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Abstraction Layers for Scalable Microfluidic Biocomputers (Extended Version)

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dc.contributor.advisor Saman Amarasinghe
dc.contributor.author Thies, William
dc.contributor.author Urbanski, John Paul
dc.contributor.author Thorsen, Todd
dc.contributor.author Amarasinghe, Saman
dc.contributor.other Computer Architecture
dc.date.accessioned 2006-05-05T19:42:11Z
dc.date.available 2006-05-05T19:42:11Z
dc.date.issued 2006-05-05
dc.identifier.other MIT-CSAIL-TR-2006-034
dc.identifier.uri http://hdl.handle.net/1721.1/32543
dc.description.abstract Microfluidic devices are emerging as an attractive technology for automatically orchestrating the reactions needed in a biological computer. Thousands of microfluidic primitives have already been integrated on a single chip, and recent trends indicate that the hardware complexity is increasing at rates comparable to Moore's Law. As in the case of silicon, it will be critical to develop abstraction layers--such as programming languages and Instruction Set Architectures (ISAs)--that decouple software development from changes in the underlying device technology.Towards this end, this paper presents BioStream, a portable language for describing biology protocols, and the Fluidic ISA, a stable interface for microfluidic chip designers. A novel algorithm translates microfluidic mixing operations from the BioStream layer to the Fluidic ISA. To demonstrate the benefits of these abstraction layers, we build two microfluidic chips that can both execute BioStream code despite significant differences at the device level. We consider this to be an important step towards building scalable biocomputers.
dc.format.extent 14 p.
dc.format.extent 6101702 bytes
dc.format.extent 453129 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subject biological computing
dc.subject DNA computing
dc.subject microfluidics
dc.title Abstraction Layers for Scalable Microfluidic Biocomputers (Extended Version)


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