Publications by authors named "J P K Doye"

In cells, proteins rapidly self-assemble into sophisticated nanomachines. Bioinspired self-assembly approaches, such as DNA origami, have been used to achieve complex three-dimensional (3D) nanostructures and devices. However, current synthetic systems are limited by low yields in hierarchical assembly and challenges in rapid and efficient reconfiguration between diverse structures.

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Pipelining is a design technique for logical circuits that allows for higher throughput than circuits in which multiple computations are fed through the system one after the other. It allows for much faster computation than architectures in which inputs must pass through every layer of the circuit before the next computation can begin (phased chaining). We explore the hypothesis that these advantages may be offset by a higher error rate, logical irreversibility, and greater thermodynamic costs by simulating pipelined molecular mechanical circuits using an explicit physical model.

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We introduce oxNA, a new model for the simulation of DNA-RNA hybrids that is based on two previously developed coarse-grained models-oxDNA and oxRNA. The model naturally reproduces the physical properties of hybrid duplexes, including their structure, persistence length, and force-extension characteristics. By parameterizing the DNA-RNA hydrogen bonding interaction, we fit the model's thermodynamic properties to experimental data using both average-sequence and sequence-dependent parameters.

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This chapter introduces how to run molecular dynamics simulations for DNA origami using the oxDNA coarse-grained model.

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Landauer's principle places a fundamental lower limit on the work required to perform a logically irreversible operation. Logically reversible gates provide a way to avoid these work costs and also simplify the task of making the computation as a whole thermodynamically reversible. The inherent reversibility of mechanical logic gates would make them good candidates for the design of practical logically reversible computing systems if not for the relatively large size and mass of such systems.

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