Natural Computing

, Volume 7, Issue 2, pp 255–275 | Cite as

Abstraction layers for scalable microfluidic biocomputing

  • William ThiesEmail author
  • John Paul Urbanski
  • Todd Thorsen
  • Saman Amarasinghe


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.


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



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).


  1. Adar R, Benenson Y, Linshiz G, Rozner A, Tishby N, Shapiro E (2004) Stochastic computing with biomolecular automata. PNAS 101:9960–9965CrossRefGoogle Scholar
  2. Adleman L (1994) Molecular computation of solutions to combinatorial problems. Science 266:1021–1024CrossRefGoogle Scholar
  3. Alfred V. Aho RS, Ullman JD (1988) Compilers: principles, techniques, and tools, 2nd edn. Addison-Wesley Publishing Company, pp 561Google Scholar
  4. Allan L, Morrice N, Brady S, Magee G, Pathak S, Clarke P (2003) Inhibition of caspase-9 through phosphorylation at Thr 125 by ERK MAPK. Nature Cell Biol 5:647–654CrossRefGoogle Scholar
  5. Batten C, Krashinsky R, Knight JT (2004) A scalable cellular logic technology using zinc-finger proteins. In 3rd workshop on non-silicon computingGoogle Scholar
  6. Benenson K, Paz-Elitzur T, Adar R, Keinan E, Livneh Z, Shapiro E (2001) Programmable and autonomous computing machine made of biomolecules. Nature 414:430–434CrossRefGoogle Scholar
  7. Benenson Y, Gil B, Ben-Dor U, Adar R, Shapiro E (2004) An autonomous molecular computer for logical control of gene expression. Nature 429:423–429CrossRefGoogle Scholar
  8. Braich RS, Chelyapov N, Johnson C, Rothemund PWK, Adleman L (2002) Solution of a 20-variable 3-SAT problem on a DNA computer. Science 296:499–502CrossRefGoogle Scholar
  9. Breslauer DN, Lee PJ, Lee LP (2006) Microfluidics-based systems biology. Mol BioSystems 2:97–112CrossRefGoogle Scholar
  10. Chou H, Unger M, Quake S (2001) A microfabricated rotary pump. Biomed Microdevices 3:323–330CrossRefGoogle Scholar
  11. Dertinger SKW, Chiu DT, Jeon NL, Whitesides GM (2001) Generation of gradients having complex shapes using microfluidic networks. Anal Chem 73:1240–1246CrossRefGoogle Scholar
  12. Ellerby H, Martin S, Ellerby L, Naiem S, Rabizadeh S, Salvesen G, Casiano C, Cashman N, Green D, Bredesen D (1997) Establishment of a cell-free system of neuronal apoptosis: comparison of premitochondrial, mitochondrial, and postmitochondrial phases. Neuroscience 17:6165–6178Google Scholar
  13. Elowitz M, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403:335–338CrossRefGoogle Scholar
  14. Erickson D, Li D (2004) Integrated microfluidic devices. Anal Chim Acta 507:11–26CrossRefGoogle Scholar
  15. Ezziane Z (2006) DNA Computing: applications and challenges. Nanotechnology 17:173–198CrossRefGoogle Scholar
  16. Farfel J, Stefanovic D (2005) Towards practical biomolecular computers using microfluidic deoxyribozyme logic gate networks. In proceedings of the 11th international meeting on DNA computing, 38–54Google Scholar
  17. Faulhammer D, Cukras AR, Lipton RJ, Landweber LF (2000) Molecular computation: RNA solutions to chess problems. PNAS 97(4):1385–1389CrossRefGoogle Scholar
  18. Gascoyne PRC, Vykoukal JV, Schwartz JA, Anderson TJ, Vykoukal DM, Current KW, McConaghy C, Becker FF, Andrews C (2004) Dielectrophoresis-based programmable fluidic processors. Lab Chip 4:299–309CrossRefGoogle Scholar
  19. Gehani A, Reif J (1999) Micro flow bio-molecular computation. Biosystems 52:197–216CrossRefGoogle Scholar
  20. Grover WH, Mathies RA (2005) An integrated microfluidic processor for single nucleotide polymorphism-based DNA computing. Lab Chip 5:1033–1040CrossRefGoogle Scholar
  21. Gu W, Zhu X, Futai N, Cho BS, Takayama S (2004) Computerized microfluidic cell culture using elastomeric channels and Braille displays. PNAS 101(45):15861–15866CrossRefGoogle Scholar
  22. Hong JW, Quake SR (2003) Integrated nanoliter systems. Nature BioTechnol 21(10):1179–1183CrossRefGoogle Scholar
  23. Johnson C (2006) Automating the DNA computer to solve n-variable 3-SAT problems. In Proceedings of the 12th international meeting on DNA computing, 360–373Google Scholar
  24. King RD, Whelan KE, Jones FM, Reiser PGK, Bryant CH, Muggleton SH, Kell DB, Oliver SG (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427:247–252CrossRefGoogle Scholar
  25. Kitano H (2002) Computational systems biology. Nature 420:206–210CrossRefGoogle Scholar
  26. Knight T, Sussman G (1998) Cellular gate technology. In Proceedings of the 1st international conference on unconventional models of computationGoogle Scholar
  27. Lin F, Saadi W, Rhee SW, Wang S-J, Mittalb S, Jeon NL (2004) Generation of dynamic temporal and spatial concentration gradients using microfluidic devices. Lab Chip 4:164–167CrossRefGoogle Scholar
  28. Livstone MS, Weiss R, Landweber LF (2006) Automated design and programming of a microfluidic DNA computer. Nat Comput 5:1–13zbMATHCrossRefMathSciNetGoogle Scholar
  29. Fluidigm Corportaion (2006) Fluidigm glossary - Moore’s Law. Website.
  30. McCaskill JS (2001) Optically programming DNA computing in microflow reactors. BioSystems 59:125–138CrossRefGoogle Scholar
  31. Neils C, Tyree Z, Finlayson B, Folch A (2004) Combinatorial mixing of microfluidic streams. Lab Chip 4:342–350CrossRefGoogle Scholar
  32. Ouyang Q, Kaplan PD, Liu S, Libchaber A (1997) DNA solution of the maximal clique problem. Science 278:446–449CrossRefGoogle Scholar
  33. Paik P, Pamula V, Fair R (2003) Rapid droplet mixers for digital microfluidic systems. Lab Chip 3:253–259CrossRefGoogle Scholar
  34. Pisanti N (1998) DNA computing: a survey. Bull EATCS 64:171–187Google Scholar
  35. Pollack M, Fair R, Shenderov A (2000) Electrowetting-based actuation of liquid droplets for microfluidic applications. Appl Phys Lett 77(11):1725–1726CrossRefGoogle Scholar
  36. Ren H, Srinivasan V, Fair R (2003) Design and testing of an interpolating mixing architecture for electrowetting-based droplet-on-chip chemical dilution. Proceedings of the 12th international conference on solid state sensors, actuators, and microsystems, 619–622Google Scholar
  37. Sia SK, Whitesides GM (2003) Microfluidic devices fabricated in poly(dimethylsiloxane) for biological studies. Electrophoresis 24:3563–3576CrossRefGoogle Scholar
  38. Somei K, Kaneda S, Fujii T, Murata S (2005) A microfluidic device for DNA tile self-assembly. In Proceedings of the 11th international meeting on DNA computing, 325–335Google Scholar
  39. Su F, Chakrabarty K (2004) Architectural-level synthesis of digital niicrofluidics-based biochips. In Proceedings of the 2004 international conference on computer aided design, 223–228Google Scholar
  40. Su F, Chakrabarty K (2005) Unified high-level synthesis and module placement for defect-tolerant microfluidic biochips. In Proceedings of the 42nd design automation conference 825–830Google Scholar
  41. Thies W, Urbanski JP, Thorsen T, Amarasinghe S (2006) Abstraction layers for scalable microfluidic biocomputers. In Proceedings of the 12th international meeting on DNA computing, 308–323Google Scholar
  42. Thorsen T, Maerkl S, Quake S (2002) Microfluidic large scale integration. Science 298:580–584CrossRefGoogle Scholar
  43. Urbanski JP, Thies W, Rhodes C, Amarasinghe S, Thorsen T (2006) Digital microfluidics using soft lithography. Lab Chip 6:96–104CrossRefGoogle Scholar
  44. van Noort D (2005) A programmable molecular computer in microreactors. In Proceedings of the 11th international meeting on DNA computing, 365–374Google Scholar
  45. van Noort D, Gast F-U, McCaskill JS (2002) DNA computing in microreactors. In Proceedings of the 8th international meeting on DNA computing, 33–45Google Scholar
  46. van Noort D, Zhang B-T (2004) PDMS valves in DNA computers. In: SPIE international symposium on smart materials, nano-, and micro-smart systems, pp 214–220Google Scholar
  47. Winfree E (2003) DNA computing by self-assembly. The Bridge 33(4):37–38Google Scholar
  48. Winfree E, Liu F, Wenzler L, Seeman N (1998) Design and self-assembly of two-dimensional DNA crystals. Nature 394:539–544CrossRefGoogle Scholar
  49. Yamamoto M, Matsuura N, Shiba T, Kawazoe Y, Ohuchi A (2002) Solutions of shortest path problems by concentration control. In Proceedings of the 7th international meeting on DNA computing, 203–212 Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  • William Thies
    • 1
    Email author
  • John Paul Urbanski
    • 2
  • Todd Thorsen
    • 2
  • Saman Amarasinghe
    • 1
  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Hatsopoulos Microfluids LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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