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Abstraction layers for scalable microfluidic biocomputing

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 biological computers.

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Notes

  1. 1.

    lg n denotes log2 n.

  2. 2.

    Alternately, BioStream supports a global error tolerance ε that applies to all concentrations.

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Acknowledgements

We are grateful to David Wentzlaff and Mats Cooper for early contributions to this research. We also thank John Albeck for helpful discussions about experimental protocols. This work was supported by National Science Foundation grant #CCF-0541319. J.P.U. was funded in part by the National Science and Engineering Research Council of Canada (PGSM Scholarship).

Author information

Correspondence to William Thies.

Additional information

This is an extended version of a paper (Thies et al. 2006) that appeared in the 12th International Meeting on DNA Computing, June, 2006.

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Thies, W., Urbanski, J.P., Thorsen, T. et al. Abstraction layers for scalable microfluidic biocomputing. Nat Comput 7, 255–275 (2008). https://doi.org/10.1007/s11047-006-9032-6

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Keywords

  • Microfluidics
  • Laboratory automation
  • DNA computing
  • Biological computation
  • Self-assembly
  • Programming languages