Nanotechnology and synthetic biology are rapidly converging, with DNA origami being one of the leading bridging technologies. DNA origami was shown to work well in a wide array of biotic environments. However, the large majority of extant DNA origami scaffolds utilize bacteriophages or plasmid sequences thus severely limiting its future applicability as a bio-orthogonal nanotechnology platform. In this paper we present the design of biologically inert (i.e., "bio-orthogonal") origami scaffolds. The synthetic scaffolds have the additional advantage of being uniquely addressable (unlike biologically derived ones) and hence are better optimized for high-yield folding. We demonstrate our fully synthetic scaffold design with both DNA and RNA origamis and describe a protocol to produce these bio-orthogonal and uniquely addressable origami scaffolds.

DNA-based memory systems are being reported with increasing frequency. However, dynamic DNA data structures able to store and recall information in an ordered way, and able to be interfaced with external nucleic acid computing circuits, have so far received little attention. Here we present an in vitro implementation of a stack data structure using DNA polymers. The stack is able to record combinations of two different DNA signals, release the signals into solution in reverse order, and then re-record. We explore the accuracy limits of the stack data structure through a stochastic rule-based model of the underlying polymerisation chemistry. We derive how the performance of the stack increases with the efficiency of washing steps between successive reaction stages, and report how stack performance depends on the history of stack operations under inefficient washing. Finally, we discuss refinements to improve molecular synchronisation and future open problems in implementing an autonomous chemical data structure.

In vitro implementation of a stack data structure based on DNA strand displacement.

We recently reported the design for a DNA nano-device that can record and store molecular signals. Here we present an evolutionary algorithm tailored to optimising nucleic acid sequences that predictively fold into our desired target structures. In our approach, a DNA device is rst speci ed abstractly: the topology of the individual strands and their desired foldings into multi-strand complexes are described at the domain-level. Initially, this design is decomposed into a set of pairwise strand interactions. en, we optimize candidate domains, such that the resulting sequences fold with high accuracy into desired target structures both (a) individually and (b) jointly, but also (c) to show high a nity for binding desired partners and simultaneously low a nity to bind with any undesired partner. As optimization heuristic we use a genetic algorithm that employs a linear combination of the above scores. Our algorithm was able to generate DNA sequences that satisfy all given criteria. Even though we cannot establish the theoretically achievable optima (as this would require exhaustive search), our solutions score 90% of an upper bound that ignores con icting objectives. We envision that this approach can be generalized towards a broad class of toehold-mediated strand displacement systems.

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