Universal Computation and Optimal Construction in the Chemical Reaction Network-Controlled Tile Assembly Model

Tile-based self-assembly and chemical reaction networks provide two well-studied models of scalable DNA-based computation. Although tile self-assembly provides a powerful framework for describing Turing-universal self-assembling systems, assembly logic in tile self-assembly is localized, so that only the nearby environment can affect the process of self-assembly. We introduce a new model of tile-based self-assembly in which a well-mixed chemical reaction network interacts with self-assembling tiles to exert non-local control on the self-assembly process. Through simulation of multi-stack machines, we demonstrate that this new model is efficiently Turing-universal, even when restricted to unbounded space in only one spatial dimension. Using a natural notion of program complexity, we also show that this new model can produce many complex shapes with programs of lower complexity. Most notably, we show that arbitrary connected shapes can be produced by a program with complexity bounded by the Kolmogorov complexity of the shape, without the large scale factor that is required for the analogous result in the abstract tile assembly model. These results suggest that controlled self-assembly provides additional algorithmic power over tile-only self-assembly, and that non-local control enhances our ability to perform computation and algorithmically self-assemble structures from small input programs.

[1]  Lulu Qian,et al.  Supporting Online Material Materials and Methods Figs. S1 to S6 Tables S1 to S4 References and Notes Scaling up Digital Circuit Computation with Dna Strand Displacement Cascades , 2022 .

[2]  G. Seelig,et al.  Enzyme-Free Nucleic Acid Logic Circuits , 2022 .

[3]  David Doty,et al.  Theory of algorithmic self-assembly , 2012, CACM.

[4]  Erik Winfree,et al.  Integrating DNA strand-displacement circuitry with DNA tile self-assembly , 2013, Nature Communications.

[5]  Matthew Cook,et al.  Computation with finite stochastic chemical reaction networks , 2008, Natural Computing.

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

[7]  Jennifer E. Padilla,et al.  A Signal-Passing DNA-Strand-Exchange Mechanism for Active Self-Assembly of DNA Nanostructures. , 2015, Angewandte Chemie.

[8]  Lila Kari,et al.  Negative Interactions in Irreversible Self-assembly , 2010, Algorithmica.

[9]  Luca Cardelli,et al.  Programmable chemical controllers made from DNA. , 2013, Nature nanotechnology.

[10]  Ho-Lin Chen,et al.  Deterministic Function Computation with Chemical Reaction Networks , 2012, DNA.

[11]  Hao Yan,et al.  Challenges and opportunities for structural DNA nanotechnology. , 2011, Nature nanotechnology.

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

[13]  Erik Winfree,et al.  The program-size complexity of self-assembled squares (extended abstract) , 2000, STOC '00.

[14]  D. Y. Zhang,et al.  Engineering Entropy-Driven Reactions and Networks Catalyzed by DNA , 2007, Science.

[15]  Charles H. Bennett,et al.  The thermodynamics of computation—a review , 1982 .

[16]  D. Gillespie A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions , 1976 .

[17]  Erik Winfree,et al.  Complexity of Self-Assembled Shapes , 2004, SIAM J. Comput..

[18]  Michael Sipser,et al.  Introduction to the Theory of Computation , 1996, SIGA.

[19]  Lulu Qian,et al.  Efficient Turing-Universal Computation with DNA Polymers , 2010, DNA.

[20]  A. Condon,et al.  Less haste, less waste: on recycling and its limits in strand displacement systems , 2011, Interface Focus.

[21]  Robert M. Dirks,et al.  Triggered amplification by hybridization chain reaction. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Nadrian C. Seeman,et al.  An Overview of Structural DNA Nanotechnology , 2007, Molecular biotechnology.

[23]  Ashish Goel,et al.  Running time and program size for self-assembled squares , 2001, STOC '01.

[24]  Robert T. Schweller,et al.  Temperature 1 self-assembly: deterministic assembly in 3D and probabilistic assembly in 2D , 2009, SODA '11.

[25]  Scott M. Summers Reducing Tile Complexity for the Self-assembly of Scaled Shapes Through Temperature Programming , 2011, Algorithmica.

[26]  G. Seelig,et al.  Dynamic DNA nanotechnology using strand-displacement reactions. , 2011, Nature chemistry.

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

[28]  Luca Cardelli,et al.  On the Computational Power of Biochemistry , 2008, AB.

[29]  Harry M. T. Choi,et al.  Programming biomolecular self-assembly pathways , 2008, Nature.

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

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

[32]  Erik Winfree,et al.  DNA as a universal substrate for chemical kinetics , 2009, Proceedings of the National Academy of Sciences.

[33]  Ming-Yang Kao,et al.  Complexities for generalized models of self-assembly , 2004, SODA '04.

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

[35]  Matthew J. Patitz,et al.  Exact Shapes and Turing Universality at Temperature 1 with a Single Negative Glue , 2011, DNA.