This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry that executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contribution of the paper is the BioScript type system. The type system ensures that certain types of errors, specific to biochemistry, do not occur, such as the interaction of chemicals that may be unsafe. Results are obtained using a custom-built compiler that implements the BioScript language and type system.

We generate droplets in a microfluidic device using a traveling surface acoustic wave (TSAW), and control droplet size by adjusting TSAW power and duration. We combine droplet production and fluorescence detection to selectively-encapsulate cells and beads; with this active method, the overwhelming majority of single particles or cells are encapsulated individually into droplets, contrasting the Poisson distribution of encapsulation number that governs traditional, passive microfluidic encapsulation. In addition, we lyse cells before selective encapsulation, and pico-inject new materials into existing droplets.

Molecular manipulation and analysis are the cornerstone of life sciences. With the recent advances in molecular data storage and computing, it has become an increasingly exciting and viable alternative for the post-CMOS scaling era. Widespread use of molecular manipulation/analysis and data storage/computing requires a scalable and low-cost platform for hybrid molecular-electronics systems. This enables us to build on the best of what molecular and electronics systems can offer. In this article, we present PurpleDrop, a full-stack digital microfluidic platform for hybrid molecular-electronic systems in multiple domains, and focus on DNA data storage as a use case. We describe its design principles and relevant aspects such as closed-loop operation with computer vision and capacitive sensing, on-board magnetic bead extraction, and polymerase chain reaction, among other primitives. Importantly, we emphasize the ability to express and execute protocols and computation that include molecular and computational components.
 

This paper report a low-cost fabrication of low-topography multilayer interconnects by selective and controlled anodization of thin-film metal layers. The process utilizes anodization of metal (tantalum in this paper) or, more specifically, repetitions of a partial anodization to form insulation layers between conductive layers and a full anodization to form isolating regions between electrodes, replacing the usual process of depositing, planarizing, and etching insulation layers. After verifying the electric connections and insulations as intended, the developed method is applied to electrowetting-on-dielectric (EWOD), whose complex microfluidic products are currently built on PCB or thin-film transistor substrates. 

This paper introduces a compiler optimization strategy for Software-Programmable Laboratories-on-a-Chip (SP-LoCs), which miniaturize and automate a wide variety of benchtop laboratory experiments. The compiler targets a specific class of SP-LoCs that manipulate discrete liquid droplets on a 2D grid, with cyber-physical feedback provided by integrated sensors and/or video monitoring equipment. The optimization strategy employed here aims to reduce the overhead of transporting fluids between operations, and explores tradeoffs between the latency and resource requirements of mixing operations: allocating more space for mixing shortens mixing time, but reduces the amount of spatial parallelism available to other operations. The compiler is empirically evaluated using a cycle-accurate simulator that mimics the behavior of the target SP-LoC. Our results show that a coalescing strategy, inspired by graph coloring register allocation, effectively reduces droplet transport latencies while speeding up the compiler and reducing its memory footprint. For biochemical reactions that are dominated by mixing operations, we observe a linear correlation between a preliminary result using a default mixing operation resource allocation and the percentage decrease in execution time that is achieved via resizing.

Microfluidic devices promise to automate wetlab procedures by manipulating small chemical or biological samples. This technology comes in many varieties, all of which aim to save time, labor, and supplies by performing lab protocol steps typically done by a technician. However, existing microfluidic platforms remain some combination of inflexible, error-prone, prohibitively expensive, and difficult to program. We address these concerns with a full-stack digital microfluidic automation platform. Our main contribution is a runtime system that provides a high-level API for microfluidic manipulations. It manages fluidic resources dynamically, allowing programmers to freely mix regular computation with microfluidics, which results in more expressive programs than previous work. It also provides real-time error correction through a computer vision system, allowing robust execution on cheaper microfluidic hardware. We implement our stack on top of a low-cost droplet microfluidic device that we have developed. We evaluate our system with the fully-automated execution of polymerase chain reaction (PCR) and a DNA sequencing preparation protocol. These protocols demonstrate high-level programs that combine computational and fluidic operations such as input/output of reagents, heating of samples, and data analysis. We also evaluate the impact of automatic error correction on our system’s reliability.

The integration of microfluidics and biosensor technology is transforming microbiology research by providing new capabilities for clinical diagnostics, cancer research, and pharmacology studies. This integration enables new approaches for biochemistry automation and cyber-physical adaptation. Similarly, recent years have witnessed the rapid growth of the Internet of Things (IoT) paradigm, where different types of real-world elements such as wearable sensors are connected and allowed to autonomously interact with each other. Combining the advances of both cyber-physical microfluidics and IoT domains can generate new opportunities for knowledge fusion by transforming distributed local microfluidic elements into a global network of coordinated microfluidic systems. This paper aims to streamline this transformation and it presents a research vision for enabling the Internet of Microfluidic Things (IoMT). To leverage advances in connected Microfluidic Things, we highlight new perspectives on system architecture, and describe technical challenges related to design automation, temporal flexibility, security, and service assignment. This vision is supported by case studies from cancer research and pharmacology studies to explain the significance of the proposed framework.

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology researchers with vast amounts of data but not necessarily the ability to analyze complex data effectively. Over the past few years, deep artificial neural networks (ANNs) leveraging modern graphics processing units (GPUs) have enabled the rapid analysis of structured input data – sequences, images, videos – to predict complex outputs with unprecedented accuracy. While there have been early successes in flow cytometry, for example, the extensive potential of pairing microfluidics (to acquire data) and deep learning (to analyze data) to tackle biotechnology challenges remains largely untapped. Here we provide a roadmap to integrating deep learning and microfluidics in biotechnology laboratories that matches computational architectures to problem types, and provide an outlook on emerging opportunities.

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry which executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contributions of the paper include the BioScript type system and relevant portions of its compiler. The type system ensures that certain types of errors, specific to biochemistry, do not occur, including the interaction of chemicals that may be unsafe.

Following the development of microfluidic systems, there has been a high tendency towards developing lab-on-a-chip devices for biochemical applications. A great deal of effort has been devoted to improve and advance these devices with the goal of performing complete sets of biochemical assays on the device and possibly developing portable platforms for point of care applications. Among the different microfluidic systems used for such a purpose, digital microfluidics (DMF) shows high flexibility and capability of performing multiplex and parallel biochemical operations, and hence, has been considered as a suitable candidate for lab-on-a-chip applications. In this review, we discuss the most recent advances in the DMF platforms, and evaluate the feasibility of developing multifunctional packages for performing complete sets of processes of biochemical assays, particularly for point-of-care applications. The progress in the development of DMF systems is reviewed from eight different aspects, including device fabrication, basic fluidic operations, automation, manipulation of biological samples, advanced operations, detection, biological applications, and finally, packaging and portability of the DMF devices. Success in developing the lab-on-a-chip DMF devices will be concluded based on the advances achieved in each of these aspects.

This paper describes a publicly available, open source software framework designed to support research efforts on algorithms and control for digital microfluidic biochips (DMFBs), an emerging laboratory-on-a-chip (LoC) technology. The framework consists of two parts: a compiler, which converts an assay, specified using the BioCoder language, into a sequence of electrode activations that execute out the assay on the DMFB; and a printed circuit board (PCB) layout tool, which includes algorithms to reduce the number of control pins and PCB layers required to drive the chip from an external source. The framework also includes a suite of visualization tools for debugging, and a collection of front-end algorithms that generate mixing/dilution trees for sample preparation.

Digital microfluidics (DMF) has emerged as a popular format for implementing quantitative immunoassays for diagnostic biomarkers. All previous reports of such assays have relied on optical detection; here, we introduce the first digital microfluidic immunoassay relying on electrochemical detection. In this system, an indium tin oxide (ITO) based DMF top plate was modified to include gold sensing electrodes and silver counter/pseudoreference electrodes suitable for in-line amperometric measurements. A thyroid stimulating hormone (TSH) immunoassay procedure was developed relying on magnetic microparticles conjugated with primary antibody (Ab1). Antigen molecules are captured followed by capture of a secondary antibody (Ab2) conjugated with horseradish peroxidase enzyme (HRP). HRP catalyzes the oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) which can be detected amperometrically. The limit of detection of the technique (2.4 μIU mL−1) is compatible with clinical applications; moreover, the simplicity and the small size of the detector suggest utility in the future for portable analysis.

We introduce DropBot: an open-source instrument for digital microfluidics (http://microfluidics.utoronto.ca/dropbot). DropBot features two key functionalities for digital microfluidics: (1) real-time monitoring of instantaneous drop velocity (which we propose is a proxy for resistive forces), and (2) application of constant electrostatic driving forces through compensation for amplifier-loading and device capacitance. We anticipate that this system will enhance insight into failure modes and lead to new strategies for improved device reliability, and will be useful for the growing number of users who are adopting digital microfluidics for automated, miniaturized laboratory operation.

Background: Published descriptions of biology protocols are often ambiguous and incomplete, making themdifficult to replicate in other laboratories. However, there is increasing benefit to formalizing the descriptions ofprotocols, as laboratory automation systems (such as microfluidic chips) are becoming increasingly capable ofexecuting them. Our goal in this paper is to improve both the reproducibility and automation of biologyexperiments by using a programming language to express the precise series of steps taken.

Results: We have developed BioCoder, a C++ library that enables biologists to express the exact steps needed toexecute a protocol. In addition to being suitable for automation, BioCoder converts the code into a readable,English-language description for use by biologists. We have implemented over 65 protocols in BioCoder; the mostcomplex of these was successfully executed by a biologist in the laboratory using BioCoder as the only reference.We argue that BioCoder exposes and resolves ambiguities in existing protocols, and could provide the softwarefoundations for future automation platforms. BioCoder is freely available for download at http://research.microsoft.com/en-us/um/india/projects/biocoder/.

Conclusions: BioCoder represents the first practical programming system for standardizing and automatingbiology protocols. Our vision is to change the way that experimental methods are communicated: rather thanpublishing a written account of the protocols used, researchers will simply publish the code. Our experiencesuggests that this practice is tractable and offers many benefits. We invite other researchers to leverage BioCoderto improve the precision and completeness of their protocols, and also to adapt and extend BioCoder to newdomains.

Microfluidic chips are emerging as a powerful platform for automating biology experiments. As it becomes possible to integrate tens of thousands of components on a single chip, researchers will require design automation tools to push the scale and complexity of their designs to match the capabilities of the substrate. However, to date such tools have focused only on droplet-based devices, leaving out the popular class of chips that are based on multilayer soft lithography. In this paper, we develop design automation techniques for microfluidic chips based on multilayer soft lithography. We focus our attention on the control layer, which is driven by pressure actuators to invoke the desired flows on chip. We present a language in which designers can specify the Instruction Set Architecture (ISA) of a microfluidic device. Given an ISA, we automatically infer the locations of valves needed to implement the ISA. We also present novel algorithms for minimizing the number of control lines needed to drive the valves, as well as for routing valves to control ports while admitting sharing between the control lines. To the microfluidic community, we offer a free computer-aided design tool, Micado, which implements a subset of our algorithms as a practical plug-in to AutoCAD. Micado is being used successfully by microfluidic designers. We demonstrate its performance on three realistic chips.