7 research outputs found
Reducing facet nucleation during algorithmic self-assembly
Algorithmic self-assembly, a generalization of crystal growth, has been proposed as a mechanism for bottom-up fabrication of complex
nanostructures and autonomous DNA computation. In principle, growth can be programmed by designing a set of molecular tiles with binding
interactions that enforce assembly rules. In practice, however, errors during assembly cause undesired products, drastically reducing yields.
Here we provide experimental evidence that assembly can be made more robust to errors by adding redundant tiles that "proofread" assembly.
We construct DNA tile sets for two methods, uniform and snaked proofreading. While both tile sets are predicted to reduce errors during
growth, the snaked proofreading tile set is also designed to reduce nucleation errors on crystal facets. Using atomic force microscopy to
image growth of proofreading tiles on ribbon-like crystals presenting long facets, we show that under the physical conditions we studied the
rate of facet nucleation is 4-fold smaller for snaked proofreading tile sets than for uniform proofreading tile sets
Toward reliable algorithmic self-assembly of DNA tiles: A fixed-width cellular automaton pattern
Bottom-up fabrication of nanoscale structures relies on chemical processes to direct self-assembly. The complexity, precision, and yield achievable by a one-pot reaction are limited by our ability to encode assembly instructions into the molecules themselves. Nucleic acids provide a platform for investigating these issues, as molecular structure and intramolecular interactions can encode growth rules. Here, we use DNA tiles and DNA origami to grow crystals containing a cellular automaton pattern. In a one-pot annealing reaction, 250 DNA strands first assemble into a set of 10 free tile types and a seed structure, then the free tiles grow algorithmically from the seed according to the automaton rules. In our experiments, crystals grew to ~300 nm long, containing ~300 tiles with an initial assembly error rate of ~1.4% per tile. This work provides evidence that programmable molecular self-assembly may be sufficient to create a wide range of complex objects in one-pot reactions
Self-Healing Tile Sets
Biology provides the synthetic chemist with a tantalizing and frustrating challenge:
to create complex objects, defined from the molecular scale up to meters,
that construct themselves from elementary components, and perhaps
even reproduce themselves. This is the challenge of bottom-up fabrication.
The most compelling answer to this challenge was formulated in the early
1980s by Ned Seeman, who realized that the information carried by DNA
strands provides a means to program molecular self-assembly, with potential
applications including DNA scaffolds for crystallography [19] or for molecular
electronic circuits [15]. This insight opened the doors to engineering with the
rich set of phenomena available in nucleic acid chemistry [20]