DNA computing
DNA computing is an innovative approach that utilizes the molecular structure of DNA to solve complex problems and process information, distinct from traditional silicon-based computing. First conceptualized in 1994 by Leonard Adleman, DNA computing leverages the naturally occurring properties of DNA to operate on vast amounts of data simultaneously. This method has demonstrated remarkable potential in areas such as medical diagnostics and treatment, particularly in detecting and targeting diseases like cancer at a molecular level with precision.
While DNA computers can perform numerous simple calculations efficiently, they face significant limitations, including slower processing speeds and reliance on specific biochemical reactions. As a result, they require extensive maintenance and are often seen as impractical for broader computing tasks. However, advances from research institutions have sparked renewed interest in DNA computing, especially in its application to medicine. Notably, researchers have made strides in developing DNA nanobots capable of responding to specific genetic markers, offering the promise of targeted therapies with reduced side effects.
Overall, while the field of DNA computing is still evolving and has not replaced conventional computing, its unique capabilities could offer revolutionary breakthroughs, particularly in healthcare and genetic research.
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DNA computing
DNA computing makes use of the structure of genetic material to solve problems, process data, and make calculations. The concept was first discovered in 1994. While typical computer hardware consists of silicon-based circuitry, DNA computers use DNA as hardware, making use of its natural functions of storing and processing information. Its advantage over more traditional computer designs is that due to DNA's structure, it can compute many different factors simultaneously. DNA computers are extremely small in comparison to their silicon counterparts. They have also shown the capacity to process information about organisms and administer treatments on a molecular level. They have displayed great potential to detect and eliminate cancers and other diseases with great precision.
However, DNA computers have significant drawbacks that prevent them from replacing traditional computers. They process data at a rate that is relatively slow. They also rely on very specific biochemical reactions to obtain data. Particular molecules can only be used once because they are forever changed by these reactions, meaning DNA computers require a great deal of maintenance and manipulation between computations.
Background
DNA, or deoxyribonucleic acid, is the material that contains life-forms' genetic code. It is made up of molecules called nucleotides, which consist of phosphate, sugar, and one of four different nitrogen bases. Different traits of an organism are formed by different arrangements of these bases. Scientists have learned to identify many of these sequences, which are often represented by writing out the letters that the four bases start with. The bases are adenine, cytosine, guanine, and thymine. Short sections of DNA are transcribed onto another molecule called RNA (ribonucleic acid), and many are then translated into proteins. These allow an organism's various cells to function properly, and each particular genetic code serves a different purpose, making up the wide variety of organisms and the unique individuals present on Earth. Proteins called enzymes are responsible for singling out the sections of DNA that are transcribed.
Leonard Adleman was a University of Southern California professor of molecular biology and computer science who was considering ways to make computers more efficient. In 1994, he realized that DNA naturally served the purpose he was looking for: storing and efficiently communicating large amounts of information. He used DNA to help solve a common logic problem, known as the Traveling Salesman Problem. It asks for the shortest distance a salesman can take between several different cities connected by a network of roads without entering the same city more than once. With DNA computing, he calculated the optimal route through seven cities in less than a second. Traditional computers could take months to arrive at the same conclusion.
His demonstration showcased the format's advantages: It processed numerous calculations simultaneously, while traditional computers followed a path that must solve one calculation at a time. His discovery generated a great deal of excitement in the scientific community, particularly chemists, biologists, and computer scientists.
However, the limitations of DNA computers became apparent in the following years. They could only perform a massive amount of calculations when each individual problem was relatively simple. While there was great potential for data storage, every possible solution to a problem needed to be represented with DNA strands, which made determining results an extremely cumbersome process. By the end of the decade, scientists regarded DNA computing as more of a novelty than a competitor to traditional computing.
Impact
In the early 2000s, researchers at Israel's Weizmann Institute of Science made great strides in the field of DNA computing. They combined enzymes and DNA to search for precise genetic codes. Dr. Ehud Shapiro helmed a 2004 project that showed the potential to seek out signs of genetic diseases. Shapiro's team produced a computer designed to solve an extremely simple problem: whether or not a DNA sample contained a particular genetic code. This could theoretically be used to identify genes that code for cancers. Shapiro's model successfully identified prostate cancer in a small, controlled test tube environment. The next step would be to do so in a living organism.
This was the beginning of a major shift in the perception of DNA computing. While it was determined to be extremely impractical for performing most functions that traditional computers were used for, this line of research emphasized its advantages. DNA computing could process numerous, very simple tasks with great speed and efficiency compared to traditional computers. It could also interact with and gain information from organic matter in ways that traditional computers never could. This led to increased interest and research in DNA computing due to its potential in the medical field.
In 2006, researchers demonstrated the ability to "fold" DNA, manipulating its structure and suppressing any reactions until a desired time. Harvard and Israel's Bar-Ilan University collaborated on a project to combine this development with DNA computing, which they demonstrated in 2014. They created microscopic nanobots with folded DNA and programmed them similarly to the computers Shapiro's team had designed. The devices would "unfold" if they encountered particular molecules and remain folded if they did not. Upon unfolding, the DNA would be able to react to outside stimuli.
The team programmed the nanobots to react to certain genetic code within cockroaches. The DNA would unfold and begin a reaction, which would administer a drug to the insects. The tests worked, proving that DNA computers could function within organisms. It also showed how cancers could not only be detected but also treated at a molecular level. The great degree of precision in treatment meant that side effects would be greatly reduced. Cancer treatments such as chemotherapy can be harmful because, in addition to the cancerous cells, they affect parts of the individual that were previously undamaged. If there was a reliable method of pinpointing cancer at a molecular level, it would not only leave healthy cells unscathed, but it would also greatly improve the chances of detecting all traces of the disease.
The tests performed as intended in cockroaches, but human immune systems proved too robust for the nanobots. The team continued to conduct tests to improve the technology. They have expressed confidence that with further research and development, DNA computing can become an essential medical tool. In 2019, scientists took another step toward viable DNA computing when researchers at the University of Washington and Microsoft were able to store and retrieve data from manufactured DNA. Scientists were able to encode the word “hello” in fragments of DNA and successfully have the data read back by an automated system.
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