Flow-based Microfluidic Biochips Research Articles

Preparing Fluid Samples Under Retention Time Constraints Using Flow-Based Microfluidic Biochips

Sample preparation is an essential step in almost all bioprotocols, which can be efficiently achieved via sequence of mixing steps called mixing graph. In literature, several techniques have been reported to determine a mixing graph with the minimal number of mixing steps, or the minimal usage of reagent fluids, or the minimal wastage, or sometime a combination of them. The retention time of an flow-based microfluidic biochip (FMB) is defined as the maximum duration for which a fluid can be stored within a microchannel without any fluid leakage. However, the retention time has not yet been considered as a scheduling constraint during automation of the sample preparation using an FMB in order to obtain the scheduled mixing graphs. In this paper, we propose a retention time aware scheduling method called time aware list scheduling ( TALS), which can be used with the state-of-the-art methods, and a new satisfiability based mixing algorithm called time aware sample preparation ( TASP) to obtain the scheduled mixing graph for a target ratio satisfying the retention time constraint and the number of available on-chip mixers in an FMB. Simulation results suggest that on an average TALS always outperforms a baseline scheduling method, while scheduling any mixing graph. Whereas, TASP can determine the optimal and scheduled mixing graphs compared to the existing mixing methods combined with TALS.

Material-level countermeasures for securing microfluidic biochips.

Flow-based microfluidic biochips (FMBs) have been rapidly commercialized and deployed in recent years for biological computing, clinical diagnostics, and point-of-care-tests (POCTs). However, outsourcing FMBs makes them susceptible to material-level attacks by malicious actors for illegitimate monetary gain. The attacks involve deliberate material degradation of an FMB's polydimethylsiloxane (PDMS) components by either doping with reactive solvents or altering the PDMS curing ratio during fabrication. Such attacks are stealthy enough to evade detection and deteriorate the FMB's function. Furthermore, material-level attacks can become prevalent in attacks based on intellectual property (IP) theft, such as counterfeiting, overbuilding, etc., which involve unscrupulous third-party manufacturers. To address this problem, we present a dynamic material-level watermarking scheme for PDMS-based FMBs with microvalves using a perylene-labeled fluorescent dye. The dyed microvalves show a unique excimer intensity peak under 405 nm laser excitation. Moreover, when pneumatically actuated, the peak shows a predetermined downward shift in intensity as a function of mechanical strain. We validated this protection scheme experimentally using fluorescence microscopy, which showed a high correlation (R2 = 0.971) between the normalized excimer intensity change and the maximum principal strain of the actuated microvalves. To detect curing ratio-based attacks, we adapted machine learning (ML) models, which were trained on the force-displacement data obtained from a mechanical punch test method. Our ML models achieved more than 99% accuracy in detecting curing ratio anomalies. These countermeasures can be used to proactively safeguard FMBs against material-level attacks in the era of global pandemics and diagnostics based on POCTs.

Structural Attacks and Defenses for Flow-Based Microfluidic Biochips.

Flow-based microfluidic biochips (FMBs) have seen rapid commercialization and deployment in recent years for point-of-care and clinical diagnostics. However, the outsourcing of FMB design and manufacturing makes them susceptible to susceptible to malicious physical level and intellectual property (IP)-theft attacks. This work demonstrates the first structure-based (SB) attack on representative commercial FMBs. The SB attacks maliciously decrease the heights of the FMB reaction chambers to produce false-negative results. We validate this attack experimentally using fluorescence microscopy, which showed a high correlation ( R2 = 0.987) between chamber height and related fluorescence intensity of the DNA amplified by polymerase chain reaction. To detect SB attacks, we adopt two existing deep learning-based anomaly detection algorithms with ∼96% validation accuracy in recognizing such deliberately introduced microstructural anomalies. To safeguard FMBs against intellectual property (IP)-theft, we propose a novel device-level watermarking scheme for FMBs using intensity-height correlation. The countermeasures can be used to proactively safeguard FMBs against SB and IP-theft attacks in the era of global pandemics and personalized medicine.

Computer-aided Design Techniques for Flow-based Microfluidic Lab-on-a-chip Systems

As one of the most promising lab-on-a-chip systems, flow-based microfluidic biochips are being increasingly used for automatically executing various laboratory procedures in biology and biochemistry, such as enzyme-linked immunosorbent assay, point-of-care diagnosis, and so on. As manufacturing technology advances, the characteristic dimensions of biochip systems keep shrinking, and tens of thousands of microvalves can now be integrated into a coin-sized microfluidic platform, making the conventional manual-based chip design no longer applicable. Accordingly, computer-aided design (CAD) of microfluidics has attracted considerable research interest in the EDA community over the past decade. This review article presents recent advances in the design automation of biochips, involving CAD techniques for architectural synthesis, wash optimization, testing, fault diagnosis, and fault-tolerant design. With the help of these CAD tools, chip designers can be released from the burden of complex, large-scale design tasks. Meanwhile, new chip architectures can be explored automatically to open new doors to meet requirements from future large-scale biological experiments and medical diagnosis. We discuss key trends and directions for future research that are related to enable microfluidics to reach its full potential, thus further advancing the development and progression of the microfluidics industry.

An Integrated Co-Design of Flow-Based Biochips Considering Flow-Control Design Issues and Objectives

With increasing effectiveness of flow-based microfluidic biochips in the field of biochemical experiments and point-of-care diagnosis, design automation demands enormous attention to integrate the flow-layer design and control-layer design instead of considering them in isolation. Some severe design issues like valve reliability, control-channel delay, pressure skew, etc. are obvious and they must be tactfully dealt along with minimization of several cost-driving issues like schedule length, pin count, and control-channel length while ensuring planarity of the flow-layer components as well as control-layer channel routes. Hence, this paper aims to propose a unified flow-control co-design methodology considering various flow-control cost-driving issues while reducing the design cycles to obtain a low-cost and efficient platform. The performance is evaluated over several benchmark bioassays that show the efficacy of the proposed co-design method which reduces the number of required valves by 30%, control-channel length by 48.5%, and number of flow-channel crossings by 72.2% than the existing works, on an average.

Fluid-to-cell assignment and fluid loading on programmable microfluidic devices for bioprotocol execution

Being a structure like a two-dimensional (2D) array of microvalves and cells, Programmable Microfluidic Device PMD biochips have the characteristics of reconfigurability and flexibility unlike conventional flow-based microfluidic biochips. In recent years, several design automation techniques for PMD biochips have been reported. For automated control of the PMD chip implementing a bioprotocol, one of the important tasks is to minimize the number of fluid flows for loading the reactant fluids into specific cells before the bioprotocol is executed. In this work we intensively study the fluid loading problem for PMD chips and we propose a two-phase approach to solve this problem. First, we propose a constraint satisfaction problem (CSP) based method, called loading-aware fluid-to-cell assignment (LAFCA) in order to obtain a suitable fluid-to-cell assignment, which will be beneficial for fluid loading phase. Then we propose an exact method, called CSP-based loading algorithm (CSPLA) and a near-optimal heuristic method, called determining flows from the last (DFL), for determining a sequence of fluid flows required to load different fluids into the cells of a PMD chip. We formulate CSPLA as a single objective optimization problem to minimize the total number of flows. For the output as a sequence of fluid flows we define three loading parameters, the total number of flows (K), the total number of 90° bends in all flow paths (B), and the total flow path length (L). Simulation results confirm that LAFCA combined with CSPLA outperforms (K, B and L reduced by 63.9%, 31.7% and 59.9%, respectively) the state-of-the-art method fluid loading algorithm for PMD (FLAP) [Gupta et al., TODAES, 2019]. Whereas, LAFCA combined with DFL can reduce the loading parameters K, B and L by 61.2%, 20.8% and 57.4%, respectively over using only FLAP. Also from the overall simulation results we can conclude that for many testcases, DFL can find the near-optimal Ks.

Flow-Based Microfluidic Biochips With Distributed Channel Storage: Synthesis, Physical Design, and Wash Optimization

System-architecture design optimization of flow-based microfluidic biochips has been extensively investigated over the past decade. Most of the prior work, however, is still based on chip architectures with dedicated storage units and this, not only limits the performance of biochips, but also increases their fabrication cost. To overcome this limitation, a distributed channel-storage architecture can be implemented, where fluid samples can be cached temporarily in flow channels instead of using a dedicated storage. This new concept of fluid storage, however, requires a careful arrangement of fluid samples to enable the channels to fulfill the dual functions of transportation and caching. Moreover, to avoid cross-contamination between different fluidic flows, wash operations are necessary to remove the residue left in flow channels. In this article, we formulate the first practical system level design and wash optimization problem for microfluidic biochips with distributed channel storage architecture, considering high-level synthesis, physical design, and wash optimization simultaneously, and present a top-down design flow to solve this problem systematically. Given the protocol of a biochemical application and the corresponding design requirements, our goal is to generate a chip architecture with low fabrication cost. Meanwhile the biochemical application can be executed efficiently with an optimized wash scheme. Experimental results on multiple benchmarks confirm that our approach leads to short completion time of biochemical applications, low chip cost, as well as high wash efficiency.

DCSA: Distributed Channel-Storage Architecture for Flow-Based Microfluidic Biochips

Flow-based microfluidic biochips have attracted much attention in the EDA community due to their miniaturized size and execution efficiency. Previous research, however, still follows the traditional computing model with a dedicated storage unit, which actually becomes a bottleneck of the performance of biochips. In this article, we propose a distributed channel-storage architecture (DCSA) to cache fluid samples inside flow channels temporarily. Since distributed storage can be accessed more efficiently than a dedicated storage unit and channels can switch between the roles of transportation and storage easily, biochips with this architecture can achieve a higher execution efficiency even with fewer resources. Furthermore, we also address the flow-path planning that enables the manipulation of actual fluid transportation/caching on a chip. The simulation results confirm that the execution efficiency of a bioassay can be improved significantly, while the number of valves in the biochip can be reduced accordingly. Also, flow paths for transportation tasks can be constructed and planned automatically with minimum extra resources.

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

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 ( CF s). 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 CF s. Furthermore, we propose a storage architecture for efficiently loading (storing) of intermediate fluids from (to) the storage units.

Security of Microfluidic Biochip

With the advancement of system miniaturization and automation, Lab-on-a-Chip (LoC) technology has revolutionized traditional experimental procedures. Microfluidic Biochip (MFB) is an emerging branch of LoC with wide medical applications such as DNA sequencing, drug delivery, and point of care diagnostics. Due to the critical usage of MFBs, their security is of great importance. In this article, we exploit the vulnerabilities of two types of MFBs: Flow-based Microfluidic Biochip (FMFB) and Digital Microfluidic Biochip (DMFB). We propose a systematic framework for applying Reverse Engineering (RE) attacks and Hardware Trojan (HT) attacks on MFBs as well as for practical countermeasures against the proposed attacks. We evaluate the attacks and defense on various benchmarks where experimental results prove the effectiveness of our methods. Security metrics are defined to quantify the vulnerability of MFBs. The overhead and performance of the proposed attacks as well as countermeasures are also discussed.

Scalable pseudo‐exhaustive methodology for testing and diagnosis in flow‐based microfluidic biochips

Microfluidics is an upcoming field of science that is going to be used widely in many safety-critical applications including healthcare, medical research and defence. Hence, technologies for fault testing and fault diagnosis of these chips are of extreme importance. In this study, the authors propose a scalable pseudo-exhaustive testing and diagnosis methodology for flow-based microfluidic biochips. The proposed approach employs a divide-and-conquer based technique wherein, large architectures are split into smaller sub-architectures and each of these are tested and diagnosed independently.

Toward Secure Microfluidic Fully Programmable Valve Array Biochips

The fully programmable valve array (FPVA) is a general-purpose programmable flow-based microfluidic platform, akin to the VLSI field-programmable gate array (FPGA). FPVAs are dynamically reconfigurable and, hence, are suitable in a broad spectrum of applications involving immunoassays and cell analysis. Since these applications are safety critical, addressing security concerns is vital for the success and adoption of FPVAs. This study evaluates the security of FPVA biochips. We show that FPVAs are vulnerable to malicious operations similar to digital and flow-based microfluidic biochips. FPVAs are further prone to new classes of attacks-tunneling and deliberate aging. This study establishes security metrics and describes possible attacks on real-life bioassays. Furthermore, we study the use of machine learning (ML) techniques to detect and classify attacks based on the golden and real-time biochip state. In order to boost the classifier's performance, we propose a smart checkpointing mechanism. Experimental results are presented to showcase: 1) best-fit ML model classifier; 2) performance of different tradeoffs in checkpointing; and 3) effectiveness of the proposed smart checkpointing scheme.

Synthesis of Reconfigurable Flow-Based Biochips for Scalable Single-Cell Screening

Single-cell screening is used to sort a stream of cells into clusters (or types) based on prespecified biomarkers, thus supporting type-driven biochemical analysis. Reconfigurable flow-based microfluidic biochips (RFBs) can be utilized to screen hundreds of heterogeneous cells within a few minutes, but they are overburdened with the control of a large number of valves. To address this problem, we present a pin-constrained RFB design methodology for single-cell screening. The proposed design is analyzed using computational fluid dynamics simulations, mapped to an RC-lumped model, and combined with intervalve connectivity information to construct a high-level synthesis framework, referred to as cell sorter using multiplexed control (Sortex). Simulation results show that Sortex significantly reduces the number of control pins and fulfills the timing requirements of single-cell screening.

Test Generation for Flow-Based Microfluidic Biochips With General Architectures

Flow-based microfluidic biochips have become a promising platform for complex biochemical assays. As the integration of such chips is increasing, a flexible general reconfigurable platform, fully programmable valve array (FPVA), has emerged. Such a 2-D array comprises regularly arranged valves using which flow-networks with different geometry, size, and connectivity can be constructed dynamically. However, the test generation for such arrays becomes challenging due to the large number of potential flow-networks and transportation paths that can be configured on-chip. In this article, we propose a strategy to generate efficient test patterns for FPVAs based on the concepts of test paths and cuts. These patterns together can cover multiple faults in both flow and control layers. We also introduce the concept of test trees and multiple cuts for a test pattern to deal with faults in FPVAs with multiple ports. Moreover, the proposed method can be applied to generate test patterns for traditional flow-based biochips with predefined architectures. The simulation results demonstrate that defects in FPVAs can be detected reliably by a limited number of test patterns generated by the proposed method. For traditional biochips with predefined architectures, these patterns also exhibit an improved test efficiency.

Microfluidic Design for Concentration Gradient Generation Using Artificial Neural Network

According to the complexity of human physiology and variability among individuals, e.g., genes, environment, lifestyle exposures, etc., personalized medicine aims to synthesize the specific efficacious drug for each individual patient. For synthesizing personalized medicine, customized solutions with specific concentrations are required. Equipped with the advantages in saving costly reagents and rare samples, microfluidic biochips are promising in generating different concentrations for personalized medicine. On the one hand, digital microfluidic biochips require the programming control for driving the movement of the droplets, which suffer from random errors caused by imbalanced droplet splitting. On the other hand, existing flow-based microfluidic biochips can only generate linear concentration gradients, which cause significant waste for synthesizing personalized medicine. To address the above issues, this article proposes the first artificial neural network (ANN)-based design method for flow-based microfluidic biochips, which accurately generates the customized concentration gradients. According to the required concentration, an initial chip is first selected from the prebuilt database and then fine-tuned by ANN to better match the required concentration. The computational simulation results show that the induced deviations in generated concentrations are generally less than 0.014, which validates the accuracy of the proposed neural network model.

Synterface

Flow-based microfluidic biochips can be used to perform bioassays by manipulating a large number of on-chip valves. These biochips are increasingly used today for biomolecular recognition, single-cell screening, and point-of-care disease diagnostics, and design-automation solutions for flow-based microfluidics enable the mapping and optimization of bimolecular protocols and software-based valve control. However, a key problem that has not received adequate attention is chip-to-world interfacing, which requires the use of off-chip control equipment to provide control signals for the on-chip valves. This problem is exacerbated by the increase in the number of valves as chips get more complex. To address the interfacing problem, we present an efficient pin-count minimization (synthesis) problem, referred to as Synterface, which uses on-chip microfluidic logic gates and optimization based on concepts from linear algebra. We present results to show that Synterface significantly reduces pin-count and simplifies the external interface for flow-based microfluidics.

Multicontrol: Advanced Control-Logic Synthesis for Flow-Based Microfluidic Biochips

Flow-based microfluidic biochips are one of the most promising platforms used in biochemical and pharmaceutical laboratories due to their high efficiency and low costs. Inside such a chip, fluids of nanoliter volumes are transported between devices for various operations, such as mixing and detection. The transportation channels and corresponding operation devices are controlled by microvalves driven by external pressure sources. Since assigning an independent pressure source to every microvalve would be impractical due to high costs and limited system dimensions, states of microvalves are switched by a control logic using time multiplexing. Existing control-logic designs, however, still switch only a single control channel per operation, leading to a low efficiency. In this article, we present the first automatic synthesis approach for a control logic that is able to switch multiple control channels simultaneously. Moreover, we propose the first fault-aware design in control logic by introducing backup control paths to maintain the correct function even when manufacturing defects occur. The construction of control logic is achieved by a highly efficient framework based on particle swarm optimization, Boolean logic simplification, grid routing, together with mixing multiplexing. The simulation results demonstrate that the proposed multichannel switching mechanism leads to fewer valve-switching times and lower total logic cost, while realizing fault tolerance for all control channels.

Timing-Driven Flow-Channel Network Construction for Continuous-Flow Microfluidic Biochips

The emergence of flow-based microfluidic biochips (FBMBs) has increased the automation level of biochemical procedures, and these lab-on-a-chip devices are now being used for enzyme-linked immunosorbent assay, point-of-care diagnosis, etc. As fabrication technology advances, the feature size of FBMBs keeps shrinking, thereby introducing a series of knotty challenges to the physical design of FBMBs. In particular, timing-sensitive bioassays, such as forensic DNA typing and chromatin immunoprecipitation, require a highly accurate time-control of fluids within a limited completion time. However, existing work does not consider the real-time requirements of these bioassays. In this paper, we formulate the first practical timing-driven flow-channel network construction problem for FBMBs and present a performance-driven placement and routing algorithm for solving this problem. Given the design specifications of a biochip and its biochemistry application, our goal is to construct a high-quality flow-channel network with minimized timing delay and total cost. The experimental results on 14 benchmarks confirm that our algorithm leads to better timing behavior and lower chip cost.

Analysis and Design of Tamper-Mitigating Microfluidic Routing Fabrics

Microfluidic routing fabrics are reconfigurable primitives that permit the dynamic redirection of fluids on a flow-based microfluidic biochip. Such primitives are bringing the benefits of rapid prototyping and on-the-fly reconfigurability from integrated circuits to the microfluidic domain. An unfortunate side effect of this increased flexibility is susceptibility to tampering. A malicious adversary can alter either the electronic control signals or the pneumatic control lines used to drive the routing fabric. In this paper, we provide a high-level security assessment of microfluidic systems utilizing routing fabrics, and analyze their security under actuation tampering attacks. We show that under reasonable assumptions, the permissible states of a routing fabric form a probability distribution. We provide methods for efficiently determining this distribution through a binary tree representation. We then show how to synthesize routings fabrics that exhibit well-defined behaviors. We call a routing fabric designed in such a way tamper-mitigating , as it makes the effects of tampering probabilistically less severe. We then show how the proposed methodology can be used to protect a forensic DNA barcoding application from attack.

AARF: Any-Angle Routing for Flow-Based Microfluidic Biochips

Flow-based microfluidic biochips are promising with significant applications for automating and miniaturizing laboratory procedures in biochemistry. Automated design methods for flow-based microfluidic biochips are becoming increasingly important due to the advancement in both integration scale and design complexity for complicated biochemical applications. Though the multilayer soft lithography fabrication provides flexibility to route both flow and control channels in any angle, existing routing algorithms still adopt Manhattan routing metrics, which design channel in either vertical or horizontal direction only. Moreover, based on the computational fluid dynamics analysis, rectilinear channels with 90° bends have the following issues: 1) reduced the fluidic flow rate, which degrades the performance of the biochip and may even result in the erroneous outcome of the whole procedure and 2) increased pressure at the right-angle bend, which negatively affects the reliability of the biochip. To fully utilize the routing flexibility, this paper proposes the first any-angle routing algorithm for flow-based microfluidic biochip, called AARF. Computational simulation results show that compared with traditional Manhattan routing method, the proposed AARF significantly improves the total wirelength and total effective wirelength (considering the turning angles) by 17.11% and 35.91%, respectively, which prove the effectiveness of the AARF routing flow.