Skip to main content
SpringerLink
Log in

Evolutionary design and analysis of ribozyme-based logic gates

  • Published:
Genetic Programming and Evolvable Machines Aims and scope Submit manuscript

Abstract

A main goal of synthetic biology is the design of logic gates that can reprogram cells to perform various user-defined tasks. One approach is the use of ribozyme-based logic gates (ribogates) consisting of catalytic RNA strands. However, existing ribogate design approaches face limitations in terms of complexity, diversity, ease of use, and reliability. To address these challenges, we introduce a multi-objective evolutionary algorithm called Truth-Seq-Er, which generates diverse and complex ribogate designs while improving user-friendliness and accessibility. Truth-Seq-Er uses a quality diversity approach and a novel technique called viability nullification to design 1, 2, and 3-input integrated ribogates that implement both linearly separable and inseparable functions. By requiring only a target Boolean function as input, the algorithm eliminates the need for domain knowledge and streamlines the design process. The diverse designs generated by Truth-Seq-Er are robust against unexpected requirements and provide a large, unbiased dataset for characterizing candidate ribogates. Moreover, we propose a graph-based model for ribogate operation and analyze the design principles shared by different ribogate families. The results demonstrate the potential of Truth-Seq-Er in advancing ribogate design and contributing to the development of novel synthetic biology and unconventional computing applications. Truth-Seq-Er is available for download at https://github.com/nickkamel/Truth_Seq_Er_CLI.

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
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. J. Zhang, M.K. Jensen, J.D. Keasling, Current Opin. Chem. Biol. 28, 1 (2015)

    Article  Google Scholar 

  2. A. Cubillos-Ruiz, T. Guo, A. Sokolovska, P.F. Miller, J.J. Collins, T.K. Lu, J.M. Lora, Nat. Rev. Drug. Discov. 20, 941 (2021)

    Article  Google Scholar 

  3. G. Rodrigo, T.E. Landrain, S. Shen, A. Jaramillo, Trends Genet. 29, 529 (2013)

    Article  Google Scholar 

  4. A.A.K. Nielsen, B.S. Der, J. Shin, P. Vaidyanathan, V. Paralanov, E.A. Strychalski, D. Ross, D. Densmore, C.A. Voigt, Science 352, aac7341 (2016)

    Article  Google Scholar 

  5. B.C. Stanton, A.A.K. Nielsen, A. Tamsir, K. Clancy, T. Peterson, C.A. Voigt, Nat. Chem. Biol. 10, 99 (2014)

    Article  Google Scholar 

  6. J. Chappell, M.K. Takahashi, J.B. Lucks, Nat. Chem. Biol. 11, 214 (2015)

    Article  Google Scholar 

  7. A.A. Green, J. Kim, D. Ma, P.A. Silver, J.J. Collins, P. Yin, Nature 548, 117 (2017)

    Article  Google Scholar 

  8. M.W. Gander, J.D. Vrana, W.E. Voje, J.M. Carothers, E. Klavins, Nat. Commun. 8, 15459 (2017)

    Article  Google Scholar 

  9. R. Penchovsky, R.R. Breaker, Nat. Biotechnol. 23, 1424 (2005)

    Article  Google Scholar 

  10. E.I. Ramlan, K.-P. Zauner, J. Cheminform. 5, 22 (2013)

    Article  Google Scholar 

  11. J. K. Pugh, L. B. Soros, K. O. Stanley, Front. Robot. AI 3, (2016)

  12. R. Lorenz, S.H. Bernhart, C. Höner zu Siederdissen, H. Tafer, C. Flamm, P.F. Stadler, I.L. Hofacker, Algorithms Mol. Biol. 6, 26 (2011)

    Article  Google Scholar 

  13. A.R. Ferré-D’Amaré, W.G. Scott, Cold Spring Harb. Perspect. Biol. 2, a003574 (2010)

    Google Scholar 

  14. A. Ren, R. Micura, D.J. Patel, Current Opin. Chem. Biol. 41, 71 (2017)

    Article  Google Scholar 

  15. N. Kharma, L. Varin, A. Abu-Baker, J. Ouellet, S. Najeh, M.-R. Ehdaeivand, G. Belmonte, A. Ambri, G. Rouleau, J. Perreault, Nucleic Acids Res. 44, e39 (2016)

    Article  Google Scholar 

  16. S. Najeh, K. Zandi, S. Djerroud, N. Kharma, J.. Perreault, in Ribozymes: Methods and Protocols. ed. by R.J. Scarborough, A. Gatignol (Springer, New York, 2021), pp.91–111

    Chapter  Google Scholar 

  17. S. Shen, G. Rodrigo, S. Prakash, E. Majer, T.E. Landrain, B. Kirov, J.-A. Darós, A. Jaramillo, Nucleic Acids Res. 43, 5158 (2015)

    Article  Google Scholar 

  18. J. Perreault, Z. Weinberg, A. Roth, O. Popescu, P. Chartrand, G. Ferbeyre, R.R. Breaker, PLoS Comput. Biol. 7, e1002031 (2011)

    Article  Google Scholar 

  19. K.H. Link, L. Guo, T.D. Ames, L. Yen, R.C. Mulligan, R.R. Breaker, Biol. Chem. 388, 779 (2007)

    Article  Google Scholar 

  20. D. Sen, E.K.Y. Leung, in Nucleic Acid Switches and Sensors. ed. by S.K. Silverman (Springer, Boston, 2006), pp.25–36

    Chapter  Google Scholar 

  21. H. Porta, P.M. Lizardi, Bio. Technol. 13, 161 (1995)

    Google Scholar 

  22. N. Kamel, N. Kharma, A. Abu-Baker, G. Rouleau, A. Bailey, in 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 12–16 (2020)

  23. D.H. Burke, N.D.S. Ozerova, M. Nilsen-Hamilton, Biochemistry 41, 6588 (2002)

    Article  Google Scholar 

  24. T. Kuwabara, M. Warashina, T. Tanabe, K. Tani, S. Asano, K. Taira, Mol. Cell 2, 617 (1998)

    Article  Google Scholar 

  25. R. Penchovsky, A.C.S. Synth, Biology 1, 471 (2012)

    Google Scholar 

  26. A. Cornish-Bowden, Nucleic Acids Res. 13, 3021 (1985)

    Article  Google Scholar 

  27. S. Hammer, B. Tschiatschek, C. Flamm, I.L. Hofacker, S. Findeiß, Bioinformatics 33, 2850 (2017)

    Article  Google Scholar 

  28. J. Lehman, Evolution Through the Search for Novelty (Ohio State University, Columbus, 2007)

    Google Scholar 

  29. R.M. Dirks, N.A. Pierce, J. Comput. Chem. 24, 1664 (2003)

    Article  Google Scholar 

  30. V. Trianni, M. López-Ibáñez, PLoS ONE 10, e0136406 (2015)

    Article  Google Scholar 

  31. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, IEEE Trans. Evol. Comput. 6, 182 (2002)

    Article  Google Scholar 

  32. E.A. Ernst, Optimal Combinational Multi-Level Logic Synthesis (University of Michigan, Ann Arbor, 2009)

    Google Scholar 

  33. T. Sasao, J. T. Butler, Synthesis Lectures on Digital Circuits and Systems vol. 4, no 1, p. 5 (2009)

  34. C.M. Bishop, Pattern Recognition and Machine Learning, Newer (Springer, New York, 2007), p.179

    Google Scholar 

  35. M. Minsky, S.A. Papert, L. Bottou, Perceptrons: An Introduction to Computational Geometry (MIT Press, Cambridge, 1969)

    MATH  Google Scholar 

  36. J. Kruskal, M. Wish, Multidimensional Scaling (Thousand Oaks, California, 1978)

    Book  Google Scholar 

Download references

Acknowledgements

We would like to thank Joey Paquet for reviewing earlier versions of this manuscript and providing critical feedback.

Funding

This work was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant (individual) and CREATE program (SynBioApps), as well as the Canadian Institutes of Health Research project grant (bridge).

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Nicolas Kamel & Nawwaf Kharma

  2. Institut National de la Recherche Scientifique, Laval, Canada

    Jonathan Perreault

Authors
  1. Nicolas Kamel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nawwaf Kharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonathan Perreault
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Kharma and Kamel performed an extensive review on different ways to implement logic gates in biological matter, and identified ribozymes as an appropriate approach. Kamel wrote the manuscript, drew the figures, designed and coded the Truth-Seq-Er evolutionary algorithm, and proposed the additive segment competition model (mechanism graphs). Kharma made substantial contributions to the formulation of the evolutionary algorithm, the presentation and discussion of the results, and the writing and critical review of the manuscript. J.P. provided critical insight into the structure and function of ribozymes, and provided feedback to ensure that the designed ribogates were biologically plausible.

Corresponding author

Correspondence to Nicolas Kamel.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

Additional information

Area Editor: Ting Hu.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamel, N., Kharma, N. & Perreault, J. Evolutionary design and analysis of ribozyme-based logic gates. Genet Program Evolvable Mach 24, 11 (2023). https://doi.org/10.1007/s10710-023-09459-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10710-023-09459-x

Keywords

Search

Navigation