Skip to main content
SpringerLink
Log in

Exploring programmable self-assembly in non-DNA based molecular computing

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Self-assembly is a phenomenon observed in nature at all scales where autonomous entities build complex structures, without external influences nor centralised master plan. Modelling such entities and programming correct interactions among them is crucial for controlling the manufacture of desired complex structures at the molecular and supramolecular scale. This work focuses on a programmability model for non DNA-based molecules and complex behaviour analysis of their self-assembled conformations. In particular, we look into modelling, programming and simulation of porphyrin molecules self-assembly and apply Kolgomorov complexity-based techniques to classify and assess simulation results in terms of information content. The analysis focuses on phase transition, clustering, variability and parameter discovery which as a whole pave the way to the notion of complex systems programmability.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Notes

  1. Available at http://pmt.sourceforge.net/pngcrush/ (accessed on October 14, 2012) set to maximum compression.

References

  • Adleman LM, Cheng Q, Goel A, Huang MD (2001) Running time and program size for self-assembled squares. In: Symposium on theory of computing, ACM, pp 740–748

  • Adleman LM, Cheng Q, Goel A, Huang MD, Kempe D, Moisset de Espanés P, Rothemund PWK (2002) Combinatorial optimization problems in self-assembly. In: Symposium on theory of computing, ACM, pp 23–32

  • Anderberg MR (1973) Cluster analysis for applications. Academic Press, New York

    MATH  Google Scholar 

  • Bruschi P, Cagnoni P, Nannini A (1997) Temperature-dependent Monte Carlo simulations of thin metal film growth and percolation. Phys Rev B: Condens Matter 55(12):7955–7963

    Article  Google Scholar 

  • Chaitin GJ (1969) On the length of programs for computing finite binary sequences: statistical considerations. J ACM 1:145–159

    Article  MathSciNet  Google Scholar 

  • Cheng Q, Goel A, Moisset de Espanés P (2004) Optimal self-assembly of counters at temperature two. In: Foundations of Nanoscience: self-assembled architectures and devices

  • Cilibrasi R, Vitányi PMB (2005) Clustering by compression. IEEE Trans Inf Theory 51(4):1523–1545

    Article  Google Scholar 

  • Dubacq JC, Durand B, Formenti E (2001) Kolmogorov complexity and cellular automata classification. Theoret Comput Sci 1–2:271–285

    Article  MathSciNet  Google Scholar 

  • Moisset de Espanés P (2008) Computer aided search for optimal self-assembly systems. In: Krasnogor et al. (ed) pp 225–243

  • Ferragina P, Giancarlo R, Greco V, Manzini G, Valiente G (2007) Compression-based classification of biological sequences and structures via the universal similarity metric: experimental assessment. BMC Bioinform 8(1):252

    Article  Google Scholar 

  • Flenner E, Janosi L, Barz B, Neagu A, Forgacs G, Kosztin I (2012) Kinetic Monte Carlo and cellular particle dynamics simulations of multicellular systems. Phys Rev E 85(3):031907–031916

    Article  Google Scholar 

  • Goldin D, Wegner P (2006) Interactive computation. In: Goldin D, Smolka S, Wegner P (eds) Principles of interactive computation, Springer, Berlin, p 25–37

    Chapter  Google Scholar 

  • Keogh E, Lonardi S, Ratanamahatana CA, Wei L, Lee SH, Handley J (2007) Compression-based data mining of sequential data. Data Min Knowl Disc 14(1):99–129

    Article  MathSciNet  Google Scholar 

  • Kolmogorov AN (1965) Three approaches to the quantitative definition of information. Probl Inf Transm 1:1–7

    Google Scholar 

  • Krasnogor N, Gustafson S, Pelta DA, Verdegay JL (2008) Systems self-assembly: multidisciplinary snapshots, studies in multidisciplinarity, vol 5. Elsevier, Amsterdam

    Google Scholar 

  • Li M, Chen X, Li X, Ma B, Vitányi PMB (2004) The similarity metric. IEEE Trans Inf Theory 50(12):3250–3264

    Article  Google Scholar 

  • Mao C, LaBean T, Reif JH (2000) Logical computation using algorithmic self-assembly of DNA triple crossover molecules. Nature 407:493–496

    Article  Google Scholar 

  • Mealy GH (1955) A method for synthesizing sequential circuits. Bell Syst Tech J 34(5):1045–1079

    Article  MathSciNet  Google Scholar 

  • Pelesko JA (2007) Self assembly: the science of things that put themselves together. Chapman & Hall/CRC, London

    Book  Google Scholar 

  • Rothemund PWK (2000) Using lateral capillary forces to compute by self-assembly. Proc Natl Acad Sci USA 97(3):984–989

    Article  Google Scholar 

  • Rothemund PWK, Winfree E (2000) The program-size complexity of self-assembled squares. In: Symposium on theory of computing, ACM, pp 459–468

  • Siepmann P, Terrazas G, Krasnogor N (2006) Evolutionary design for the behaviour of cellular automaton-based complex systems. In: Adaptive computing in design and manufacture, pp 199–208

  • Siepmann P, Martin CP, Vancea I, Moriarty PJ, Krasnogor N (2007) A genetic algorithm approach to probing the evolution of self-organised nanostructured systems. Nano Lett 7:1985–1990

    Article  Google Scholar 

  • Soloveichik D, Winfree E (2005) The computational power of Benenson automata. Theoret Comput Sci 344(2–3):279–297

    Article  MathSciNet  MATH  Google Scholar 

  • Terrazas G, Krasnogor N, Kendall G, Gheorghe M (2005) Automated tile design for self-assembly conformations. In: IEEE congress on evolutionary computation, vol 2. IEEE Press, Los Alamitos, p 1808–1814

  • Terrazas G, Gheorghe M, Kendall G, Krasnogor N (2007a) Evolving tiles for automated self-assembly design. In: IEEE congress on evolutionary computation, IEEE Press, Los Alamitos, p 2001–2008

  • Terrazas G, Siepmann P, Kendall G, Krasnogor N (2007b) An evolutionary methodology for the automated design of cellular automaton-based complex systems. J Cell Autom 2(1):77–102

    MathSciNet  MATH  Google Scholar 

  • Terrazas G, Lui LT, Krasnogor N (2013) Spatial computation and algorithmic information content in non-DNA based molecular self-assembly. In: Spatial computing, p 85–90

  • Vitányi PMB (2011) Information distance in multiples. IEEE Trans Inf Theory 57(4):2451–2456

    Article  Google Scholar 

  • Vitányi PMB (2012) Information distance: new developments. In: Information theoretic methods in science and engineering, p 71–74

  • Wang H (1961) Proving theorems by pattern recognition. Bell Syst Tech J 40:1–42

    Article  Google Scholar 

  • Winfree E (1996) On the computational power of DNA annealing and ligation. In: Discrete mathematics and theoretical computer science, vol 27. American Mathematical Society, Providence, p 199–221

  • Winfree E, Bekbolatov R (2003) Proofreading tile sets: error correction for algorithmic self-assembly. DNA Comput 2943:126–144

    Article  MathSciNet  Google Scholar 

  • Winfree E, Liu F, Wenzler LA, Seeman NC (1998) Design and self-assembly of two-dimensional DNA crystals. Nature 394:539–544

    Article  Google Scholar 

  • Woolley RAJ, Stirling J, Radocea A, Krasnogor N, Moriarty P (2011) Automated probe microscopy via evolutionary optimization at the atomic scale. Appl Phys Lett 98(25):253,104–253,104

    Article  Google Scholar 

  • Zenil H (2010) Compression-based investigation of the dynamical properties of cellular automata and other systems. Complex Syst 19(1):1–28

    MathSciNet  MATH  Google Scholar 

  • Zenil H (2012) On the dynamic qualitative behavior of universal computation. Complex Syst 20(3):265–278

    MathSciNet  Google Scholar 

Download references

Acknowledgments

This work is supported by EPSRC grants EPSRC EP/J004111/1 and EP/H010432/1 Evolutionary Optimisation of Self-Assembly Nano-Design (ExIStENcE). The authors acknowledge the insightful discussions on the chemistry and physics of porphyrins with Prof. N. Champness, Prof. A. Moriarty and Prof. P. Beton from the University of Nottingham.

Author information

Authors and Affiliations

  1. Institute for Advanced Manufacturing, Faculty of Engineering, University of Nottingham, Nottingham, UK

    Germán Terrazas

  2. School of Computing Science and Centre for Bacterial Cell Biology, Newcastle University, Newcastle upon Tyne, UK

    Natalio Krasnogor

  3. Behavioural and Evolutionary Theory Lab, Department of Computer Science, Kroto Research Institute, University of Sheffield, Sheffield, UK

    Hector Zenil

Authors
  1. Germán Terrazas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hector Zenil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natalio Krasnogor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natalio Krasnogor.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Terrazas, G., Zenil, H. & Krasnogor, N. Exploring programmable self-assembly in non-DNA based molecular computing. Nat Comput 12, 499–515 (2013). https://doi.org/10.1007/s11047-013-9397-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-013-9397-2

Keywords

Search

Navigation