An autonomous molecular computer for logical control of gene expression

Abstract

Early biomolecular computer research focused on laboratory-scale, human-operated computers for complex computational problems1,2,3,4,5,6,7. Recently, simple molecular-scale autonomous programmable computers were demonstrated8,9,10,11,12,13,14,15 allowing both input and output information to be in molecular form. Such computers, using biological molecules as input data and biologically active molecules as outputs, could produce a system for ‘logical’ control of biological processes. Here we describe an autonomous biomolecular computer that, at least in vitro, logically analyses the levels of messenger RNA species, and in response produces a molecule capable of affecting levels of gene expression. The computer operates at a concentration of close to a trillion computers per microlitre and consists of three programmable modules: a computation module, that is, a stochastic molecular automaton12,13,14,15,16,17; an input module, by which specific mRNA levels or point mutations regulate software molecule concentrations, and hence automaton transition probabilities; and an output module, capable of controlled release of a short single-stranded DNA molecule. This approach might be applied in vivo to biochemical sensing, genetic engineering and even medical diagnosis and treatment. As a proof of principle we programmed the computer to identify and analyse mRNA of disease-related genes18,19,20,21,22 associated with models of small-cell lung cancer and prostate cancer, and to produce a single-stranded DNA molecule modelled after an anticancer drug.

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Figure 1: Logical design and logical operation of the molecular computer.
Figure 2: Operation of the molecular computer.
Figure 3: Experimental demonstration of diagnosis.
Figure 4: Experimental demonstration of drug administration.

References

  1. 1

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

    ADS  Article  Google Scholar 

  2. 2

    Lipton, R. J. DNA solution of hard computational problem. Science 268, 542–545 (1995)

    ADS  CAS  Article  Google Scholar 

  3. 3

    Ouyang, Q., Kaplan, P. D., Liu, S. & Libchaber, A. DNA solution of the maximal clique problem. Science 278, 446–449 (1997)

    ADS  CAS  Article  Google Scholar 

  4. 4

    Khodor, J. & Gifford, D. K. Design and implementation of computational systems based on programmed mutagenesis. Biosystems 52, 93–97 (1999)

    CAS  Article  Google Scholar 

  5. 5

    Faulhammer, D., Cukras, A. R., Lipton, R. J. & Landweber, L. F. Molecular computation: RNA solutions to chess problems. Proc. Natl Acad. Sci. USA 97, 1385–1389 (2000)

    ADS  CAS  Article  Google Scholar 

  6. 6

    Liu, Q. et al. DNA computing on surfaces. Nature 403, 175–179 (2000)

    ADS  CAS  Article  Google Scholar 

  7. 7

    Ruben, A. J. & Landweber, L. F. The past, present and future of molecular computing. Nature Rev. Mol. Cell Biol. 1, 69–72 (2000)

    CAS  Article  Google Scholar 

  8. 8

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

    ADS  CAS  Article  Google Scholar 

  9. 9

    Mao, C., LaBean, T. H., Reif, J. H. & Seeman, N. C. Logical computation using algorithmic self-assembly of DNA triple-crossover molecules. Nature 407, 493–496 (2000)

    ADS  CAS  Article  Google Scholar 

  10. 10

    Sakamoto, K. et al. State transitions by molecules. Biosystems 52, 81–91 (1999)

    CAS  Article  Google Scholar 

  11. 11

    Sakamoto, K. et al. Molecular computation by DNA hairpin formation. Science 288, 1223–1226 (2000)

    ADS  CAS  Article  Google Scholar 

  12. 12

    Benenson, Y. et al. Programmable and autonomous computing machine made of biomolecules. Nature 414, 430–434 (2001)

    ADS  CAS  Article  Google Scholar 

  13. 13

    Benenson, Y., Adar, R., Paz-Elizur, T., Livneh, Z. & Shapiro, E. DNA molecule provides a computing machine with both data and fuel. Proc. Natl Acad. Sci. USA 100, 2191–2196 (2003)

    ADS  CAS  Article  Google Scholar 

  14. 14

    Adar, R. et al. Stochastic computing with biomolecular automata. Proc. Natl Acad. Sci. USA (submitted)

  15. 15

    Benenson, Y. & Shapiro, E. in Dekker Encyclopedia of Nanoscience and Nanotechnology (eds Schwarz, J. A., Contescu, C. I. & Putyera, K.) 2043–2056 (Dekker, New York, 2004)

    Google Scholar 

  16. 16

    Rabin, M. O. Probabilistic automata. Inform. Contr. 6, 230–245 (1963)

    Article  Google Scholar 

  17. 17

    Bar-Ziv, R., Tlusty, T. & Libchaber, A. Protein-DNA computation by stochastic assembly cascade. Proc. Natl Acad. Sci. USA 99, 11589–11592 (2002)

    ADS  CAS  Article  Google Scholar 

  18. 18

    Sidransky, D. Emerging molecular markers of cancer. Nature Rev. Cancer 2, 210–219 (2002)

    CAS  Article  Google Scholar 

  19. 19

    Pedersen, N. et al. Transcriptional gene expression profiling of small cell lung cancer cells. Cancer Res. 63, 1943–1953 (2003)

    CAS  PubMed  Google Scholar 

  20. 20

    Dhanasekaran, S. M. et al. Delineation of prognostic biomarkers in prostate cancer. Nature 412, 822–826 (2001)

    ADS  CAS  Article  Google Scholar 

  21. 21

    Takahashi, T. et al. The p53 gene is very frequently mutated in small-cell lung cancer with a distinct nucleotide substitution patter. Oncogene 6, 1775–1778 (1991)

    CAS  PubMed  Google Scholar 

  22. 22

    Thorns, C., Gaiser, T., Lange, K., Metz, H. & Feller, A. C. cDNA arrays: Gene expression profiles of Hodgkin's disease and anaplastic large cell lymphoma cell lines. Pathol. Int. 52, 578–585 (2002)

    CAS  Article  Google Scholar 

  23. 23

    Ledley, R. S. & Lusted, L. B. Reasoning foundation of medical diagnosis. Science 130, 9–21 (1959)

    ADS  CAS  Article  Google Scholar 

  24. 24

    Holzer, S., Fremgen, A. M., Hundahl, S. A. & Dudeck, J. Analysis of medical-decision making and the use of standards of care in oncology. J. Am. Med. Inf. Assoc. (Suppl. S) 364–368 (2000)

  25. 25

    Capoulade, C. et al. Apoptosis of tumoral and nontumoral lymphoid cells is induced by both mdm2 and p53 antisense oligodeoxynucleotides. Blood 97, 1043–1049 (2001)

    CAS  Article  Google Scholar 

  26. 26

    Holmlund, J. T. Applying antisense technology. Ann. NY Acad. Sci. 1002, 244–251 (2003)

    ADS  CAS  Article  Google Scholar 

  27. 27

    Klasa, R. J., Gillum, A. M., Klem, R. E. & Frankel, S. R. Oblimersen Bcl-2 antisense: Facilitating apoptosis in anticancer treatment. Antisense Nucleic Acid Drug Dev. 12, 193–213 (2002)

    CAS  Article  Google Scholar 

  28. 28

    Yurke, B., Turberfield, A. J., Mills, A. P., Simmel, F. C. & Neumann, J. L. A DNA-fuelled molecular machine made of DNA. Nature 406, 605–608 (2000)

    ADS  CAS  Article  Google Scholar 

  29. 29

    Stojanovic, M. N. & Stefanovic, D. A deoxyribozyme-based molecular automaton. Nature Biotechnol. 21, 1069–1074 (2003)

    CAS  Article  Google Scholar 

  30. 30

    Balzani, V., Credi, A. & Venturi, M. Molecular logic circuits. Chemphyschem 4, 49–59 (2003)

    CAS  Article  Google Scholar 

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Acknowledgements

We thank K. Katzav for the design and preparation of the figures; G. Linshiz for discussion and help in oligonucleotide purification; Z. Livneh for encouraging us to pursue this research direction; A. Regev for critical review and suggestions; and M. Vardi for discussion and references. This work was supported by the Moross Institute for Cancer Research, Israeli Science Foundation and the Minerva Foundation.

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Correspondence to Ehud Shapiro.

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Supplementary information

Supplementary Methods

This file contains all the experimental protocols relevant to the main text, the detailed description of the automata design and the deoxyoligonucleotide sequences of its parts. (DOC 146 kb)

Supplementary Data

Three additional experiments are described: 1. Demonstration of the automaton ability to detect a point mutation; 2. Adjusting confidence in a positive diagnosis for various concentrations of the molecular indicator 3. The release of an approved ssDNA drug (Vitravene). (DOC 74 kb)

Supplementary Notes

A probabilistic framework for the diagnostic process is given. (DOC 19 kb)

Supplementary Figure 1

Architecture of the molecular finite automaton, featuring its input, software and hardware components. (JPG 91 kb)

Supplementary Figure 2

Molecular components of the computer. (JPG 145 kb)

Supplementary Figure 3

Calibration curve showing regulation of probability of Yes output state in a single-step computation by a pTRI-Xef generic mRNA marker. (JPG 96 kb)

Supplementary Figure 4

Selectivity of the diagnostic automata for their disease models. (JPG 102 kb)

Supplementary Figure 5

Release of the approved antisense drug. (JPG 103 kb)

Supplementary Figure Legends (DOC 60 kb)

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Benenson, Y., Gil, B., Ben-Dor, U. et al. An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004). https://doi.org/10.1038/nature02551

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