDA) for continuous-flow-based microfluidic biochips, which to the best of our knowledge is the first of its kind. We design a flow-based rotary mixer with a suitable number of segments depending on the target-CF profile, error tolerance, and optimization criteria. To schedule several intermediate fluid-mixing tasks, we develop a multi-target scheduling algorithm (MTSA) aiming to minimize the usage of storage units while producing dilutions with multiple CFs. Furthermore, we propose a storage architecture for efficiently loading (storing) of intermediate fluids from (to) the storage units.

Architectural Design of Flow-Based Microfluidic Biochips for Multi-Target Dilution of Biochemical Fluids

Production of correct bioassay outcome is the foremost objective in digital microfluidic biochips (or DMFBs). In high-frequency DMFBs, continuous actuation of electrodes leads to malfunctioning or even breakdown of the system. The improper functioning of a biochip tends to produce erroneous results. On the other hand, while transporting droplets, the residues may get stuck to electrode walls and cause contamination to other droplets. To ensure proper assay outcome, washing becomes mandatory, whose incorporation may delay the bioassay completion time significantly. Furthermore, each wash droplet possesses a capacity constraint within which the residues can be washed off successfully. Evidently, the design objectives possess a large degree of trade-offs among themselves and must be attacked to prepare an efficient platform. Here, the authors propose a complete fluid-level synthesis considering all the essential goals together instead of dealing with them in isolation. The presented approach effectively handles the trade-off scenarios and provides flexibility to the designer to decide the threshold of the individual optimisation objective leading to the construction of a good-quality solution as a whole. The performance is evaluated over several benchmark bioassays.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cdt.2018.5037

Fluid-level synthesis unifying reliability, contamination avoidance, and capacity-wastage-aware washing for droplet-based microfluidic biochips

Scheduling algorithms for reservoir- and mixer-aware sample preparation with microfluidic biochips

In recent years, digital microfluidic biochips are being dominantly used for implementing a wide range of biochemical laboratory protocols (bioprotocols) on hand-held devices. Accurate preparation of fluid-samples is a fundamental preprocessing step that is needed in many bioprotocols. Oftentimes, the number of reservoirs built on-chip may be far less than that of the reactant fluids to be mixed. Hence, during the execution of an assay, several fluids are to be unloaded from the reservoirs to make room for loading new fluids stored off-line. Such unload-wash-load steps (switching) may be required several times, and these steps, being manual, significantly impact assay-completion time. In this paper, we propose a new scheduling scheme namely Reservoir and Mixer constrained Scheduling (RMS) that can schedule a mixing tree obtained by a mixing algorithm, while minimizing the number of switching such that the total completion time can be minimized. Simulation results over a large number of target ratios show that given the mixing trees obtained by standard mixing algorithms such as MinMix/RMA/CoDOS, RMS reduces switching steps (on average by 40.3%/41.9%/33%) at the cost of increasing mixing time (by only 3.5%/6.2%/4.8%), compared to an existing scheduling scheme invoked with reservoir constraints.

Mahammad Samiuddin

Papers

Reservoir and mixer constrained scheduling for sample preparation on digital microfluidic biochips

Scheduling algorithms for reservoir- and mixer-aware sample preparation with microfluidic biochips