Encyclopedia of Algorithms

Living Edition
| Editors: Ming-Yang Kao

Active Self-Assembly and Molecular Robotics with Nubots

  • Damien WoodsEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27848-8_672-1

Years and Authors of Summarized Original Work

  • 2013; Woods, Chen, Goodfriend, Dabby, Winfree, Yin

  • 2013; Chen, Xin, Woods

  • 2014; Chen, Doty, Holden, Thachuk, Woods, Yang

Problem Definition

In the theory of molecular-scale self-assembly, large numbers of simple interacting components are designed to come together to build complicated shapes and patterns. Many models of self-assembly, such as the abstract Tile Assembly Model [ 6], are cellular automata-like crystal growth models. Indeed such models have given rise to a rich theory of self-assembly as described elsewhere in this encyclopedia. In biological organisms we frequently see much more sophisticated growth processes, where self-assembly is combined with active molecular components that change internal state and even molecular motors that have the ability to push and pull large structures around. Molecular engineers are now beginning to design and build molecular-scale DNA motors and active self-assembly systems [ 2]. We wish to...

Keywords

Molecular robotics Rigid-body motion Self-assembly 
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Notes

Acknowledgements

A warm thanks to all of my coauthors on this topic and especially to Erik Winfree and Chris Thachuk for their helpful comments. The author is supported by NSF grants 0832824, 1317694, CCF-1219274, and CCF-1162589.

Recommended Reading

  1. 1.
    Adleman LM, Cheng Q, Goel A, Huang MD (2001) Running time and program size for self-assembled squares. In: STOC 2001: proceedings of the 33rd annual ACM symposium on theory of computing, Hersonissos. ACM, pp 740–748Google Scholar
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    Bath J, Turberfield A (2007) DNA nanomachines. Nat Nanotechnol 2:275–284CrossRefGoogle Scholar
  3. 3.
    Chen M, Xin D, Woods D (2013) Parallel computation using active self-assembly. In: DNA19: the 19th international conference on DNA computing and molecular programming. LNCS, vol 8141. Springer, pp 16–30. arxiv preprint arXiv:1405.0527 Google Scholar
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    Chen HL, Doty D, Holden D, Thachuk C, Woods D, Yang CT (2014) Fast algorithmic self-assembly of simple shapes using random agitation. In: DNA20: the 20th international conference on DNA computing and molecular programming. LNCS, vol 8727. Springer, pp 20–36. arxiv preprint: arXiv:1409.4828 Google Scholar
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    Keenan A, Schweller R, Sherman M, Zhong X (2014) Fast arithmetic in algorithmic self-assembly. In: UCNC: the 13th international conference on unconventional computation and natural computation. LNCS, vol 8553. Springer, pp 242–253. arxiv preprint arXiv:1303.2416 [cs.DS]Google Scholar
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    Winfree E (1998) Algorithmic self-assembly of DNA. PhD thesis, California Institute of TechnologyGoogle Scholar
  7. 7.
    Woods D (2015) Intrinsic universality and the computational power of self-assembly. Philos Trans R Soc A: Math Phys Eng Sci 373(2046). doi:10.1098/rsta.2014.0214, ISBN:1471-2962, ISSN:1364-503XGoogle Scholar
  8. 8.
    Woods D, Chen HL, Goodfriend S, Dabby N, Winfree E, Yin P (2013) Active self-assembly of algorithmic shapes and patterns in polylogarithmic time. In: ITCS’13: proceedings of the 4th conference on innovations in theoretical computer science. ACM, pp 353–354. Full version: arXiv:1301.2626 [cs.DS]Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Computer ScienceCalifornia Institute of TechnologyCAUSA