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Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks

A Publisher Correction to this article was published on 08 September 2022

This article has been updated

Abstract

Engineered far-from-equilibrium synthetic chemical networks that pulse or switch states in response to environmental signals could precisely regulate the kinetics of chemical synthesis or self-assembly. Currently, such networks must be extensively tuned to compensate for the different activities of and unintended reactions between a network’s various chemical components. Modular elements with standardized performance could be used to rapidly construct networks with designed functions. Here we develop standardized excitable chemical regulatory elements, termed genelets, and use them to construct complex in vitro transcriptional networks. We develop a protocol for identifying >15 interchangeable genelet elements with uniform performance and minimal crosstalk. These elements can be combined to engineer feedforward and feedback modules whose dynamics match those predicted by a simple kinetic model. Modules can then be rationally integrated and organized into networks that produce tunable temporal pulses and act as multistate switchable memories. Standardized genelet elements, and the workflow to identify more, should make engineering complex far-from-equilibrium chemical dynamics routine.

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Fig. 1: The hairpin clamp (HPC5) genelet toolbox.
Fig. 2: Design and screening protocol for identifying sequences for standardized HPC5 genelet domains.
Fig. 3: IFFLs orchestrate temporal pulses in genelet activation.
Fig. 4: A TSN composed of three mutually repressive BSMs.
Fig. 5: Engineering mesoscale networks by integrating modules and programming additional interactions.

Data availability

The data associated with this manuscript are available at: https://doi.org/10.7281/T1/UBSZF1.

Code availability

The general genelet model code, including scripts for the main text simulations, is available at: https://github.com/sschaff6/general-genelet-model.git.

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References

  1. Schultz, D., Wolynes, P. G., Jacob, E. B. & Onuchic, J. N. Deciding fate in adverse times: sporulation and competence in Bacillus subtilis. Proc. Natl Acad. Sci. USA 106, 21027–21034 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Oppenheim, A. B., Kobiler, O., Stavans, J., Court, D. L. & Adhya, S. Switches in bacteriophage lambda development. Annu. Rev. Genet. 39, 409–429 (2005).

    CAS  PubMed  Article  Google Scholar 

  3. Peter, I. S. & Davidson, E. H. Assessing regulatory information in developmental gene regulatory networks. Proc. Natl Acad. Sci. USA 114, 5862 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007).

    CAS  PubMed  Article  Google Scholar 

  5. van Esch, J. H., Klajn, R. & Otto, S. Chemical systems out of equilibrium. Chem. Soc. Rev. 46, 5474–5475 (2017).

    PubMed  Article  Google Scholar 

  6. van Roekel, H. W. H. et al. Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach. Chem. Soc. Rev. 44, 7465–7483 (2015).

    PubMed  Article  Google Scholar 

  7. Ferrell, J. E.Jr & Ha, S. H. Ultrasensitivity part III: cascades, bistable switches, and oscillators. Trends Biochem. Sci. 39, 612–618 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. McAdams Harley, H. & Shapiro, L. Circuit simulation of genetic networks. Science 269, 650–656 (1995).

    Article  Google Scholar 

  9. Ackermann, J., Wlotzka, B. & McCaskill, J. S. In vitro DNA-based predator–prey system with oscillatory kinetics. Bull. Math. Biol. 60, 329–354 (1998).

    CAS  Article  Google Scholar 

  10. Montagne, K., Plasson, R., Sakai, Y., Fujii, T. & Rondelez, Y. Programming an in vitro DNA oscillator using a molecular networking strategy. Mol. Syst. Biol. 7, 466 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  11. Semenov, S. N. et al. Rational design of functional and tunable oscillating enzymatic networks. Nat. Chem. 7, 160–165 (2015).

    CAS  PubMed  Article  Google Scholar 

  12. Kim, J. & Winfree, E. Synthetic in vitro transcriptional oscillators. Mol. Syst. Biol. 7, 465 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. Montagne, K., Gines, G., Fujii, T. & Rondelez, Y. Boosting functionality of synthetic DNA circuits with tailored deactivation. Nat. Commun. 7, 13474 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Padirac, A., Fujii, T. & Rondelez, Y. Bottom-up construction of in vitro switchable memories. Proc. Natl Acad. Sci. USA 109, E3212–E3220 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Helwig, B., van Sluijs, B., Pogodaev, A. A., Postma, S. G. J. & Huck, W. T. S. Bottom-up construction of an adaptive enzymatic reaction network. Angew. Chem. Int. Ed. 57, 14065–14069 (2018).

    CAS  Article  Google Scholar 

  16. Subsoontorn, P., Kim, J. & Winfree, E. Ensemble Bayesian analysis of bistability in a synthetic transcriptional switch. ACS Synth. Biol. 1, 299–316 (2012).

    CAS  PubMed  Article  Google Scholar 

  17. Postma, S. G. J., te Brinke, D., Vialshin, I. N., Wong, A. S. Y. & Huck, W. T. S. A trypsin-based bistable switch. Tetrahedron 73, 4896–4900 (2017).

    CAS  Article  Google Scholar 

  18. Genot, A. J. et al. High-resolution mapping of bifurcations in nonlinear biochemical circuits. Nat. Chem. 8, 760 (2016).

    CAS  PubMed  Article  Google Scholar 

  19. Kim, J., White, K. S. & Winfree, E. Construction of an in vitro bistable circuit from synthetic transcriptional switches. Mol. Syst. Biol. 2, 68 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. Kim, J., Khetarpal, I., Sen, S. & Murray, R. M. Synthetic circuit for exact adaptation and fold-change detection. Nucleic Acids Res. 42, 6078–6089 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Zadorin, A. S. et al. Synthesis and materialization of a reaction–diffusion French flag pattern. Nat. Chem. 9, 990 (2017).

    CAS  PubMed  Article  Google Scholar 

  22. Gines, G. et al. Microscopic agents programmed by DNA circuits. Nat. Nanotechnol. 12, 351 (2017).

    CAS  PubMed  Article  Google Scholar 

  23. Dupin, A. & Simmel, F. C. Signalling and differentiation in emulsion-based multi-compartmentalized in vitro gene circuits. Nat. Chem. 11, 32–39 (2019).

    CAS  PubMed  Article  Google Scholar 

  24. Green, L. N. et al. Autonomous dynamic control of DNA nanostructure self-assembly. Nat. Chem. 11, 510–520 (2019).

    CAS  PubMed  Article  Google Scholar 

  25. Franco, E. et al. Timing molecular motion and production with a synthetic transcriptional clock. Proc. Natl Acad. Sci. USA 108, E784–E793 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Meijer, L. H. H. et al. Hierarchical control of enzymatic actuators using DNA-based switchable memories. Nat. Commun. 8, 1117 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. Schaffter, S. W. & Schulman, R. Building in vitro transcriptional regulatory networks by successively integrating multiple functional circuit modules. Nat. Chem. 11, 829–838 (2019).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  29. Song, T. et al. Fast and compact DNA logic circuits based on single-stranded gates using strand-displacing polymerase. Nat. Nanotechnol. 14, 1075–1081 (2019).

    CAS  PubMed  Article  Google Scholar 

  30. Kishi, J. Y., Schaus, T. E., Gopalkrishnan, N., Xuan, F. & Yin, P. Programmable autonomous synthesis of single-stranded DNA. Nat. Chem. 10, 155–164 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. Shah, S. et al. Using strand displacing polymerase to program chemical reaction networks. J. Am. Chem. Soc. 142, 9587–9593 (2020).

    CAS  PubMed  Google Scholar 

  32. Chen, Z. et al. De novo design of protein logic gates. Science 368, 78 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Franco, E., Giordano, G., Forsberg, P.-O. & Murray, R. M. Negative autoregulation matches production and demand in synthetic transcriptional networks. ACS Synth. Biol. 3, 589–599 (2014).

    CAS  PubMed  Article  Google Scholar 

  34. Kim, J., Hopfield, J. & Winfree, E. in Advances in Neural Information Processing Systems 17 (eds Saul, L. K., Weiss, Y. & Bottou, L.) 681–688 (MIT Press, 2005).

  35. Zadeh, J. N. et al. NUPACK: analysis and design of nucleic acid systems. J. Comput. Chem. 32, 170–173 (2011).

    CAS  PubMed  Article  Google Scholar 

  36. Dabby, N. Synthetic Molecular Machines for Active Self-assembly: Prototype Algorithms, Designs, and Experimental Study. PhD thesis, California Institute of Technology (2013).

  37. Groves, B. et al. Computing in mammalian cells with nucleic acid strand exchange. Nat. Nanotechnol. 11, 287–294 (2016).

    CAS  PubMed  Article  Google Scholar 

  38. Isambert, H. The jerky and knotty dynamics of RNA. Methods 49, 189–196 (2009).

    CAS  PubMed  Article  Google Scholar 

  39. Zhang, D. Y. & Winfree, E. Control of DNA strand displacement kinetics using toehold exchange. J. Am. Chem. Soc. 131, 17303–17314 (2009).

    CAS  PubMed  Article  Google Scholar 

  40. Mangan, S. & Alon, U. Structure and function of the feed-forward loop network motif. Proc. Natl Acad. Sci. USA 100, 11980–11985 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Krupp, G. RNA synthesis: strategies for the use of bacteriophage RNA polymerases. Gene 72, 75–89 (1988).

    CAS  PubMed  Article  Google Scholar 

  42. Lapham, J. & Crothers, D. M. RNase H cleavage for processing of in vitro transcribed RNA for NMR studies and RNA ligation. RNA 2, 289–296 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    CAS  PubMed  Article  Google Scholar 

  44. Mahmoudabadi, G. & Phillips, R. A comprehensive and quantitative exploration of thousands of viral genomes. eLife 7, e31955 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  45. O’Reilly, R. K., Turberfield, A. J. & Wilks, T. R. The evolution of DNA-templated synthesis as a tool for materials discovery. Acc. Chem. Res. 50, 2496–2509 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. Schaffter, S. W. General Genelet Model (2020); https://github.com/sschaff6/general-genelet-model.git

  47. Dubuc, E. et al. Cell-free microcompartmentalised transcription–translation for the prototyping of synthetic communication networks. Curr. Opin. Biotechnol. 58, 72–80 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Chatterjee, G., Dalchau, N., Muscat, R. A., Phillips, A. & Seelig, G. A spatially localized architecture for fast and modular DNA computing. Nat. Nanotechnol. 12, 920–927 (2017).

    CAS  PubMed  Article  Google Scholar 

  49. Laohakunakorn, N. et al. Bottom-up construction of complex biomolecular systems with cell-free synthetic biology. Front. Bioeng. Biotechnol. 8, 213 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  50. Cunningham, P. & Ofengand, J. Use of inorganic pyrophosphatase to improve the yield of in vitro transcription reactions catalyzed by T7 RNA polymerase. BioTechniques 9, 713–714 (1990).

    CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank E. Franco, E. Nakamura, M. Rubanov and P. Moerman for insightful conversations and comments on the manuscript. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under grant number DGE-1232825 to S.W.S. This work was principally supported by the Department of Energy under award number DE-SC001 0426 to R.S. K.C. was supported by National Science Foundation award number EFRI-1830893 and Army Research Office award W911NF2010057. This work was also supported by the University of Chicago Materials Research Science and Engineering Center, which is funded by the National Science Foundation under award number DMR-2011854 to J.O. and A.M. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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S.W.S and R.S. designed the research. S.W.S conducted most of the experiments and simulations. K.C. performed the experiments presented in Supplementary Information, sections 11, 3.4 and 4.6. M.N. performed preliminary experiments for the study. J.O. and A.M. conducted the multistability simulations and analysis. S.W.S and R.S. wrote the paper with feedback from the other authors.

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Correspondence to Rebecca Schulman.

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Schaffter, S.W., Chen, KL., O’Brien, J. et al. Standardized excitable elements for scalable engineering of far-from-equilibrium chemical networks. Nat. Chem. (2022). https://doi.org/10.1038/s41557-022-01001-3

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