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Combinatorial optimization problems in self-assembly

Published:19 May 2002Publication History

ABSTRACT

Self-assembly is the ubiquitous process by which simple objects autonomously assemble into intricate complexes. It has been suggested that intricate self-assembly processes will ultimately be used in circuit fabrication, nano-robotics, DNA computation, and amorphous computing. In this paper, we study two combinatorial optimization problems related to efficient self-assembly of shapes in the Tile Assembly Model of self-assembly proposed by Rothemund and Winfree [18]. The first is the Minimum Tile Set Problem, where the goal is to find the smallest tile system that uniquely produces a given shape. The second is the Tile Concentrations Problem, where the goal is to decide on the relative concentrations of different types of tiles so that a tile system assembles as quickly as possible. The first problem is akin to finding optimum program size, and the second to finding optimum running time for a "program" to assemble the shape.Self-assembly is the ubiquitous process by which simple objects autonomously assemble into intricate complexes. It has been suggested that intricate self-assembly processes will ultimately be used in circuit fabrication, nano-robotics, DNA computation, and amorphous computing. In this paper, we study two combinatorial optimization problems related to efficient self-assembly of shapes in the Tile Assembly Model of self-assembly proposed by Rothemund and Winfree [18]. The first is the Minimum Tile Set Problem, where the goal is to find the smallest tile system that uniquely produces a given shape. The second is the Tile Concentrations Problem, where the goal is to decide on the relative concentrations of different types of tiles so that a tile system assembles as quickly as possible. The first problem is akin to finding optimum program size, and the second to finding optimum running time for a "program" to assemble the shape.We prove that the first problem is NP-complete in general, and polynomial time solvable on trees and squares. In order to prove that the problem is in NP, we present a polynomial time algorithm to verify whether a given tile system uniquely produces a given shape. This algorithm is analogous to a program verifier for traditional computational systems, and may well be of independent interest. For the second problem, we present a polynomial time $O(\log n)$-approximation algorithm that works for a large class of tile systems that we call partial order systems.

References

  1. H. Abelson, D, Allen, D. Coore, C. Hanson, G. Homsy, T. Knight, R. Nagpal, E. Rauch, G. Sussman and R. Weiss. Amorphous Computing. Communications of the ACM vol 43, p 74--82, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. L. Adleman. Towards a (MATH)ematical theory of self-assembly. Technical Report 00-722, Department of Computer Science, University of Southern California, (2000).Google ScholarGoogle Scholar
  3. L. Adleman, Q. Cheng, A. Goel, M. Huang and Hal Wasserman. Linear Self-Assemblies: Equilibria, Entropy, and Convergence Rates. Unpublished.Google ScholarGoogle Scholar
  4. L. Adleman, Q. Cheng, A. Goel and M. Huang, Running time and program size for self-assembled squares, ACM Symposium on Theory of Computing (STOC) 2001. pages 740--748. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Cook, D. Kempe, P. Rothemund, E. Winfree.Google ScholarGoogle Scholar
  6. M. Cook, D. Kempe, P. Rothemund, E. Winfree. Personal communication.Google ScholarGoogle Scholar
  7. M. Gomez-Lopez, J. Preece, and J. Stoddart, The art and science of self-assembling molecular machines, Nanotechnology, Vol. 7, No. 3, pp. 183--192, September 1996.Google ScholarGoogle Scholar
  8. D. Gracias, J. Tien, T. Breen, C. Hsu and G. Whitesides, Forming Electrical Networks in Three Dimensions by Self-Assembly, Science 289, 5482, p 1170--1173 (2000).Google ScholarGoogle Scholar
  9. M. Grotschel, L. Lovasz, and A. Schrijver. Geometric Algorithms and Combinatorial Optimization. Springer-Verlag, 1993 (2nd corrected edition).Google ScholarGoogle ScholarCross RefCross Ref
  10. L. Khachian, A Polynomial Algorithm in Linear Programming. Soviet (MATH)ematics Doklady 20, 191--194 (1979).Google ScholarGoogle Scholar
  11. G. Lopinski, D. Wayner and R. Wolkow. Self-Directed Growth of Molecular Nano-Structures on Silicon. Nature 406, 48 (2000).Google ScholarGoogle Scholar
  12. G. Louth, M. Mitzenmacher and F. Kelly. Computational complexity of loss networks, Theoretical Computer Science journal, (1994) vol 125, p 45--59. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. C. Mao, T. LaBean, J. Reif and N. Seeman. Logical computation using algorithmic self-assembly of DNA triple-crossover molecules. Nature.407, 493--496. (2000).Google ScholarGoogle ScholarCross RefCross Ref
  14. Lagoudakis and T. LaBean. 2D DNA Self-Assembly for Satisfiability. in DIMACS Series in Discrete (MATH)ematics and Theoretical Computer Science 1999, Volume 54, Editors: E. Winfree and D.K. Gifford, Proceedings of the 5th DIMACS Workshop on DNA Based Computers; MIT: Cambridge. ISBN 0-8218-2053-2Google ScholarGoogle Scholar
  15. J. Reif. Local Parallel Biomolecular Computation. Third Annual DIMACS Workshop on DNA Based Computers, University of Pennsylvania, June 23--26, 1997. Published in DNA Based Computers, III, DIMACS Series in Discrete (MATH)ematics and Theoretical Computer Science, Vol 48 (ed. H. Rubin), American (MATH)ematical Society, p 217--254, (1999).Google ScholarGoogle Scholar
  16. P. Rothemund. Using lateral capillary forces to compute by self-assembly. Proceedings of the National Academy of Sciences, vol 9. p 984--989. (2000).Google ScholarGoogle ScholarCross RefCross Ref
  17. P. Rothemund. Theory and Experiments in Algorithmic Self-Assembly University of Southern California Ph.D. Thesis Copyright 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. P. Rothemund and E. Winfree. The program-size complexity of self-assembled squares. ACM Symposium on Theory of Computing (STOC) 2001. pages 459--468. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J.H. Schön, H. Meng and Z. Bao. Self-assembled monolayer organic field-effect transistors. Nature. 413, 713--715. (2001).Google ScholarGoogle Scholar
  20. H. Wang. Proving theorems by pattern recognition. II. Bell Systems Technical Journal, 40:1--42, (1961).Google ScholarGoogle ScholarCross RefCross Ref
  21. E. Winfree, X. Yang and N. Seeman, Universal Computation via Self-assembly of DNA: Some Theory and Experiments, Proceedings of the Second Annual Meeting on DNA Based Computers, Princeton University, June 10--12, (1996).Google ScholarGoogle Scholar
  22. E. Winfree, F. Liu, L. Wenzler, N. Seeman. Design and self-assembly of two-dimensional DNA crystals, 6 pages. (Nature 394, 539--544 (Aug. 6, 1998) Article).Google ScholarGoogle ScholarCross RefCross Ref
  23. E. Winfree. Algorithmic Self-Assembly of DNA, Ph.D. thesis. California Institute of Technology, Pasadena, (1998). Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. G. Whitesides, J. (MATH)ias and Christopher T. Seto. Molecular self-assembly and nanochemistry: a chemical strategy for the synthesis of nanostructures, Science, vol 254, p 1312--1319. Nov 1991.Google ScholarGoogle Scholar

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              cover image ACM Conferences
              STOC '02: Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
              May 2002
              840 pages
              ISBN:1581134959
              DOI:10.1145/509907

              Copyright © 2002 ACM

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              • Published: 19 May 2002

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