Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A spatially localized architecture for fast and modular DNA computing

Abstract

Cells use spatial constraints to control and accelerate the flow of information in enzyme cascades and signalling networks. Synthetic silicon-based circuitry similarly relies on spatial constraints to process information. Here, we show that spatial organization can be a similarly powerful design principle for overcoming limitations of speed and modularity in engineered molecular circuits. We create logic gates and signal transmission lines by spatially arranging reactive DNA hairpins on a DNA origami. Signal propagation is demonstrated across transmission lines of different lengths and orientations and logic gates are modularly combined into circuits that establish the universality of our approach. Because reactions preferentially occur between neighbours, identical DNA hairpins can be reused across circuits. Co-localization of circuit elements decreases computation time from hours to minutes compared to circuits with diffusible components. Detailed computational models enable predictive circuit design. We anticipate our approach will motivate using spatial constraints for future molecular control circuit designs.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Spatial organization controls signal propagation.
Figure 2: Signals propagate along wires of different lengths and orientations.
Figure 3: Design and implementation of a wire crossover.
Figure 4: Elementary logic gates are realized with localized DNA hairpins.
Figure 5: Elementary logic gates are combined into multi-input logic circuits.

Similar content being viewed by others

References

  1. Agapakis, C., Boyle, P. & Silver, P. Natural strategies for the spatial optimization of metabolism in synthetic biology. Nat. Chem. Biol. 8, 527–535 (2012).

    Article  CAS  Google Scholar 

  2. Good, M., Zalatan, J. & Lim, W. Scaffold proteins: hubs for controlling the flow of cellular information. Science 332, 680–686 (2011).

    Article  CAS  Google Scholar 

  3. Morrison, D. & Davis, R. Regulation of MAP kinase signaling modules by scaffold proteins in mammals. Annu. Rev. Cell Dev. Biol. 19, 91–118 (2003).

    Article  CAS  Google Scholar 

  4. Sweetlove, L. J. & Fernie, A. R. The spatial organization of metabolism within the plant cell. Annu. Rev. Plant Biol. 64, 723–746 (2013).

    Article  CAS  Google Scholar 

  5. Ellis, R. J. Macromolecular crowding: an important but neglected aspect of the intracellular environment. Curr. Opin. Struc. Biol. 11, 114–119 (2001).

    Article  CAS  Google Scholar 

  6. Konopka, M. C., Shkel, I. A., Cayley, S. & Record, M. T. Crowding and confinement effects on protein diffusion in vivo. J. Bacteriol. 188, 6115–6123 (2006).

    Article  CAS  Google Scholar 

  7. Polka, J. K., Hays, S. G. & Silver, P. A. Building spatial synthetic biology with compartments, scaffolds, and communities. Cold Spring Harb. Perspect. Biol. 8, a024018 (2016).

    Article  Google Scholar 

  8. Park, S.-H., Zarrinpar, A. & Lim, W. Rewiring MAP kinase pathways using alternative scaffold assembly mechanisms. Science 299, 1061–1064 (2003).

    Article  CAS  Google Scholar 

  9. Delebecque, C., Lindner, A., Silver, P. & Aldaye, F. Organization of intracellular reactions with rationally designed RNA assemblies. Science 333, 470–474 (2011).

    Article  CAS  Google Scholar 

  10. Rothemund, P. W. Folding DNA to create nanoscale shapes and patterns. Nature 440, 297–302 (2006).

    Article  CAS  Google Scholar 

  11. Wickham, S. F. et al. A DNA-based molecular motor that can navigate a network of tracks. Nat. Nanotech. 7, 169–173 (2012).

    Article  CAS  Google Scholar 

  12. Muscat, R. A., Bath, J. & Turberfield, A. J. A programmable molecular robot. Nano Lett. 11, 982–987 (2011).

    Article  CAS  Google Scholar 

  13. Gu, H., Chao, J., Xiao, S.-J. & Seeman, N. A proximity-based programmable DNA nanoscale assembly line. Nature 465, 202–205 (2010).

    Article  CAS  Google Scholar 

  14. Lund, K. et al. Molecular robots guided by prescriptive landscapes. Nature 465, 206–210 (2010).

    Article  CAS  Google Scholar 

  15. Boemo, M. A., Lucas, A. E., Turberfield, A. J. & Cardelli, L. The formal language and design principles of autonomous DNA walker circuits. ACS Synth. Biol. 5, 878–884 (2016).

    Article  CAS  Google Scholar 

  16. Dannenberg, F., Kwiatkowska, M., Thachuk, C. & Turberfield, A. DNA walker circuits: computational potential, design, and verification. Nat. Comput. 14, 195–211 (2015).

    Article  CAS  Google Scholar 

  17. Mo, D., Lakin, M. & Stefanovic, D. Scalable design of logic circuits using an active molecular spider system. In Proc. 10th International Conference on Information Processing in Cells and Tissues (eds Lones, M., Tyrrell, A., Smith, S. & Fogel, G.) 13–28 (Lecture Notes in Computer Science 9303, Springer, 2015).

  18. Yurke, B., Turberfield, A., Mills, A., Simmel, F. & Neumann, J. A DNA-fuelled molecular machine made of DNA. Nature 406, 605–608 (2000).

    Article  CAS  Google Scholar 

  19. Zhang, D. Y. & Seelig, G. Dynamic DNA nanotechnology using strand-displacement reactions. Nat. Chem. 3, 103–113 (2011).

    Article  CAS  Google Scholar 

  20. Seelig, G., Soloveichik, D., Zhang, D. Y. & Winfree, E. Enzyme-free nucleic acid logic circuits. Science 314, 1585–1588 (2006).

    Article  CAS  Google Scholar 

  21. Qian, L. & Winfree, E. Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011).

    Article  CAS  Google Scholar 

  22. Chen, Y.-J. J. et al. Programmable chemical controllers made from DNA. Nat. Nanotech. 8, 755–762 (2013).

    Article  CAS  Google Scholar 

  23. Qian, L., Winfree, E. & Bruck, J. Neural network computation with DNA strand displacement cascades. Nature 475, 368–372 (2011).

    Article  CAS  Google Scholar 

  24. Chirieleison, S. M., Allen, P. B., Simpson, Z. B. & Ellington, A. D. Pattern transformation with DNA circuits. Nat. Chem. 5, 1000–1005 (2013).

    Article  CAS  Google Scholar 

  25. Muscat, R., Strauss, K., Ceze, L. & Seelig, G. DNA-based molecular architecture with spatially localized components. ACM SIGARCH Comp. Architect. News - ICSA '13 41, 177–188 (2013).

    Article  Google Scholar 

  26. Chandran, H., Gopalkrishnan, N., Phillips, A. & Reif, J. in DNA Computing and Molecular Programming Vol. 6937 (eds Cardelli, L. & Shih, W.) 64–83 (Springer, 2011).

    Book  Google Scholar 

  27. Dalchau, N., Chandran, H., Gopalkrishnan, N., Phillips, A. & Reif, J. Probabilistic analysis of localized DNA hybridization circuits. ACS Synth. Biol. 4, 898–913 (2015).

    Article  CAS  Google Scholar 

  28. Qian, L . & Winfree, E. in DNA Computing and Molecular Programming (eds Murata, S. & Kobayashi, S.) 114–131 (Springer, 2014).

  29. Teichmann, M., Kopperger, E. & Simmel, F. C. Robustness of localized DNA strand displacement cascades. ACS Nano 8, 8487–8496 (2014).

    Article  CAS  Google Scholar 

  30. Dunn, K. E., Trefzer, M. A., Johnson, S. & Tyrrell, A. M. Investigating the dynamics of surface-immobilized DNA nanomachines. Sci. Rep. 6, 29581 (2016).

    Article  CAS  Google Scholar 

  31. Ruiz, I. M. et al. Connecting localized DNA strand displacement reactions. Nanoscale 7, 12970–12978 (2015).

    Article  Google Scholar 

  32. Gerasimova, Y. & Kolpashchikov, D. Towards a DNA nanoprocessor: reusable tile-integrated DNA circuits. Angew. Chem. Int. Ed. 128, 10400–10403 (2016).

    Article  Google Scholar 

  33. Jung, J., Hyun, D. & Shin, Y. in Proceedings of Computer Design (ICCD), 2015 33rd IEEE International Conference 259–265 (IEEE, 2015).

  34. Dirks, R. M. & Pierce, N. A. Triggered amplification by hybridization chain reaction. Proc. Natl Acad. Sci. USA 101, 15275–15278 (2004).

    Article  CAS  Google Scholar 

  35. Genot, A. J., Zhang, D. Y. & Bath, J. Remote toehold: a mechanism for flexible control of DNA hybridization kinetics. J. Am. Chem. Soc. 133, 2177–2182 (2011).

    Article  CAS  Google Scholar 

  36. Lakin, M. R., Petersen, R ., Gray, K. E. & Phillips, A. in DNA Computing and Molecular Programming (eds Murata, S. & Kobayashi, S.) 132–147 (Springer, 2014).

  37. Jungmann, R ., Avendaño, M. S., Dai, M . & Woehrstein, J. B. Quantitative super-resolution imaging with qPAINT. Nat. Methods 13, 439–442 (2016).

    Article  CAS  Google Scholar 

  38. Chen, Y.-J. J., Groves, B., Muscat, R. A. & Seelig, G. DNA nanotechnology from the test tube to the cell. Nat. Nanotech. 10, 748–760 (2015).

    Article  CAS  Google Scholar 

  39. Groves, B ., Chen, Y. J., Zurla, C . & Pochekailov, S. Computing in mammalian cells with nucleic acid strand exchange. Nat. Nanotech. 11, 287–294 (2015).

    Article  Google Scholar 

  40. Hemphill, J. & Deiters, A. DNA computation in mammalian cells: microRNA logic operations. J. Am. Chem. Soc. 135, 10512–10518 (2013).

    Article  CAS  Google Scholar 

  41. He, Y. & Liu, D. Autonomous multistep organic synthesis in a single isothermal solution mediated by a DNA walker. Nat. Nanotech. 5, 778–782 (2010).

    Article  CAS  Google Scholar 

  42. Meng, W. et al. An autonomous molecular assembler for programmable chemical synthesis. Nat. Chem. 8, 542–548 (2016).

    Article  CAS  Google Scholar 

  43. Douglas, S. M., Bachelet, I. & Church, G. M. A logic-gated nanorobot for targeted transport of molecular payloads. Science 335, 831–834 (2012).

    Article  CAS  Google Scholar 

  44. Zadeh, J. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank K. Strauss and L. Ceze for their support in initiating this project and F. Randisi for assistance with oxDNA simulations. This work was supported by National Science Foundation grants CCF-1409831, CCF-1317653 and IIS-1212940 and Office of Naval Research grant N00014-13-1-0880 to G.S. G.C. was partially supported by Microsoft Research Ltd.

Author information

Authors and Affiliations

Authors

Contributions

G.C., N.D., R.A.M., A.P. and G.S. designed the experiments and wrote the paper. G.C. performed the experiments. N.D. and A.P. performed the modelling studies.

Corresponding authors

Correspondence to Andrew Phillips or Georg Seelig.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Information (PDF 5358 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chatterjee, G., Dalchau, N., Muscat, R. et al. A spatially localized architecture for fast and modular DNA computing. Nature Nanotech 12, 920–927 (2017). https://doi.org/10.1038/nnano.2017.127

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nnano.2017.127

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing