Construction of one-dimensional random walk lattices using DNA algorithmic self-assembly

Algorithmic DNA lattices are constructed using pre-defined rules such as COPY, NOT, and XOR, where patterns are predicted based on initial values. However, the experimental implementation of an unpredictable random walk pattern (which is the implementation of a random rule, i.e., equally probable to move toward either the left or right in 1D systems) in DNA has not been reported yet. Here, we construct DNA lattices with DNA rule tiles implemented using the random rule. Patterns are visualized by atomic force microscopy. Finally, we discussed the average displacement, mean-square displacement, and number of displacement occurrences of experimental as well as simulated 1D random walk. The encoded information in sticky ends of DNA rule tiles demonstrates the feasibility of universal computation through DNA algorithmic self-assembly, which could be extremely beneficial in future computations.

[1]  Erik Winfree,et al.  Diverse and robust molecular algorithms using reprogrammable DNA self-assembly , 2019, Nature.

[2]  J. Reif,et al.  Logical computation using algorithmic self-assembly of DNA triple-crossover molecules , 2000, Nature.

[3]  Edward A. Codling,et al.  Random walk models in biology , 2008, Journal of The Royal Society Interface.

[4]  E. Winfree,et al.  Algorithmic Self-Assembly of DNA Sierpinski Triangles , 2004, PLoS biology.

[5]  Lulu Qian,et al.  Programmable disorder in random DNA tilings. , 2017, Nature nanotechnology.

[6]  Luvena L. Ong,et al.  Three-Dimensional Structures Self-Assembled from DNA Bricks , 2012, Science.

[7]  E. Winfree,et al.  Toward reliable algorithmic self-assembly of DNA tiles: a fixed-width cellular automaton pattern. , 2008, Nano letters.

[8]  James A. Reggia,et al.  Automatic discovery of self-replicating structures in cellular automata , 1997, IEEE Trans. Evol. Comput..

[9]  Jihoon Shin,et al.  Streptavidin-Decorated Algorithmic DNA Lattices Constructed by Substrate-Assisted Growth Method. , 2018, ACS biomaterials science & engineering.

[10]  Erik Winfree,et al.  Physical principles for DNA tile self-assembly. , 2017, Chemical Society reviews.

[11]  E. Winfree Algorithmic Self-Assembly of DNA: Theoretical Motivations and 2D Assembly Experiments , 2000, Journal of biomolecular structure & dynamics.

[12]  Juan J. de Pablo,et al.  Monte Carlo simulation of proteins through a random walk in energy space , 2002 .

[13]  S. Johnson,et al.  Random-tiling model of membranes and interfaces , 1996 .

[14]  P. Rothemund,et al.  Programmable molecular recognition based on the geometry of DNA nanostructures. , 2011, Nature chemistry.

[15]  Jehoshua Bruck,et al.  Neural network computation with DNA strand displacement cascades , 2011, Nature.

[16]  Shawn M. Douglas,et al.  A Logic-Gated Nanorobot for Targeted Transport of Molecular Payloads , 2012, Science.

[17]  N. Seeman,et al.  Design and self-assembly of two-dimensional DNA crystals , 1998, Nature.

[18]  Erik Winfree,et al.  An information-bearing seed for nucleating algorithmic self-assembly , 2009, Proceedings of the National Academy of Sciences.

[19]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[20]  Sung Ha Park,et al.  3-Input/1-Output Logic Implementation Demonstrated by DNA Algorithmic Self-Assembly. , 2018, ACS nano.

[21]  P. Yin,et al.  Complex shapes self-assembled from single-stranded DNA tiles , 2012, Nature.

[22]  Erik Winfree,et al.  Two computational primitives for algorithmic self-assembly: copying and counting. , 2005, Nano letters.

[23]  E. Winfree,et al.  Design and characterization of programmable DNA nanotubes. , 2004, Journal of the American Chemical Society.

[24]  Matthew J. Patitz An introduction to tile-based self-assembly and a survey of recent results , 2014, Natural Computing.