Error suppression mechanisms for DNA tile self-assembly and their simulation

Algorithmic self-assembly using DNA-based molecular tiles has been demonstrated to implement molecular computation. When several different types of DNA tile self-assemble, they can form large two-dimensional algorithmic patterns. Prior analysis predicted that the error rates of tile assembly can be reduced by optimizing physical parameters such as tile concentrations and temperature. However, in exchange, the growth speed is also very low. To improve the tradeoff between error rate and growth speed, we propose two novel error suppression mechanisms: the Protected Tile Mechanism (PTM) and the Layered Tile Mechanism (LTM). These utilize DNA protecting molecules to form kinetic barriers against spurious assembly. In order to analyze the performance of these two mechanisms, we introduce the hybridization state Tile Assembly Model (hsTAM), which evaluates intra-tile state changes as well as assembly state changes. Simulations using hsTAM suggest that the PTM and LTM improve the optimal tradeoff between error rate $\epsilonAlgorithmic self-assembly using DNA-based molecular tiles has been demonstrated to implement molecular computation. When several different types of DNA tile self-assemble, they can form large two-dimensional algorithmic patterns. Prior analysis predicted that the error rates of tile assembly can be reduced by optimizing physical parameters such as tile concentrations and temperature. However, in exchange, the growth speed is also very low. To improve the tradeoff between error rate and growth speed, we propose two novel error suppression mechanisms: the Protected Tile Mechanism (PTM) and the Layered Tile Mechanism (LTM). These utilize DNA protecting molecules to form kinetic barriers against spurious assembly. In order to analyze the performance of these two mechanisms, we introduce the hybridization state Tile Assembly Model (hsTAM), which evaluates intra-tile state changes as well as assembly state changes. Simulations using hsTAM suggest that the PTM and LTM improve the optimal tradeoff between error rate e\epsilon and growth speed r, from r ? be^2.0r \approx \beta \epsilon^{2.0} (for the conventional mechanism) to r ? be^1.4r \approx \beta \epsilon^{1.4} and r ? be^0.7r \approx \beta \epsilon^{0.7}, respectively.