DNA computing in the context of Leonard Adleman

Nucleic acid secondary structure is the basepairing interactions within a single nucleic acid polymer or between two polymers. It can be represented as a list of bases which are paired in a nucleic acid molecule.The secondary structures of biological DNAs and RNAs tend to be different: biological DNA mostly exists as fully base paired double helices, while biological RNA is single stranded and often forms complex and intricate base-pairing interactions due to its increased ability to form hydrogen bonds stemming from the extra hydroxyl group in the ribose sugar.

In a non-biological context, secondary structure is a vital consideration in the nucleic acid design of nucleic acid structures for DNA nanotechnology and DNA computing, since the pattern of basepairing ultimately determines the overall structure of the molecules.

Nucleic acid design is the process of generating a set of nucleic acid base sequences that will associate into a desired conformation. Nucleic acid design is central to the fields of DNA nanotechnology and DNA computing. It is necessary because there are many possible sequences of nucleic acid strands that will fold into a given secondary structure, but many of these sequences will have undesired additional interactions which must be avoided. In addition, there are many tertiary structure considerations which affect the choice of a secondary structure for a given design.

Nucleic acid design has similar goals to protein design: in both, the sequence of monomers is rationally designed to favor the desired folded or associated structure and to disfavor alternate structures. However, nucleic acid design has the advantage of being a much computationally simpler problem, since the simplicity of Watson-Crick base pairing rules leads to simple heuristic methods which yield experimentally robust designs. Computational models for protein folding require tertiary structure information whereas nucleic acid design can operate largely on the level of secondary structure. However, nucleic acid structures are less versatile than proteins in their functionality.

DNA nanotechnology is the design and manufacture of artificial nucleic acid structures for technological uses. In this field, nucleic acids are used as non-biological engineering materials for nanotechnology rather than as the carriers of genetic information in living cells. Researchers in the field have created static structures such as two- and three-dimensional crystal lattices, nanotubes, polyhedra, and arbitrary shapes, and functional devices such as molecular machines and DNA computers. The field is beginning to be used as a tool to solve basic science problems in structural biology and biophysics, including applications in X-ray crystallography and nuclear magnetic resonance spectroscopy of proteins to determine structures. Potential applications in molecular scale electronics and nanomedicine are also being investigated.

The conceptual foundation for DNA nanotechnology was first laid out by Nadrian Seeman in the early 1980s, and the field began to attract widespread interest in the mid-2000s. This use of nucleic acids is enabled by their strict base pairing rules, which cause only portions of strands with complementary base sequences to bind together to form strong, rigid double helix structures. This allows for the rational design of base sequences that will selectively assemble to form complex target structures with precisely controlled nanoscale features. Several assembly methods are used to make these structures, including tile-based structures that assemble from smaller structures, folding structures using the DNA origami method, and dynamically reconfigurable structures using strand displacement methods. The field's name specifically references DNA, but the same principles have been used with other types of nucleic acids as well, leading to the occasional use of the alternative name nucleic acid nanotechnology.

A molecular logic gate is a molecule that performs a logical operation based on at least one physical or chemical inputs and a single output. The field has advanced from simple logic systems based on a single chemical or physical input to molecules capable of combinatorial and sequential operations such as arithmetic operations (i.e. moleculators and memory storage algorithms). Molecular logic gates work with input signals based on chemical processes and with output signals based on spectroscopic phenomena.

Logic gates are the fundamental building blocks of computers, microcontrollers and other electrical circuits that require one or more logical operations. They can be used to construct digital architectures with varying degrees of complexity by a cascade of a few to several million logic gates, and are essentially physical devices that produce a singular binary output after performing logical operations based on Boolean functions on one or more binary inputs. The concept of molecular logic gates, extending the applicability of logic gates to molecules, aims to convert chemical systems into computational units. The field has evolved to realize several practical applications in fields such as molecular electronics, biosensing, DNA computing, nanorobotics, and cell imaging.  

The concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology. As a field, biological computation can include the study of the systems biology computations performed by biota, the design of algorithms inspired by the computational methods of bio-data, the design and engineering of manufactured computational devices using synthetic biology and computer methods for biological data, Computational Biology. This extenuates DNA Computation, Evolutionary Computation, Autonomic Computation, Morphological Computation, Morphogenetic Computation, Amorphous Computation, and Hyperdimensional Computation.

According to Dominique Chu, Mikhail Prokopenko, and J. Christian J. Ray, "the most important class of natural computers can be found in biological systems that perform computation on multiple levels. From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets, we so far lack principles to understand rigorously how computation is done in living, or active, matter".