Challenges and applications for self-assembled DNA nanostructures?

Capturing the embryonic stages of self-assembly - design rules for molecular computation

The drive towards organic computing is gaining momentum. Interestingly, the building blocks for such architectures is based on molecular ensembles extending from nucleic acids to synthetic molecules. Advancement in this direction requires devising precise nanoscopic experiments and model calculations to decipher the mechanisms governing the integration of a large number of molecules over time at room-temperature. Here, we report on ultrahigh-resolution scanning tunnelling microscopic measurements to register the motion of molecules in the absence of external stimulus in liquid medium. We observe the collective behavior of individual molecules within a swarm which constantly iterate their position to attain an energetically favourable site. Our approach provides a consistent pathway to register molecular self-assembly in sequential steps from visualising thermodynamically driven repair of defects up until the formation of a stable two-dimensional configuration. These elemental findings on molecular surface dynamics, self-repair and intermolecular kinetic pathways rationalised by atom-scale simulations can be explored for developing new models in algorithmic self-assembly to realisation of evolvable hardware.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

SELF-RECOGNITION OF DNA FROM LIFE PROCESSES TO DNA COMPUTATION

Ever since the first appearance of deoxyribose nucleic acid (DNA) in 1953, it has fascinated multitudes with its simplicity. With a modest syllabus of four nucleotides (adenine, thymine, cytosine and guanine), it codes for the complexity of life around us. In this paper, we investigate how the structure of DNA codes for life processes and how we can take advantage of its minuscule size, mechanism of self-recognition and self-assembly for "bottom-up" nanotechnology. High hopes are also placed on miniaturizing present computing technology using DNA computing based on two fundamental features; massive parallelism of DNA strands and Watson-Crick complementarity. Advances in DNA-based computation and algorithmic assembly are then used to complement researches in DNA nanotechnology.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

Molecular solutions for the subset-sum problem on DNA-based supercomputing

In this paper our main purpose is to give molecular solutions for the subset-sum problem. In order to achieve this, we propose a DNA-based algorithm of an n-bit parallel adder and a DNA-based algorithm of an n-bit parallel comparator to formally verify our designed molecular solutions for the subset-sum problem.

Solving the set cover problem and the problem of exact cover by 3-sets in the Adleman–Lipton model

Adleman wrote the first paper in which it is shown that deoxyribonucleic acid (DNA) strands could be employed towards calculating solutions to an instance of the NP-complete Hamiltonian path problem (HPP). Lipton also demonstrated that Adleman's techniques could be used to solve the NP-complete satisfiability (SAT) problem (the first NP-complete problem). In this paper, it is proved how the DNA operations presented by Adleman and Lipton can be used for developing DNA algorithms to resolving the set cover problem and the problem of exact cover by 3-sets.