Rule-based in vitro molecular classification and visualization

Abstract

Molecular computing using programmable nucleic acids has been attracting attention for use in autonomous sensing systems and information processing systems by interacting with a biological environment. Here, we introduce a rule-based in vitro molecular classification system that can classify disease patterns using several microRNA (miRNA) markers via the assembly of programmed DNA strands. The classification rules were derived by analyzing large-scale miRNA expression data obtained from a public database, and the identified rules were converted into DNA sequences. Classification was performed via the detection of miRNA markers in the rules. The classification results were reported as a binary output pattern according to their hybridization to the rule sequences, which can be conveniently visualized using gold nanoparticle aggregation. Our results demonstrate the utility of in vitro molecular classification by illustrating one of the ways in which molecular computing can be used in future biological and medical applications.

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References

  1. 1.

    Adleman, L.M. Molecular computation of solutions to combinatorial problems. Science 266, 1021–1024 (1994).

    Article  CAS  Google Scholar 

  2. 2.

    Reif, J.H. The emergence of the discipline of biomolecular computation in the US. New Generat. Comput. 20, 217–236 (2002).

    Article  Google Scholar 

  3. 3.

    Amos, M., Paun, G., Rozenberg, G. & Salomaa, A. Topics in the theory of DNA computing. Nat. Comp. 287, 3–38 (2002).

    Google Scholar 

  4. 4.

    Chen, X. & Ellington, A.D. Shaping up nucleic acid computation. Curr. Opin. Biotech. 21, 392–400 (2010).

    Article  CAS  Google Scholar 

  5. 5.

    Lee, J.Y., Shin, S.-Y., Park, T.H. & Zhang, B.-T. Solving traveling salesman problems with DNA molecules encoding numerical values. Biosystems 78, 39–47 (2004).

    Article  CAS  Google Scholar 

  6. 6.

    Benenson, Y., Gil, B., Ben-Dor, U., Adar, R. & Shapiro, E. An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004).

    Article  CAS  Google Scholar 

  7. 7.

    Martinez-Perez, I.M., Zhang, G., Ignatova, Z. & Zimmermann, K.-H. Computational genes: a tool for molecular diagnosis and therapy of aberrant mutational phenotype. BMC Bioinformatics 8, 365 (2007).

    Article  Google Scholar 

  8. 8.

    Rinaudo, K. et al. A universal RNAi-based logic evaluator that operates in mammalian cells. Nat. Biotechnol. 25, 795–801 (2007).

    Article  CAS  Google Scholar 

  9. 9.

    Lee, I.-H. et al. The use of gold nanoparticle aggregation for DNA computing and logic-based biomolecular detection. Nanotechnology 19, 395103 (2008).

    Article  Google Scholar 

  10. 10.

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

    Article  CAS  Google Scholar 

  11. 11.

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

    Article  CAS  Google Scholar 

  12. 12.

    Stojanovic, M.N. & Stefanovic, D. A deoxyribozymebased molecular automaton. Nat. Biotechnol. 21, 1069–1074 (2003).

    Article  CAS  Google Scholar 

  13. 13.

    Macdonald, J. et al. Medium scale integration of molecular logic gates in an automaton. Nano Lett. 6, 2598–2603 (2006).

    Article  CAS  Google Scholar 

  14. 14.

    Pei, R., Matamoros, E., Liu, M., Stefanovic, D. & Stojanovic, M.N. Training a molecular automaton to play a game. Nat. Nanotechnol. 5, 773–777 (2010).

    Article  CAS  Google Scholar 

  15. 15.

    Lund, K. et al. Molecular robots guided by prescriptive landscapes. Nature 465, 206–209 (2010).

    Article  CAS  Google Scholar 

  16. 16.

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

    Article  CAS  Google Scholar 

  17. 17.

    Stojanovic, M. Some experiments and directions in molecular computing and robotics. Israel J. Chem. 51, 99–105 (2011).

    Article  CAS  Google Scholar 

  18. 18.

    Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).

    Article  CAS  Google Scholar 

  19. 19.

    Lee, S.H. et al. Biomolecular theorem proving on a chip: A novel microfluidic solution to a classical logic problem. Lab. Chip 12, 1841–1848 (2012).

    Article  CAS  Google Scholar 

  20. 20.

    Mitchell, T. Machine Learing. McGraw-Hill (1997).

    Google Scholar 

  21. 21.

    Witten, I.H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufmann (2005).

    Google Scholar 

  22. 22.

    Mirkin, C.A., Letsinger, R.L., Mucic, R.C. & Storhoff, J.J. A DNA-based method for rationally assembling nanoparticles into macroscopic materials. Nature 382, 607–609 (1996).

    Article  CAS  Google Scholar 

  23. 23.

    Shin, S.-Y., Lee, I.-H., Kim, D. & Zhang, B.-T. Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing. IEEE T. Evolut. Comput. 9, 143–158 (2005).

    Article  Google Scholar 

  24. 24.

    Owczarzy, R. et al. IDT SciTools: a suite for analysis and design of nucleic acid oligomers. Nucleic Acids Res. 36, W163–W169 (2008).

    Article  CAS  Google Scholar 

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Correspondence to Byoung-Tak Zhang.

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These authors contributed equally to this work.

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Shin, SY., Yang, KA., Lee, IH. et al. Rule-based in vitro molecular classification and visualization. BioChip J 7, 29–37 (2013). https://doi.org/10.1007/s13206-013-7105-z

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Keywords

  • In vitro classification
  • Molecular classification
  • DNA computing
  • Nanoparticle self-assembly
  • Rule-based system