Skip to main content

Time Complexity of Computation and Construction in the Chemical Reaction Network-Controlled Tile Assembly Model

  • Conference paper
  • First Online:
DNA Computing and Molecular Programming (DNA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9818))

Included in the following conference series:

Abstract

In isolation, chemical reaction networks and tile-based self-assembly are well-studied models of chemical computation. Previously, we introduced the chemical reaction network-controlled tile assembly model (CRN-TAM), in which a stochastic chemical reaction network can act as a non-local control and signalling system for tile-based assembly, and showed that the CRN-TAM can perform several tasks related to the simulation of Turing machines and construction of algorithmic shapes with lower space or program complexity than in either of its parent models. Here, we introduce a kinetic variant of the CRN-TAM and investigate the time complexity of computation and construction. We analyze the time complexity of decision problems in the CRN-TAM, and show that decidable languages can be decided as efficiently by CRN-TAM programs as by Turing machines. We also give a lower bound for the space-time complexity of CRN-TAM computation that rules out efficient parallel stack machines. We provide efficient parallel implementations of non-deterministic computations, showing among other things that CRN-TAM programs can decide languages in \(\mathsf {NTIME}(f(n)) \cap \mathsf {coNTIME}(f(n))\) in \(\mathcal {O}(f(n) + n + \log c)\) time with \(1 - \exp (-c)\) probability, using volume exponential in n. Lastly, we provide basic mechanisms for parallel computations that share information and illustrate the limits of parallel computation in the CRN-TAM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    By necessity, a different notion of “encoding” must be used in the aTAM, since building even a \(1 \times n\) rectangle requires \(\varTheta (n)\) tile types [2]. However, the notion used in the aTAM is analogous to our notion of encoding.

References

  1. Adleman, L., Cheng, Q., Goel, A., Huang, M.D.: Running time and program size for self-assembled squares. In: Proceedings of the 33rd Annual ACM Symposium on Theory of Computing, STOC 2001, pp. 740–748 (2001)

    Google Scholar 

  2. Aggarwal, G., Cheng, Q., Goldwasser, M.H., Kao, M.Y., de Espanes, P.M., Schweller, R.T.: Complexities for generalized models of self-assembly. SIAM J. Comput. 34, 1493–1515 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  3. Barish, R.D., Schulman, R., Rothemund, P.W.K., Winfree, E.: An information-bearing seed for nucleating algorithmic self-assembly. Proc. Natl. Acad. Sci. 106, 6054–6059 (2009)

    Article  Google Scholar 

  4. Bennett, C.H.: The thermodynamics of computation - a review. Int. J. Theoret. Phys. 21, 905–940 (1982)

    Article  Google Scholar 

  5. Cardelli, L.: Two-domain DNA strand displacement. Math. Struct. Comput. Sci. 23, 247–271 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  6. Cardelli, L., Zavattaro, G.: On the computational power of biochemistry. In: Horimoto, K., Regensburger, G., Rosenkranz, M., Yoshida, H. (eds.) AB 2008. LNCS, vol. 5147, pp. 65–80. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  7. Chen, Y.J., Dalchau, N., Srinivas, N., Cardelli, L., Soloveichik, D., Seelig, G.: Programmable chemical controllers made from DNA. Nat. Nanotechnol. 8, 755–762 (2013)

    Article  Google Scholar 

  8. Condon, A., Kirkpatrick, B., Maňuch, J.: Reachability bounds for chemical reaction networks and strand displacement systems. Nat. Comput. 13, 499–516 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Doty, D.: Theory of algorithmic self-assembly. Commun. ACM 55, 78–88 (2012)

    Article  Google Scholar 

  10. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340–2361 (1977)

    Article  Google Scholar 

  11. Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annu. Rev. Phys. Chem. 58, 35–55 (2007)

    Article  Google Scholar 

  12. Ke, Y., Ong, L.L., Shih, W.M., Yin, P.: Three-dimensional structures self-assembled from DNA bricks. Science 338, 1177–1183 (2012)

    Article  Google Scholar 

  13. Kurtz, S., Mahaney, S., Royer, J., Simon, J.: Biological computing. In: Complexity Theory Retrospective II, pp. 179–195 (1997)

    Google Scholar 

  14. Lakin, M.R., Phillips, A.: Modelling, simulating and verifying turing-powerful strand displacement systems. In: Cardelli, L., Shih, W. (eds.) DNA 17 2011. LNCS, vol. 6937, pp. 130–144. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  15. Lakin, M.R., Stefanovic, D., Phillips, A.: Modular verification of chemical reaction network encodings via serializability analysis. Theoret. Comput. Sci. 632, 21–42 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  16. Lipton, R.J.: DNA computations can have global memory. In: International Conference on Computer Design: VLSI in Computers and Processor, pp. 344–347 (1996)

    Google Scholar 

  17. Patitz, M.J.: An introduction to tile-based self-assembly and a survey of recent results. Nat. Comput. 13, 195–224 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  18. Phillips, A., Cardelli, L.: A programming language for composable DNA circuits. J. R. Soc. Interface 6, S419–S436 (2009)

    Article  Google Scholar 

  19. Qian, L., Soloveichik, D., Winfree, E.: Efficient turing-universal computation with DNA polymers. In: Sakakibara, Y., Mi, Y. (eds.) DNA 16 2010. LNCS, vol. 6518, pp. 123–140. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Rothemund, P.W.K., Papadakis, N., Winfree, E.: Algorithmic self-assembly of DNA Sierpinski triangles. PLoS Biol. 2, e424 (2004)

    Article  Google Scholar 

  23. Rothemund, P.W.K., Winfree, E.: The program-size complexity of self-assembled squares. In: Proceedings of the 32nd Annual ACM Symposium on Theory of Computing, STOC 2000, pp. 459–468 (2000)

    Google Scholar 

  24. Schiefer, N., Winfree, E.: Universal computation and optimal construction in the chemical reaction network-controlled tile assembly model. In: Phillips, A., Yin, P. (eds.) DNA 2015. LNCS, vol. 9211, pp. 34–54. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  26. Soloveichik, D., Cook, M., Winfree, E., Bruck, J.: Computation with finite stochastic chemical reaction networks. Nat. Comput. 7, 615–633 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  27. Soloveichik, D., Seelig, G., Winfree, E.: DNA as a universal substrate for chemical kinetics. Proc. Natl. Acad. Sci. 107, 5393–5398 (2010)

    Article  Google Scholar 

  28. Soloveichik, D., Winfree, E.: Complexity of self-assembled shapes. SIAM J. Comput. 36, 1544–1569 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  29. Wei, B., Dai, M., Yin, P.: Complex shapes self-assembled from single-stranded DNA tiles. Nature 485, 623–626 (2012)

    Article  Google Scholar 

  30. Winfree, E.: On the computational power of DNA annealing and ligation. In: DNA Computers. DIMACS Series in Discrete Mathematics and Computer Science, vol. 27, pp. 199–221. American Mathematical Society (1996)

    Google Scholar 

  31. Winfree, E., Liu, F., Wenzler, L.A., Seeman, N.C.: Design and self-assembly of two-dimensional DNA crystals. Nature 394, 539–544 (1998)

    Article  Google Scholar 

  32. Winfree, E., Yang, X., Seeman, N.C.: Universal computation via self-assembly of DNA: some theory and experiments. In: DNA Based Computers II. DIMACS Series in Discrete Mathematics and Computer Science, vol. 44, pp. 191–213. American Mathematical Society (1999)

    Google Scholar 

  33. Yin, P., Choi, H.M.T., Calvert, C.R., Pierce, N.A.: Programming biomolecular self-assembly pathways. Nature 451, 318–322 (2008)

    Article  Google Scholar 

  34. Zhang, D.Y., Hariadi, R.F., Choi, H.M.T., Winfree, E.: Integrating DNA strand-displacement circuitry with DNA tile self-assembly. Nat. Commun. 4, Article no. 1965 (2013)

    Google Scholar 

  35. Zhang, D.Y., Turberfield, A.J., Yurke, B., Winfree, E.: Engineering entropy-driven reactions and networks catalyzed by DNA. Science 318, 1121–1125 (2007)

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge financial support from National Science Foundation grant CCF-1317694 and the Soli Deo Gloria Summer Undergraduate Research Fellowship at the California Institute of Technology. We also thank Dave Doty and Damien Woods for their insights.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Erik Winfree .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Schiefer, N., Winfree, E. (2016). Time Complexity of Computation and Construction in the Chemical Reaction Network-Controlled Tile Assembly Model. In: Rondelez, Y., Woods, D. (eds) DNA Computing and Molecular Programming. DNA 2016. Lecture Notes in Computer Science(), vol 9818. Springer, Cham. https://doi.org/10.1007/978-3-319-43994-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43994-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43993-8

  • Online ISBN: 978-3-319-43994-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics