Advertisement

DNA-Guided Assembly of Nanocellulose Meshes

  • Alexandru Amărioarei
  • Gefry Barad
  • Eugen Czeizler
  • Ana-Maria Dobre
  • Corina Iţcuş
  • Victor Mitrana
  • Andrei Păun
  • Mihaela Păun
  • Frankie Spencer
  • Romică Trandafir
  • Iris Tuşa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

Nanoengineered materials are a product of joint collaboration of theoreticians and experimentalists, of physicists, (bio-)chemists, and recently, of computer scientists. In the field of Nanotechnology and Nanoengineering, DNA (algorithmic) self-assembly has an acknowledged leading position. As a fabric, DNA is a rather inferior material; as a medium for shape, pattern, and dynamic behavior reconstruction, it is one of the most versatile nanomaterials. This is why the prospect of combining the physical properties of known high performance nanomaterials, such as cellulose, graphene, or fibroin, with the assembly functionality of DNA scaffolds is a very promising prospect. In this work we analyze the dynamical and structural properties of a would-be DNA-guided assembly of nanocellulose meshes. The aim is to generate pre-experimental insights on possible ways of manipulating structural properties of such meshes. The mechanistic principles of these systems, implemented through the DNA assembly apparatus, ensure the formation of 2D nanocellulose mesh structures. A key desired feature for such an engineered synthetic material, e.g. with applications in bio-medicine and nano-engineering, would be to control the size of the openings (gaps) within these meshes, a.k.a. its aperture. However, in the case of this composite material, this is not a direct engineered feature. Rather, we assert it could be indirectly achieved through varying several key parameters of the system. We consider here several experimentally tunable parameters, such as the ratio between nanocellulose fibrils and the DNA guiding elements, i.e., aptamer-functionalized DNA origamis, as well as the assumed length of the nanocellulose fibrils. To this aim, we propose a computational model of the mesh-assembly dynamical system, which we subject to numerical parameter scan and analysis.

Keywords

DNA nanotechnology DNA-guided assembly Self-assembly system Rule-based modelling 

Notes

Acknowledgments

This work was supported by the Academy of Finland through grant 311371/2017 and by the Romanian National Authority for Scientific Research and Innovation, through the POC grant P_37_257.

References

  1. 1.
    Amărioarei, A., Barad, G., Czeizler, E., Czeizler, E., Dobre, A.M., Iţcuş, C., Păun, A., Păun, M., Trandafir, R., Tuşa, I.: One dimensional DNA tiles self assembly model simulation. Int. J. Unconventional Comput. 13(4/5), 399–415 (2018)Google Scholar
  2. 2.
    Benson, E., Mohammed, A., Gardell, J., Masich, S., Czeizler, E., Orponen, P., Högberg, B.: DNA rendering of polyhedral meshes at the nanoscale. Nature 523, 441–444 (2015)CrossRefGoogle Scholar
  3. 3.
    Boese, B.J., Breaker, R.R.: In vitro selection and characterization of cellulose-binding DNA aptamers. Nucleic Acids Res. 35(19), 6378–6388 (2007)CrossRefGoogle Scholar
  4. 4.
    Chen, W.T., Zhu, A.Y., Sanjeev, V., Khorasaninejad, M., Shi, Z., Lee, E., Capasso, F.: A broadband achromatic metalens for focusing and imaging in the visible. Nat. Nanotechnol. 13(3), 220–226 (2018)CrossRefGoogle Scholar
  5. 5.
    Ding, L., et al.: MXene molecular sieving membranes for highly efficient gas separation. Nat. Commun. 9(1), 155 (2018)CrossRefGoogle Scholar
  6. 6.
    Eskelinen, A.P., Kuzyk, A., Kaltiaisenaho, T.K., Timmermans, M.Y., Nasibulin, A.G., Kauppinen, E.I., Törmä, P.: Assembly of single-walled carbon nanotubes on DNA-origami templates through streptavidin-biotin interaction. Small 7(6), 746–750 (2011)CrossRefGoogle Scholar
  7. 7.
    Faeder, J.R., Blinov, M.L., Goldstein, B., Hlavacek, W.S.: Rule-based modeling of biochemical networks. Complexity 10(4), 22–41 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Faeder, J.R., Blinov, M.L., Hlavacek, W.S.: Rule-based modeling of biochemical systems with BioNetGen. In: Methods in Molecular Biology, vol. 500, pp. 113–167. Humana Press (2009)Google Scholar
  9. 9.
    Jang, Y., Choi, W.T., Johnson, C.T., García, A.J., Singh, P.M., Breedveld, V., Hess, D.W., Champion, J.A.: Inhibition of bacterial adhesion on nanotextured stainless steel 316L by electrochemical etching. ACS Biomater. Sci. Eng. 4(1), 90–97 (2018)CrossRefGoogle Scholar
  10. 10.
    Kuzyk, A., Laitinen, K.T., Törmä, P.: DNA origami as a nanoscale template for protein assembly. Nanotechnology 20(23), 235305:1–235305:5 (2009)CrossRefGoogle Scholar
  11. 11.
    Kuzyk, A., Schreiber, R., Fan, Z., Pardatscher, G., Roller, E.M., Högele, A., Simmel, F.C., Govorov, A.O., Liedl, T.: DNA-based self-assembly of chiral plasmonic nanostructures with tailored optical response. Nature 483(7389), 311–314 (2012)CrossRefGoogle Scholar
  12. 12.
    Lund, K., Manzo, A.J., Dabby, N., Michelotti, N., Johnson-Buck, A., Nangreave, J., Taylor, S., Pei, R., Stojanovic, M.N., Walter, N.G., Winfree, E., Yan, H.: Molecular robots guided by prescriptive landscapes. Nature 465(7295), 206–209 (2010)CrossRefGoogle Scholar
  13. 13.
    Maune, H.T., Han, S.P., Barish, R.D., Bockrath, M., Iii, W.A., Rothemund, P.W., Winfree, E.: Self-assembly of carbon nanotubes into two-dimensional geometries using DNA origami templates. Nat. Nanotechnol. 5(1), 61–66 (2010)CrossRefGoogle Scholar
  14. 14.
    Mohammed, A., Czeizler, E., Czeizler, E.: Computational modelling of the kinetic tile assembly model using a rule-based approach. Theor. Comput. Sci. 701, 203–215 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Park, J.H., Rutledge, G.C.: Ultrafine high performance polyethylene fibers. J. Mater. Sci. 53(4), 3049–3063 (2018)CrossRefGoogle Scholar
  16. 16.
    Picker, A., et al.: Mesocrystalline calcium silicate hydrate: a bioinspired route toward elastic concrete materials. Sci. Adv. 3(11), e1701216 (2017)CrossRefGoogle Scholar
  17. 17.
    Rothemund, P.W.: Folding DNA to create nanoscale shapes and patterns. Nature 440(7082), 297–302 (2006)CrossRefGoogle Scholar
  18. 18.
    Smith, A.M.: Rulebender: integrated modeling, simulation and visualization for rule-based intracellular biochemistry. BMC J. Bioinf. 13, 1–39 (2012)Google Scholar
  19. 19.
    Sneddon, M.W., Faeder, J.R., Emonet, T.: Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat. Methods 8(2), 177–183 (2011)CrossRefGoogle Scholar
  20. 20.
    Snow, R.J., Bhatkar, H., Diaye, A.T.N., Arenholz, E., Idzerda, Y.U., Snow, R.J., Bhatkar, H., Diaye, A.T.N., Arenholz, E., Idzerda, Y.U.: Large moments in bcc \(Fe_{x }Co_{y}Mn_{z}\) ternary alloy thin films. Appl. Phys. Lett. 112(7), 1–5 (2018)CrossRefGoogle Scholar
  21. 21.
    Tikhomirov, G., Petersen, P., Qian, L.: Fractal assembly of micrometre-scale DNA origami arrays with arbitrary patterns. Nature 552(7683), 67–71 (2017)CrossRefGoogle Scholar
  22. 22.
    Ware, C.S., Smith-Palmer, T., Peppou-Chapman, S., Scarratt, L.R., Humphries, E.M., Balzer, D., Neto, C.: Marine antifouling behavior of lubricant-infused nanowrinkled polymeric surfaces. ACS Appl. Mate. Interfaces 10(4), 4173–4182 (2018)CrossRefGoogle Scholar
  23. 23.
    Xiong, G., He, P., Lyu, Z., Chen, T., Huang, B., Chen, L., Fisher, T.S.: Bioinspired leaves-on-branchlet hybrid carbon nanostructure for supercapacitors. Nat. Commun. 9(1), 790 (2018)CrossRefGoogle Scholar
  24. 24.
    Yang, J., Hlavacek, W.S.: The efficiency of reactant site sampling in network-free simulation of rule-based models for biochemical systems. Phys. Biol. 8(5), 055009 (2011)CrossRefGoogle Scholar
  25. 25.
    Yang, Q., Goldstein, I.J., Mei, H.Y., Engelke, D.R.: DNA ligands that bind tightly and selectively to cellobiose. Proc. Nat. Acad. Sci. 95(10), 5462–5467 (1998)CrossRefGoogle Scholar
  26. 26.
    Zhang, K., Lin, S., Feng, Q., Dong, C., Yang, Y., Li, G., Bian, L.: Nanocomposite hydrogels stabilized by self-assembled multivalent bisphosphonate-magnesium nanoparticles mediate sustained release of magnesium ion and promote in-situ bone regeneration. Acta Biomaterialia 64, 389–400 (2017)CrossRefGoogle Scholar
  27. 27.
    Zhang, X., Ding, X., Zou, J., Gu, H.: A proximity-based programmable DNA nanoscale assembly line. Methods Mol. Biol. 1500, 257–268 (2017)CrossRefGoogle Scholar
  28. 28.
    Zheng, J., Birktoft, J.J., Chen, Y., Wang, T., Sha, R., Constantinou, P.E., Ginell, S.L., Mao, C., Seeman, N.C.: From molecular to macroscopic via the rational design of a self-assembled 3D DNA crystal. Nature 461(7260), 74–77 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of BioinformaticsNational Institute of Research and Development for Biological SciencesBucharestRomania
  2. 2.Computational Biomodeling Laboratory, Turku Centre for Computer Science and Department of Computer ScienceÅbo Akademi UniversityTurkuFinland

Personalised recommendations