Extraordinary energy efficiency.

Much of our scientific, technological, and economic future depends on the availability of an ever-increasing supply of computational power. However, the increasing demand for such power has pushed electronic technology to the limit of physical feasibility and has raised the concern that this technology may not be able to sustain our growth in the near future. It became important to consider an alternative means of achieving computational power. In this regard, DNA computing was introduced based on the usage of DNA and molecular biology hardware instead of the typical silicon based technology. The molecular computers could take advantage of DNA's physical properties to store information and perform calculations. These include extremely dense information storage, enormous parallelism and extraordinary energy efficiency. One of the main advantages that DNA computations would add to computation is its self - parallel processing while most of the electronic computers now use linear processing. In this paper, the DNA computation is reviewed and its state of the art challenges and applications are presented. Some of these applications are those require fast processing, at which DNA computers would be able to solve the hardest problems faster than the traditional ones. For example, 10 trillion DNA molecules can fit in one cubic centimeter that would result in a computer that holds 10 terabytes of data. Moreover, this work focuses on whether a large scale molecular computer can be built. 

Massively parallel computation

DNA computation investigates the potential of DNA as a massively parallel computing device. Research is focused on designing parallel computation models executable by DNA based chemical processes and on developing algorithms in the models. L. Adleman (1994) initiated this area of research by presenting a DNA based method for solving the Hamilton Path Problem. That contribution raised the hope that parallel computation by DNA could be used to tackle NP-complete problems which are thought of as intractable. The current realization however, is that NP-complete problems may not be best suited for DNA based (more generally, molecule based) computing. A better subject for DNA computing could be large scale evaluation of parallel computation models. Several proposals have been made in this direction. We overview those methods, discuss technical and theoretical issues involved, and present some possible applications of those methods. 

DENSE DATA STORAGE

DNA digital data storage refers to any process to store digital data in the base sequence of DNA. This technology uses artificial DNA made using commercially available oligonucleotide synthesis machines for storage and DNA sequencing machines for retrieval. This type of storage system is more compact than current magnetic tape or hard drive-storage systems due to the data density of the DNA. Currently it was reported that in 1 gram of DNA 215 petabytes (215 million gigabytes) could be stored. It also has the capability for longevity, as long as the DNA is held in cold, dry and dark conditions, as is shown by the study of woolly mammoth DNA from up to 60,000 years ago, and for resistance to obsolescence, as DNA is a universal and fundamental data storage mechanism in biology. These features have led to researchers involved in their development to call this method of data storage "apocalypse-proof" because "after a hypothetical global disaster, future generations might eventually find the stores and be able to read them." It is, however, a slow process, as the DNA needs to be sequenced in order to retrieve the data, and so the method is intended for uses with a low access rate such as long-term archival of large amounts of scientific data.

PROPERTIES OF A DNA COMPUTER

Dense data storage.

Massively parallel computation. 

Extraordinary energy efficiency.

EVOLUTION OF THE DNA COMPUTER

A major improvement in DNA information storage and retrieval has been announced in Science Magazine. Two scientists from Columbia University and the New York Genome Center report a new high bar for DNA data storage — over two orders of magnitude better than previous attempts. They were able to encode text, images, a movie, and an operating system in 2 megabytes of DNA, and retrieve it back perfectly in multiple trials. News from Columbia University Data Science Institute says,

Humanity may soon generate more data than hard drives or magnetic tape can handle, a problem that has scientists turning to nature’s age-old solution for information-storage — DNA.

In a new study in Science, a pair of researchers at Columbia University and the New York Genome Center (NYGC) show that an algorithm designed for streaming video on a cellphone can unlock DNA’s nearly full storage potential by squeezing more information into its four base nucleotides. They demonstrate that this technology is also extremely reliable. [Emphasis added.]

DNA & DNA Computing

DNA: Definition & Structure

Deoxyribonucleic acid or DNA is a molecule that contains the instructions an organism needs to develop, live and reproduce. These instructions are found inside every cell, and are passed down from parents to their children.

DNA structure

DNA is made up of molecules called nucleotides. Each nucleotide contains a phosphate group, a sugar group and a nitrogen base. The four types of nitrogen bases are adenine (A), thymine (T), guanine (G) and cytosine (C). The order of these bases is what determines DNA's instructions, or genetic code. Human DNA has around 3 billion bases, and more than 99 percent of those bases are the same in all people, according to the U.S. National Library of Medicine (NLM).

Similar to the way the order of letters in the alphabet can be used to form a word, the order of nitrogen bases in a DNA sequence forms genes, which in the language of the cell, tells cells how to make proteins. Another type of nucleic acid, ribonucleic acid, or RNA, translates genetic information from DNA into proteins.

Nucleotides are attached together to form two long strands that spiral to create a structure called a double helix. If you think of the double helix structure as a ladder, the phosphate and sugar molecules would be the sides, while the bases would be the rungs. The bases on one strand pair with the bases on another strand: adenine pairs with thymine, and guanine pairs with cytosine.

DNA molecules are long — so long, in fact, that they can't fit into cells without the right packaging. To fit inside cells, DNA is coiled tightly to form structures we call chromosomes. Each chromosome contains a single DNA molecule. Humans have 23 pairs of chromosomes, which are found inside the cell's nucleus. 

DNA Computing

DNA computing is a branch of computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of DNA computing. The term "molectronics" has sometimes been used, but this term had already been used for an earlier technology, a then-unsuccessful rival of the first integrated circuits, this term has also been used more generally, for molecular-scale electronic technology.

This field was initially developed by Leonard Adleman of the University of Southern California, in 1994. Adleman demonstrated a proof-of-concept use of DNA as a form of computation which solved the seven-point Hamiltonian path problem. Since the initial Adleman experiments, advances have been made and various Turing machines have been proven to be constructible.

While the initial interest was in using this novel approach to tackle NP-hard problems, it was soon realized that they may not be best suited for this type of computation, and several proposals have been made to find a "killer application" for this approach. In 1997, computer scientist Mitsunori Ogihara working with biologist Animesh Ray suggested one to be the evaluation of Boolean circuits and described an implementation.

In 2002, researchers from the Weizmann Institute of Science in Rehovot, Israel, unveiled a programmable molecular computing machine composed of enzymes and DNA molecules instead of silicon microchips. On April 28, 2004, Ehud Shapiro, Yaakov Benenson, Binyamin Gil, Uri Ben-Dor, and Rivka Adar at the Weizmann Institute announced in the journal Nature that they had constructed a DNA computer coupled with an input and output module which would theoretically be capable of diagnosing cancerous activity within a cell, and releasing an anticancer drug upon diagnosis.

In January 2013, researchers were able to store a JPEG photograph, a set of Shakespearean sonnets, and an audio file of Martin Luther King, speech I Have a Dream on DNA digital data storage.

In March 2013, researchers created a transcriptor (a biological transistor).

In August 2016, researchers used the CRISPR gene-editing system to insert a GIF of a galloping horse and rider into the DNA of living bacteria.

BIOLOGICAL COMPUTING

Bio computers use systems of biologically derived molecules—such as DNA and proteins—to perform computational calculations involving storing, retrieving, and processing data.

The development of bio-computers has been made possible by the expanding new science of NANO-Biotechnology. The term NANO-Biotechnology can be defined in multiple ways; in a more general sense, NANO-Biotechnology can be defined as any type of technology that uses both NANO-Scale materials (i.e. materials having characteristic dimensions of 1- 100 nanometers) and biologically based materials.

A more restrictive definition views NANO-Biotechnology more specifically as the design and engineering of proteins that can then be assembled into larger, functional structures.

The implementation of NANO-Biotechnology, as defined in this narrower sense, provides scientists with the ability to engineer bio-molecular systems specifically so that they interact in a fashion that can ultimately result in the computational functionality of a computer. 

Why Bio-Computing?

Silicon microprocessors have been the heart of the computing world for more than 40 years. In that time, manufacturers have crammed more and more electronic devices onto their microprocessors. In accordance with Moore's Law, the number of electronic devices put on a microprocessor has doubled every 18 months. Moore's Law is named after Intel founder Gordon Moore, who predicted in 1965 that microprocessors would double in complexity every two years. Many have predicted that Moore's Law will soon reach its end, because of the physical speed and miniaturization limitations of silicon microprocessors.

DNA computers have the potential to take computing to new levels, picking up where Moore's Law leaves off. There are several advantages to using DNA instead of silicon:

• As long as there are cellular organisms, there will always be a supply of DNA.
• The large supply of DNA makes it a cheap resource. 
• Unlike the toxic materials used to make traditional microprocessors, DNA bio-chips can be made cleanly.
• DNA computers are many times smaller than today's computers. 

DNA's key advantage is that it will make computers smaller than any computer that has come before them, while at the same time holding more data. One pound of DNA has the capacity to store more information than all the electronic computers ever built; and the computing power of a teardrop-sized DNA computer, using the DNA logic gates, will be more powerful than the world's most powerful supercomputer. More than 10 trillion DNA molecules can fit into an area no larger than 1 cubic centimeter (0.06 cubic inches). With this small amount of DNA, a computer would be able to hold 10 terabytes of data, and perform 10 trillion calculations at a time. By adding more DNA, more calculations could be performed.

Unlike conventional computers, DNA computers perform calculations parallel to other calculations. Conventional computers operate linearly, taking on tasks one at a time. It is parallel computing that allows DNA to solve complex mathematical problems in hours, whereas it might take electrical computers hundreds of years to complete them.

The first DNA computers are unlikely to feature word processing, e-mailing and solitaire programs. Instead, their powerful computing power will be used by national governments for cracking secret codes, or by airlines wanting to map more efficient routes. Studying DNA computers may also lead us to a better understanding of a more complex computer -- the human brain.

BIO COMPUTING

INTRODUCTION

Research at the border of Molecular Biology and Information Technology has witnessed in recent years an exciting development, with remarkable benefits for both areas. On one hand, biological data is being produced at an astounding rate nowadays, supported by the ever increasing advances in biotechnology, and IT-related tools are necessary to handle the data, interpret them, visualize various parameters, etc. Moreover, many combinatoric problems related to these biological data need IT-specific approaches. On the other hand, the biological systems have huge capabilities for information storing, data manipulation, pattern recognition, parallelism, and energy efficiency, that makes them interesting for computer scientists. 

Bio computing is often used as a catch-all term covering all this area at the intersection of Biology and Computation, although many other terms are used to name the same area. There are four joint sub-fields:

• Computational Biology - this includes efforts to solve biological problems with computational tools (such as modeling, algorithms, heuristics).
• Bioinformatics - this includes management of biological databases, data mining and data modeling, as well as IT-tools for data visualization.
• DNA computing and NANO-engineering - this includes models and experiments to use DNA and other molecules to perform computations.
• Computations in living organisms - this is concerned with constructing computational components in living cells, as well as with studying computational processes taking place daily in living organisms.

What is Bioinformatics?

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines Computer Science, Biology, Mathematics, and Engineering to analyze and interpret biological data. Bioinformatics has been used for in silicon analyses of biological queries using mathematical and statistical techniques. More broadly, bioinformatics is applied statistics and computing to biological science.