The design of DNA and RNA sequences is critical for many endeavors, from DNA nanotechnology, to PCR-based applications, to DNA hybridization arrays. Results in the literature rely on a wide variety of design criteria adapted to the particular requirements of each application. Using an extensively-studied thermodynamic model, we perform a detailed study of several criteria for designing sequences intended to adopt a target secondary structure. We conclude that superior design methods should explicitly implement both a positive design paradigm (optimize affinity for the target structure) and a negative design paradigm (optimize specificity for the target structure). The commonly used approaches of sequence symmetry minimization and minimum free energy satisfaction primarily implement negative design and can be strengthened by introducing a positive design component. Surprisingly, our findings hold for a wide range of secondary structures and are robust to modest perturbation of the thermodynamic parameters used for evaluating sequence quality, suggesting the feasibility and ongoing utility of a unified approach to nucleic acid design as parameter sets are further refined. Finally, we observe that designing for thermodynamic stability does not determine folding kinetics, emphasizing the opportunity for extending design criteria to target kinetic features of the energy landscape.

We have developed an algorithm for designing multiple sequences of nucleic acids that have a uniform melting temperature between the sequence and its complement and that do not hybridize nonspecifically with each other based on the minimum free energy (DGmin). Sequences that satisfy these constraints can be utilized in computations, various engineering applications such as microarrays, and nano-fabrications. Our algorithm is a random generate-and-test algorithm: it generates a candidate sequence randomly and tests whether the sequence satisfies the constraints. The novelty of our algorithm is that the filtering method uses a greedy search to calculate DGmin. This effectively excludes inappropriate sequences before DGmin is calculated, thereby reducing computation time drastically when compared with an algorithm without the filtering. Experimental results in silico showed the superiority of the greedy search over the traditional approach based on the hamming distance. In addition, experimental results in vitro demonstrated that the experimental free energy (DGexp) of 126 sequences correlated well with DGmin ( jRj = 0.90) than with the hamming distance ( jRj = 0.80). These results validate the rationality of a thermodynamic approach. We implemented our algorithm in a graphic user interface-based program written in Java.

Intelligent nanomechanical devices that operate in an autonomous fashion are of great theoretical and practical interest. Recent successes in building large scale DNA nanostructures, in constructing DNA mechanical devices, and in DNA computing provide a solid foundation for the next step forward: designing autonomous DNA mechanical devices capable of arbitrarily complex behavior. One prototype system towards this goal can be a DNA mechanical device that is capable of universal computation, by mimicking the operation of a universal Turing machine. Building on our prior theoretical designs and a prototype experimental construction of autonomous unidirectional DNA walking devices that move along linear tracks, we present in this paper the design of a nanomechanical DNA device that autonomously mimics the operation of a 2-state 5color universal Turing machine. Our autonomous nanomechanical device, which we call an Autonomous DNA Turing Machine, is thus capable of universal computation and hence complex translational motion which we define as universal translational motion.

My acquaintance with Ned Seeman began in the Caltech library sometime during 1992. At the time I was trying to design a DNA computer and was collecting papers in an attempt to learn all the biochemical tricks ever performed with DNA. Among the papers was Ned and Junghuei Chen’s beautiful construction of a DNA cube [1]. I had no idea how to harness such a marvel for computation – the diagrams explaining the cube were in a visual language that I could not parse and its static structure, once formed, did not seem to allow further information processing. However, I was in awe of the cube and wondered what kind of mad and twisted genius had conjured it. Ned’s DNA sculptures did turn out to have a relationship to computation. In 1994 Len Adleman’s creation of a DNA computer [2] showed that linear DNA self-assembly, together with operations such as PCR, could tackle NP-complete computational problems. Excited by this result, Erik Winfree quickly forged an amazing link that showed how the self-assembly of geometrical DNA objects, alone, can perform universal computation [3]. The

Abstract. The design of DNA sequences is a key problem for implementing molecular self-assembly with nucleic acid molecules. These molecules must meet several physical, chemical and logical requirements, mainly to avoid mishybridization. Since manual selection of proper sequences is too time-consuming for more than a handful of molecules, the aid of computer programs is advisable. In this paper two software tools for designing DNA sequences are presented, the DNASequenceGenerator and the DNASequence-Compiler. Both employ an approach of sequence dissimilarity based on the uniqueness of overlapping subsequences and a graph based algorithm for sequence generation. Other sequence properties like melting temperature or forbidden subsequences are also regarded, but not secondary structure errors or equilibrium chemistry. Fields of application are DNA computing and DNA-based nanotechnology. In the second part of this paper, sequences generated with the DNASequenceGenerator are compared to those from several publications of other groups, an example application for the DNASequenceCompiler is presented, and the advantages and disadvantages of the presented approach are discussed. 1

This paper provides a description of the recent evolution in the US of an emerging technology known as DNA Computation or more generally as Biomolecular Computation from its early stages to its development and extension into other areas such as nanotechnology, emerging as a viable sub-discipline of science and engineering.

this paper, I first describe the directions of current research in the field of DNA and molecular computing by summarizing a recent international conference in this field: DNA8, the Eighth Intetmational Meeting on DNA Based Computers. Research in this field has clearly shifted from purely mathematical computations to broader applications in nanotechnology and biotechnology, and the principles and methods for designing molecular systems with information-processing capability for such applications are considered important. We call research into designing such molecular systems molecular programming. This paper reviews existing models of DNA and molecular computation and analyzes the results of these models, and then briefly describes some methods for molecular programming, including sequence design. It finally touches on molecular machines made of DNA, the current focus of molecular programming

Abstract. Recent experimental progress in DNA lattice construction, DNA robotics, and DNA computing provides the basis for designing DNA cellular computing devices, i.e. autonomous nano-mechanical DNA computing devices embedded in DNA lattices. Once assembled, DNA cellular computing devices can serve as reusable, compact computing devices that perform (universal) computation, and programmable robotics devices that demonstrate complex motion. As a prototype of such devices, we recently reported the design of an Autonomous DNA Turing Machine, which is capable of universal sequential computation, and universal translational motion, i.e. the motion of the head of a single tape universal mechanical Turing machine. In this paper, we describe the design of an Autonomous DNA Cellular Automaton (ADCA), which can perform parallel universal computation by mimicking a one-dimensional (1D) universal cellular automaton. In the computation process, this device, embedded in a 1D DNA lattice, also demonstrates well coordinated parallel motion. The key technical innovation here is a molecular mechanism that synchronizes pipelined “molecular reaction waves”

Abstract. We present a theoretical model for self-assembling tiles with flexible branches motivated by DNA branched junction molecules. We encode an instance of a “problem ” as a pot of such tiles, and a “solution” as an assembled complete complex without any free sticky ends (called ports), whose number of tiles is within predefined bounds. We develop an algebraic representation of this self-assembly process and use it to prove that this model of self-assembly precisely captures NP-computability when the number of tiles in the minimal complete complexes is bounded by a polynomial. 1

ABSTRACT Double crossover molecules are DNA structures containing two Holliday junctions connected by two double helical arms. There are several types of double crossover molecules, differentiated by the relative orientations of their helix axes, parallel or antiparallel, and by the number of double helical half-turns (even or odd) between the two crossovers. They are found as intermediates in meiosis and they have been used extensively in structural DNA nanotechnology for the construction of one-dimensional and two-dimensional arrays and in a DNA nanomechanical device. Whereas the parallel double helical molecules are usually not well behaved, we have focused on the antiparallel molecules; antiparallel molecules with an even number of half-turns between crossovers (termed DAE molecules) produce a reporter strand when ligated, facilitating their characterization in a ligation cyclization assay. Hence, we have estimated the flexibility of antiparallel DNA double crossover molecules by means of ligation-closure experiments. We are able to show that these molecules are approximately twice as rigid as linear duplex DNA.