Synthetic analog computation in living cells (original) (raw)

Nature volume 497, pages 619–623 (2013)Cite this article

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Abstract

A central goal of synthetic biology is to achieve multi-signal integration and processing in living cells for diagnostic, therapeutic and biotechnology applications1. Digital logic has been used to build small-scale circuits, but other frameworks may be needed for efficient computation in the resource-limited environments of cells2,3. Here we demonstrate that synthetic analog gene circuits can be engineered to execute sophisticated computational functions in living cells using just three transcription factors. Such synthetic analog gene circuits exploit feedback to implement logarithmically linear sensing, addition, ratiometric and power-law computations. The circuits exhibit Weber’s law behaviour as in natural biological systems4, operate over a wide dynamic range of up to four orders of magnitude and can be designed to have tunable transfer functions. Our circuits can be composed to implement higher-order functions that are well described by both intricate biochemical models and simple mathematical functions. By exploiting analog building-block functions that are already naturally present in cells3,5, this approach efficiently implements arithmetic operations and complex functions in the logarithmic domain. Such circuits may lead to new applications for synthetic biology and biotechnology that require complex computations with limited parts, need wide-dynamic-range biosensing or would benefit from the fine control of gene expression.

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Figure 1: Positive-feedback linearization of gene circuits for wide-dynamic-range analog computation.

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Figure 2: Analog behaviour versus digital.

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Figure 3: Log-domain analog computation.

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References

  1. Chen, Y. Y., Galloway, K. E. & Smolke, C. D. Synthetic biology: advancing biological frontiers by building synthetic systems. Genome Biol. 13, 240 (2012)
    Article Google Scholar
  2. Cardinale, S. & Arkin, A. P. Contextualizing context for synthetic biology: identifying causes of failure of synthetic biological systems. Biotechnol. J. 7, 856–866 (2012)
    Article CAS Google Scholar
  3. Sarpeshkar, R. Analog versus digital: extrapolating from electronics to neurobiology. Neural Comput. 10, 1601–1638 (1998)
    Article CAS Google Scholar
  4. Ferrell, J. E. Signaling motifs and Weber’s law. Mol. Cell 36, 724–727 (2009)
    Article CAS Google Scholar
  5. Sarpeshkar, R. Ultra Low Power Bioelectronics: Fundamentals, Biomedical Applications, and Bio-Inspired Systems 651–694, 753–786 (Cambridge Univ. Press, 2010)
    Book Google Scholar
  6. Sprinzak, D. et al. _Cis_-interactions between Notch and Delta generate mutually exclusive signalling states. Nature 465, 86–90 (2010)
    Article ADS CAS Google Scholar
  7. Canton, B., Labno, A. & Endy, D. Refinement and standardization of synthetic biological parts and devices. Nature Biotechnol. 26, 787–793 (2008)
    Article CAS Google Scholar
  8. Giorgetti, L. et al. Noncooperative interactions between transcription factors and clustered DNA binding sites enable graded transcriptional responses to environmental inputs. Mol. Cell 37, 418–428 (2010)
    Article CAS Google Scholar
  9. Clark, B. & Hausser, M. Neural coding: hybrid analog and digital signalling in axons. Curr. Biol. 16, R585–R588 (2006)
    Article CAS Google Scholar
  10. Daniel, R., Woo, S. S., Turicchia, L. & Sarpeshkar, R. in Biomedical Circuits and Systems Conference (BioCAS 2011) 333–336 (IEEE, 2011)
    Book Google Scholar
  11. Tavakoli, M. & Sarpeshkar, R. A sinh resistor and its application to tanh linearization. IEEE J. Solid-State Circuits 40, 536–543 (2005)
    Article ADS Google Scholar
  12. Wild, J., Hradecna, Z. & Szybalski, W. Conditionally amplifiable BACs: switching from single-copy to high-copy vectors and genomic clones. Genome Res. 12, 1434–1444 (2002)
    Article CAS Google Scholar
  13. Qian, L. & Winfree, E. Scaling up digital circuit computation with DNA strand displacement cascades. Science 332, 1196–1201 (2011)
    Article ADS CAS Google Scholar
  14. Stricker, J. et al. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008)
    Article ADS CAS Google Scholar
  15. Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000)
    ADS CAS PubMed Google Scholar
  16. McMillen, D., Kopell, N., Hasty, J. & Collins, J. J. Synchronizing genetic relaxation oscillators by intercell signaling. Proc. Natl Acad. Sci. USA 99, 679–684 (2002)
    Article ADS CAS Google Scholar
  17. Madar, D., Dekel, E., Bren, A. & Alon, U. Negative auto-regulation increases the input dynamic-range of the arabinose system of Escherichia coli. BMC Syst. Biol. 5, 111 (2011)
    Article Google Scholar
  18. Nevozhay, D., Adams, R. M., Murphy, K. F., Josić, K. & Balázsi, G. Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression. Proc. Natl Acad. Sci. USA 106, 5123–5128 (2009)
    Article ADS CAS Google Scholar
  19. Shen-Orr, S. S., Milo, R., Mangan, S. & Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genet. 31, 64–68 (2002)
    Article CAS Google Scholar
  20. You, L., Cox, R. S., Weiss, R. & Arnold, F. H. Programmed population control by cell–cell communication and regulated killing. Nature 428, 868–871 (2004)
    Article ADS CAS Google Scholar
  21. Tabor, J. J. et al. A synthetic genetic edge detection program. Cell 137, 1272–1281 (2009)
    Article Google Scholar
  22. Tamsir, A., Tabor, J. J. & Voigt, C. A. Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature 469, 212–215 (2011)
    Article ADS CAS Google Scholar
  23. Auslander, S., Auslander, D., Muller, M., Wieland, M. & Fussenegger, M. Programmable single-cell mammalian biocomputers. Nature 487, 123–127 (2012)
    Article ADS Google Scholar
  24. Isaacs, F. J. et al. Engineered riboregulators enable post-transcriptional control of gene expression. Nature Biotechnol. 22, 841–847 (2004)
    Article CAS Google Scholar
  25. Win, M. N. & Smolke, C. D. Higher-order cellular information processing with synthetic RNA devices. Science 322, 456–460 (2008)
    Article ADS CAS Google Scholar
  26. Xie, Z., Wroblewska, L., Prochazka, L., Weiss, R. & Benenson, Y. Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science 333, 1307–1311 (2011)
    Article ADS CAS Google Scholar
  27. Khalil, A. et al. A synthetic biology framework for programming eukaryotic transcription functions. Cell 150, 647–658 (2012)
    Article CAS Google Scholar
  28. Dueber, J. E., Yeh, B. J., Chak, K. & Lim, W. A. Reprogramming control of an allosteric signaling switch through modular recombination. Science 301, 1904–1908 (2003)
    Article ADS CAS Google Scholar
  29. Hahnloser, R. H. R., Sarpeshkar, R., Mahowald, M. A., Douglas, R. J. & Seung, H. S. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405, 947–951 (2000)
    Article ADS CAS Google Scholar
  30. Lu, T. K., Khalil, A. S. & Collins, J. J. Next-generation synthetic gene networks. Nature Biotechnol. 27, 1139–1150 (2009)
    Article CAS Google Scholar

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Acknowledgements

We would like to thank J. Nungesser for assistance with figures and members of the Lu and Sarpeshkar laboratories for discussions. This work was supported in part by a campus collaboration initiative from Lincoln Labs (R.D. and R.S.), the US National Science Foundation (R.D., J.R.R., R.S. and T.K.L.) under grant number 1124247, and the Office of Naval Research (J.R.R. and T.K.L.) under grant number N000141110725.

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Author notes

  1. Rahul Sarpeshkar and Timothy K. Lu: These authors contributed equally to this work.

Authors and Affiliations

  1. Analog Circuits and Biological Systems Group, Research Lab of Electronics, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Ramiz Daniel & Rahul Sarpeshkar
  2. Synthetic Biology Group, Research Lab of Electronics, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Ramiz Daniel, Jacob R. Rubens & Timothy K. Lu
  3. Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Ramiz Daniel, Jacob R. Rubens, Rahul Sarpeshkar & Timothy K. Lu
  4. MIT Microbiology Program, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Jacob R. Rubens, Rahul Sarpeshkar & Timothy K. Lu
  5. Department of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Rahul Sarpeshkar & Timothy K. Lu
  6. MIT Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Rahul Sarpeshkar & Timothy K. Lu
  7. MIT Biophysics Program, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Rahul Sarpeshkar
  8. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
    Timothy K. Lu

Authors

  1. Ramiz Daniel
  2. Jacob R. Rubens
  3. Rahul Sarpeshkar
  4. Timothy K. Lu

Contributions

R.D., R.S. and T.K.L. designed the study. R.D. and J.R.R. performed experiments and collected data. R.D., J.R.R., R.S. and T.K.L. invented the analog circuit motifs. R.D., R.S. and T.K.L. developed the analog circuit motifs and associated models and simulations. All authors analysed the data, discussed results and wrote the manuscript.

Corresponding authors

Correspondence toRahul Sarpeshkar or Timothy K. Lu.

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Competing interests

Massachusetts Institute of Technology, with which all the authors are affiliated, has filed a PCT patent application based on this work.

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This file contains Supplementary Text and Data, Supplementary Figures 1-53, Supplementary Tables 1-4, and Supplementary References (see Table of Contents for more details). (PDF 6125 kb)

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Daniel, R., Rubens, J., Sarpeshkar, R. et al. Synthetic analog computation in living cells.Nature 497, 619–623 (2013). https://doi.org/10.1038/nature12148

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Editorial Summary

Living-cell computation simplified

The design of novel genetic control systems for synthetic biology is dominated by digital logic. This is necessarily a complex arrangement. Now Timothy Lu and colleagues have harnessed analog building-blocks found in natural cells to perform arithmetic operations in the logarithmic domain. Such analog circuits — which could be integrated with digital — should make it possible to use fewer components to implement complex computations that require wide dynamic range in biosensing.

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