Anne Condon

DNA computing on surfaces

DNA computing was proposed as a means of solving a class of intractable computational problems in which the computing time can grow exponentially with problem size (the ‘NP-complete’ or non-deterministic polynomial time complete problems). The principle of the technique has been demonstrated experimentally for a simple example of the hamiltonian path problem (in this case, finding an airline flight path between several cities, such that each city is visited only once). DNA computational approaches to the solution of other problems have also been investigated. One technique involves the immobilization and manipulation of combinatorial mixtures of DNA on a support. A set of DNA molecules encoding all candidate solutions to the computational problem of interest is synthesized and attached to the surface. Successive cycles of hybridization operations and exonuclease digestion are used to identify and eliminate those members of the set that are not solutions. Upon completion of all the multi-step cycles, the solution to the computational problem is identified using a polymerase chain reaction to amplify the remaining molecules, which are then hybridized to an addressed array. The advantages of this approach are its scalability and potential to be automated (the use of solid-phase formats simplifies the complex repetitive chemical processes, as has been demonstrated in DNA and protein synthesis). Here we report the use of this method to solve a NP-complete problem. We consider a small example of the satisfiability problem (SAT), in which the values of a set of boolean variables satisfying certain logical constraints are determined.

The complexity of stochastic games

Abstract We consider the complexity of stochastic games—simple games of chance played by two players. We show that the problem of deciding which player has the greatest chance of winning the game is in the class NP ⌢ co- NP .

RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

BackgroundThe ability to access, search and analyse secondary structures of a large set of known RNA molecules is very important for deriving improved RNA energy models, for evaluating computational predictions of RNA secondary structures and for a better understanding of RNA folding. Currently there is no database that can easily provide these capabilities for almost all RNA molecules with known secondary structures.ResultsIn this paper we describe RNA STRAND – the RNA secondary STRucture and statistical ANalysis Database, a curated database containing known secondary structures of any type and organism. Our new database provides a wide collection of known RNA secondary structures drawn from public databases, searchable and downloadable in a common format. Comprehensive statistical information on the secondary structures in our database is provided using the RNA Secondary Structure Analyser, a new tool we have developed to analyse RNA secondary structures. The information thus obtained is valuable for understanding to which extent and with which probability certain structural motifs can appear. We outline several ways in which the data provided in RNA STRAND can facilitate research on RNA structure, including the improvement of RNA energy models and evaluation of secondary structure prediction programs. In order to keep up-to-date with new RNA secondary structure experiments, we offer the necessary tools to add solved RNA secondary structures to our database and invite researchers to contribute to RNA STRAND.ConclusionRNA STRAND is a carefully assembled database of trusted RNA secondary structures, with easy on-line tools for searching, analyzing and downloading user selected entries, and is publicly available at http://www.rnasoft.ca/strand.

Designed DNA molecules: principles and applications of molecular nanotechnology

Long admired for its informational role in the cell, DNA is now emerging as an ideal molecule for molecular nanotechnology. Biologists and biochemists have discovered DNA sequences and structures with new functional properties, which are able to prevent the expression of harmful genes or detect macromolecules at low concentrations. Physical and computational scientists can design rigid DNA structures that serve as scaffolds for the organization of matter at the molecular scale, and can build simple DNA-computing devices, diagnostic machines and DNA motors. The integration of biological and engineering advances offers great potential for therapeutic and diagnostic applications, and for nanoscale electronic engineering.

RNAsoft: A suite of RNA secondary structure prediction and design software tools.

DNA and RNA strands are employed in novel ways in the construction of nanostructures, as molecular tags in libraries of polymers and in therapeutics. New software tools for prediction and design of molecular structure will be needed in these applications. The RNAsoft suite of programs provides tools for predicting the secondary structure of a pair of DNA or RNA molecules, testing that combinatorial tag sets of DNA and RNA molecules have no unwanted secondary structure and designing RNA strands that fold to a given input secondary structure. The tools are based on standard thermodynamic models of RNA secondary structure formation. RNAsoft can be found online at http://www.RNAsoft.ca.

Efficient parameter estimation for RNA secondary structure prediction

MOTIVATION Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. The most widely used model, the Turner99 model, has hundreds of parameters, and so a robust parameter estimation scheme should efficiently handle large data sets with thousands of structures. Moreover, the estimation scheme should also be trained using available experimental free energy data in addition to structural data. RESULTS In this work, we present constraint generation (CG), the first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data. Our CG approach employs a novel iterative scheme, whereby the energy values are first computed as the solution to a constrained optimization problem. Then the newly computed energy parameters are used to update the constraints on the optimization function, so as to better optimize the energy parameters in the next iteration. Using our method on biologically sound data, we obtain revised parameters for the Turner99 energy model. We show that by using our new parameters, we obtain significant improvements in prediction accuracy over current state of-the-art methods. AVAILABILITY Our CG implementation is available at http://www.rnasoft.ca/CG/.

Strand design for biomolecular computation

The design of DNA or RNA strands for DNA computations poses many new questions in algorithms and coding theory. DNA strand design also arises in use of molecular bar codes to manipulate and identify individual molecules in complex chemical libraries, and to attach molecules to DNA chips. We survey several formulations of the DNA strand design problem, along with results and open questions in this area.

A Surface-Based Approach to DNA Computation

A scalable approach to DNA-based computations is described. Complex combinatorial mixtures of DNA molecules encoding all possible answers to a computational problem are synthesized and attached to the surface of a solid support. This set of molecules is queried in successive MARK (hybridization) and DESTROY (enzymatic digestion) operations. Determination of the sequence of the DNA molecules remaining on the surface after completion of these operations yields the answer to the computational problem. Experimental demonstrations of aspects of the strategy are presented.

Specifying and verifying a broadcast and a multicast snooping cache coherence protocol

We develop a specification methodology that documents and specifies a cache coherence protocol in eight tables: the states, events, actions, and transitions of the cache and memory controllers. We then use this methodology to specify a detailed, modern three-state broadcast snooping protocol with an unordered data network and an ordered address network that allows arbitrary skew. We also present a detailed specification of a new protocol called multicast snooping (Bilir et al., 1999) and, in doing so, we better illustrate the utility of the table-based specification methodology. Finally, we demonstrate a technique for verification of the multicast snooping protocol, through the sketch of a manual proof that the specification satisfies a sequentially consistent memory model.

Classifying RNA pseudoknotted structures

Computational prediction of the minimum free energy (mfe) secondary structure of an RNA molecule from its base sequence is valuable in understanding the structure and function of the molecule. Since the general problem of predicting pseudoknotted secondary structures is NP-hard, several algorithms have been proposed that find the mfe secondary structure from a restricted class of secondary structures. In this work, we order the algorithms by generality of the structure classes that they handle. We provide simple characterizations of the classes of structures handled by four algorithms, as well as linear time methods to test whether a given secondary structure is in three of these classes. We report on the percentage of biological structures from the PseudoBase and Gutell databases that are handled by these three algorithms.