Controlling Matter at the Molecular Scale with DNA Circuits.

In recent years, a diverse set of mechanisms have been developed that allow DNA strands with specific sequences to sense information in their environment and to control material assembly, disassembly, and reconfiguration. These sequences could serve as the inputs and outputs for DNA computing circuits, enabling DNA circuits to act as chemical information processors to program complex behavior in chemical and material systems. This review describes processes that can be sensed and controlled within such a paradigm. Specifically, there are interfaces that can release strands of DNA in response to chemical signals, wavelengths of light, pH, or electrical signals, as well as DNA strands that can direct the self-assembly and dynamic reconfiguration of DNA nanostructures, regulate particle assemblies, control encapsulation, and manipulate materials including DNA crystals, hydrogels, and vesicles. These interfaces have the potential to enable chemical circuits to exert algorithmic control over responsive materials, which may ultimately lead to the development of materials that grow, heal, and interact dynamically with their environments.

Binary counting with chemical reactions.

This paper describes a scheme for implementing a binary counter with chemical reactions. The value of the counter is encoded by logical values of "0" and "1" that correspond to the absence and presence of specific molecular types, respectively. It is incremented when molecules of a trigger type are injected. Synchronization is achieved with reactions that produce a sustained three-phase oscillation. This oscillation plays a role analogous to a clock signal in digital electronics. Quantities are transferred between molecular types in different phases of the oscillation. Unlike all previous schemes for chemical computation, this scheme is dependent only on coarse rate categories for the reactions ("fast" and "slow"). Given such categories, the computation is exact and independent of the specific reaction rates. Although conceptual for the time being, the methodology has potential applications in domains of synthetic biology such as biochemical sensing and drug delivery. We are exploring DNA-based computation via strand displacement as a possible experimental chassis.

Rate-independent constructs for chemical computation.

This paper presents a collection of computational modules implemented with chemical reactions: an inverter, an incrementer, a decrementer, a copier, a comparator, and a multiplier. Unlike previous schemes for chemical computation, ours produces designs that are dependent only on coarse rate categories for the reactions ("fast" vs. "slow"). Given such categories, the computation is exact and independent of the specific reaction rates. We validate our designs through stochastic simulations of the chemical kinetics. Although conceptual for the time being, our methodology has potential applications in domains of synthetic biology such as biochemical sensing and drug delivery. We are exploring DNA-based computation via strand displacement as a possible experimental chassis.

DNA-based random number generation in security circuitry.

DNA-based circuit design is an area of research in which traditional silicon-based technologies are replaced by naturally occurring phenomena taken from biochemistry and molecular biology. This research focuses on further developing DNA-based methodologies to mimic digital data manipulation. While exhibiting fundamental principles, this work was done in conjunction with the vision that DNA-based circuitry, when the technology matures, will form the basis for a tamper-proof security module, revolutionizing the meaning and concept of tamper-proofing and possibly preventing it altogether based on accurate scientific observations. A paramount part of such a solution would be self-generation of random numbers. A novel prototype schema employs solid phase synthesis of oligonucleotides for random construction of DNA sequences; temporary storage and retrieval is achieved through plasmid vectors. A discussion of how to evaluate sequence randomness is included, as well as how these techniques are applied to a simulation of the random number generation circuitry. Simulation results show generated sequences successfully pass three selected NIST random number generation tests specified for security applications.

Statistical measures of uncertainty for branches in phylogenetic trees inferred from molecular sequences by using model-based methods.

In recent years, the emphasis of theoretical work on phylogenetic inference has shifted from the development of new tree inference methods to the development of methods to measure the statistical support for the topologies. This paper reviews 3 approaches to assign support values to branches in trees obtained in the analysis of molecular sequences: the bootstrap, the Bayesian posterior probabilities for clades, and the interior branch tests. In some circumstances, these methods give different answers. It should not be surprising: their assumptions are different. Thus the interior branch tests assume that a given topology is true and only consider if a particular branch length is longer than zero. If a tree is incorrect, a wrong branch (a low bootstrap or Bayesian support may be an indication) may have a non-zero length. If the substitution model is oversimplified, the length of a branch may be overestimated, and the Bayesian support for the branch may be inflated. The bootstrap, on the other hand, approximates the variance of the data under the real model of sequence evolution, because it involves direct resampling from this data. Thus the discrepancy between the Bayesian support and the bootstrap support may signal model inaccuracy. In practical application, use of all 3 methods is recommended, and if discrepancies are observed, then a careful analysis of their potential origins should be made.

Using simple rules on presence and positioning of motifs for promoter structure modeling and tissue-specific expression prediction.

Regulation of transcription is controlled by sets of transcription factors binding specific sites in the regulatory regions of genes. It is therefore believed that regulatory regions driving similar expression profiles share some common structural features. We here introduce a computational approach for finding a small set of rules describing the presence and positioning of motifs in a set of promoter sequences. This rule set is subsequently used for finding promoters that drive similar expression profiles from a genomic set of sequences. We applied our approach on muscle-expressed genes in Caenorhabditis elegans. We obtained a high average performance, and in the best case we found that almost 50% of true positive test genes scored higher than 90% of the true negative test genes. High scoring non-training sequences were enriched for muscle-expressed genes, and predicted motifs fitting the rules showed a significant tendency to be present in experimentally verified regulatory regions. Our model is more general than existing cis-regulatory module models, as rules selected by our model contain a variety of information, including not only proximal but also distal positioning of pairs of motifs, positioning with regard to the translation start site, and simply presences of motifs. We believe our model can help to increase our understanding about transcription factor cooperation and transcription initiation.

DNA computing of solutions to knapsack problems.

One line of DNA computing research focuses on parallel search algorithms, which can be used to solve many optimization problems. DNA in solution can provide an enormous molecular library, which can be searched by molecular biological techniques. We have implemented such a parallel search for solutions to knapsack problems, which ask for the best way to pack a knapsack of limited volume. Several instances of knapsack problems were solved using DNA. We demonstrate how the computations can be extended by in vivo translation of the DNA library into protein. This combination of DNA and protein allows for multi-criterion optimization. The knapsack computations performed can then be seen as protein optimizations, one of the most complex computations performed by natural systems.