Interpreting Assays with Control Flow on Digital Microfluidic Biochips

Grissom, Daniel; Curtis, Christopher W.; Brisk, Philip

doi:10.1145/2567669

Cited by 22 publications

(34 citation statements)

References 44 publications

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“…The assays are specified using BioCoder, a somewhat more general language (technically, a C++ library) originally developed at Microsoft Research, India [3], and subsequently specialized by the authors for digital microfluidics [11]. The authors introduced extensions to BioCoder obtain sensory feedback from the EWoD device, and to take actions (conditional statements; loops) based on computations performed on the sensor readings.…”

Section: Biocoder: a High-level Programming Language For Digital Micrmentioning

confidence: 99%

“…[11]. Our compiler converts the BioCoder application into a bytecode format that is fed into a real-time interpreter, which communicates with the simulator to execute the simulated assay.…”

Section: Simulatormentioning

confidence: 99%

“…Secondly, control is provided exclusively through electricity, which eliminates the need for external pressure sources, which significant inhibit the ability to remove other LoCs from the laboratory for usage in the field. Thirdly, the electrical interface provided by a DMFB is a natural match for computer control; thus, there has been interest in the development of programming languages, compiler technology and software control of DMFBs in recent years [5,9,11,14,18,27,28].…”

Section: Introductionmentioning

confidence: 99%

“…4), where feedback provided from the device to the host PC controller cannot be predicted a-priori. This context necessitates the specification of a feedback-driven assay using a domain-specific programming language, coupled with the software infrastructure (compiler and runtime system) that coordinates the entire system [11]. Such a system offers a potential productivity boon to users of digital microfluidic systems; however, it requires them to think about their research at a higher level of abstraction, akin to professional software development, as opposed to biochemistry.…”

Section: Introductionmentioning

confidence: 99%

“…We report the specification and simulated execution of two feedback-driven assays using a domain-specific language and software infrastructure developed previously by the authors [11]. The assays that we simulate are PCR variants: one uses sensory feedback to terminate thermocycling early when the initial droplet contains insufficient Microelectronic Engineering 148 (2015) 110-116 product for amplification [18]; the other uses integrated sensors to detect droplet evaporation during thermocycling in an air-matrix digital microfluidic system, and performs just-in-time replenishment using droplets of solvent to ensure that the reaction volume remains within an acceptable window [14].…”

Section: Introductionmentioning

confidence: 99%

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Simulation of feedback-driven PCR assays on a 2D electrowetting array using a domain-specific high-level biological programming language

Curtis

Brisk

2015

Microelectronic Engineering

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a b s t r a c t a r t i c l e i n f oThe motivation of our work is to demonstrate that scientists can describe biochemical assays using a high-level domain-specific programming language and can execute them automatically on a two-dimensional electrowetting array featuring integrated sensors that provide feedback in real-time to the host PC that controls the integrated system. The fundamental research problem that this paper addresses is how to express, at a high level of abstraction, the acquisition of sensory information from the device, and the computation on that data, which is required to perform real-time decision making in response. The approach that we have taken is first, to create a new version of BioCoder, a programming language for automated biology, which we have specialized for electrowetting arrays featuring integrated sensors, and second, to use this language to specify two feedbackdriven Polymerase Chain Reaction (PCR) assays at the desired level of abstraction. Our methodology is to evaluate the performance of these assays using a custom-build runtime system to control the execution of feedbackdriven assays on a cycle-accurate software simulator that accurately characterizes the behavior of the electrowetting platform during assay execution. The result of this experiment is successful simulation of these non-trivial feedback-driven assays, starting from a high-level specification, which allows us to conclude that high-level programming language design and implementation targeting electrowetting (or other competing laboratory-on-a-chip technologies) is feasible.

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Section: Biocoder: a High-level Programming Language For Digital Micrmentioning

confidence: 99%

“…[11]. Our compiler converts the BioCoder application into a bytecode format that is fed into a real-time interpreter, which communicates with the simulator to execute the simulated assay.…”

Section: Simulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulation of feedback-driven PCR assays on a 2D electrowetting array using a domain-specific high-level biological programming language

Curtis

Brisk

2015

Microelectronic Engineering

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A hybrid artificial bee colony algorithm for scheduling of digital microfluidic biochip operations

Rajesh

Pyne

2021

Concurrency and Computation

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Summary Digital microfluidic biochips (DMFBs) are designed to efficiently carry out biochemical and biomedical analysis in a miniaturized way. DMFBs offer various advantages over traditional laboratory techniques and reduces cost, and increases automation and software programmability. Scheduling of microfluidic operations is the first and essential step in the fluidic‐level synthesis of DMFBs, while the other two are the module placement and droplet routing. Scheduling DMFB operations is a multiconstrained optimization problem, and the particular decision problem is NP‐complete. We propose a hybrid artificial bee colony (ABC) algorithm using generalized N‐point crossover (GNX) based scheduling of DMFB operations. Proposed ABC‐GNX perturbs through search space, evaluates various schedules possible, and returns the best schedule among the evaluated schedules. Simple list scheduling based heuristic algorithms can explore a single schedule based on the sequence generated by the priority function. Iterative improvement based search algorithms explore the search space and evaluate more schedules, but the proposed ABC‐GNX algorithm produces optimal solutions in shorter execution times. Simulation results show that the proposed ABC‐GNX produces a higher number of optimal completion times and faster execution times than existing algorithms.

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