Puddle

Abstract

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.

A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips

“…However, this approach exhibits a limitation in its adaptability to unknown errors. This limitation stems from the fact that the method obtains information about each electrode from a charge-coupled device (CCD) camera [36,37] before initiating the routing process. Consequently, this method falls short in handling the unknown errors that may occur during the routing process or errors that remain undetectable by the CCD camera.…”

“…Therefore, the DMFB environment also offers these four choices of action. The current state provided by the DMFB environment includes the droplet's status, the goal state, and the presence of any known errors, which are provided from a CCD camera in real experiments [36,37]. Furthermore, if a droplet exhibits irregular movements, the DMFB environment incorporates unknown error information into the state.…”

A performance-optimizing compiler for cyber-physical digital microfluidic biochips

“…A compiler or interpreter will translate the specification into an executable format that will run on the SP-LoC. A number of domain-specific programming languages have been proposed for SP-LoCs [4,5,17,18,64,83,84,86]; while most of these languages are tied to specific SP-LoC technologies, any DSL compatible with DMFBs (see § 2.2) could be used as a front-end to the compiler presented here.…”

“…Another approach, which is orthogonal to what we propose here, is to interpret assays online, rather than compile them offline [31,86]. The interpreter JIT-compiles each basic block in an on-demand fashion, emphasizing compilation speed over solution quality.…”