True random numbers are required in fields as diverse as slot machines & data encoding. These numbers got to be truly random, so that they can’t even be predicted by people with detailed knowledge of the tactic or method used to generate them.
As a rule, they’re generated using physical methods. As an example, all thanks to the tiniest high-frequency electron movements, electric resistance of wire isn’t constant but instead fluctuates slightly in an unpredictable way. It means, measurements of this background noise might be used to generate true random numbers.
For the first time, a research team led by Robert Grass, Professor at Institute of Chemical & Bioengineering, has described a non-physical method of generating such numbers: one that uses biochemical signals & truly works in practice. In past, the ideas suggested by other scientists for generating random numbers by chemical means attended be largely theoretical.
DNA Synthesis With Random Building Blocks
For this new approach, ETH researchers apply the synthesis of DNA molecules, a chemical research method frequently used over many years. It’s traditionally used-to produce a precisely defined DNA sequence. In that case, the research-team built DNA molecules with 64 building-block positions in which one among the 4 DNA bases A, C, G & T was randomly located at each position. The scientists achieved this by using a mixture of four building blocks, instead of only one at every step of the synthesis.
As a result, a relatively simple synthesis produced a mixture of approximately 3 quadrillion individual molecules. The subsequently scientists used an efficient method to determine the DNA sequence of 5 million of those molecules. This resulted in 12 megabytes of data which the researchers stored as zeros & ones on a computer.
Huge Quantities Of Randomness In A Small Space
However, an analysis showed that the distribution of the 4 building blocks A, C, G & T wasn’t completely even. Either the intricacies of nature or the synthesis method deployed led to the bases G & T being integrated more frequently in the molecules than A & C. Nonetheless, the scientists were ready to correct this bias with simple algorithm, thereby generating perfect random numbers.
The main aim of ETH Professor Grass & his team was to point-out that random occurrences in reaction are often exploited to get perfect random numbers. Translating finding into a direct application wasn’t a major concern initially. “Compared with other methods, however, ours has the advantage of having the ability to get huge quantities of randomness, which can be stored in a particularly small space, one test tube,” Grass says. “We can read-out the information & reinterpret it in digital form at a later date. This is often impossible with the previous methods.”