Algorithmic DNA tile systems have the potential to allow the construction by self-assembly of large structures with complex nanometer-scale details out of relatively few monomer types, but are constrained by errors in growth and the limited sequence space of orthogonal DNA sticky ends that program tile interactions. We present a tile set optimization technique that, through analysis of algorithmic growth equivalence, potentially sensitive error pathways, and potential lattice defects, can significantly reduce the size of tile systems while preserving proofreading behavior that is essential for obtaining low error rates. Applied to systems implementing multiple algorithms that are far beyond the size of currently feasible implementations, the optimization technique results in systems that are comparable in size to already-implemented experimental systems.
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