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Code Language Developed for Building Lab-On-Chip Devices

Microfluidic chip

Microfluidic chip (Sandia National Lab)

17 June 2014. Engineers at University of California in Riverside developed a programming language to automate the design of lab-on-a-chip devices used in medical diagnostics and other life science applications. The team from the lab of computer science and engineering professor Philip Brisk published its results in a recent issue of the ACM Journal on Emerging Technologies in Computing Systems.

Microfluidics — analyzing fluids in tiny channels on a miniaturized plastic chip — offers ways to perform biochemical tests faster, more reliably, and less expensively than before. As a result, many new types of microfluidic, or lab-on-a-chip, devices are being developed to simulate benchtop lab tests that detect disease, discover therapies, and sequence DNA.

With current practices, note the researchers, lab-on-a-chip components are passive devices with electrodes and sensors that perform one or a limited number of tests and require an associated computing system to process signals sent from the chip. Designing these chips is a manual process, which usually limits the chip’s functions, and increases the chance for human error in the design. Brisk and colleagues aimed to develop a process that makes it possible to dynamically and interactively design labs-on-a-chip, to incorporate feedback and reduce the chance for human error.

For this task, the researchers adapted BioCoder, a programming language developed at Microsoft for biological test protocols, with a library of routines in C++ for a human-readable format. The team, led by Ph.D. candidate and first-author Dan Grissom, extended BioCoder to interpret microfluidic signals with a process called electrowetting on dielectric for analyzing droplets, such as those from human specimens.

The extensions to BioCoder, say the authors, make it possible to dynamically design labs-on-a-chip based on feedback from the sensors, and thus make it easier and cheaper to extend the capabilities of chips for diagnostics or other life science applications. The team tested their designs on a low-power software simulator, to approximate real-life environments of lab-on-a-chip devices, such as in hand-held and point-of-care diagnostics. The results of three experiments showed the dynamic design process could incorporate control-flow feedback and reduce the overhead of static methods.

“Now, you have a chip, you use it and then you analyze it,” says Brisk in a university statement. “Through automation and programmability, you eliminate human error, cut costs, and speed up the entire process.” The team next plans to build a prototype device with biomedical engineers at Riverside.

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