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Microsynth primer12/30/2023 ![]() Our contribution is twofold: We offer a transformative application of chemical synthesis as well as explore the robustness and the statistical properties of DNA synthesis. In this work, we combine the technologies readily available for synthesizing and sequencing DNA to generate random numbers, analyze the results of DNA synthesis, and evaluate the produced randomness. However, the physical realization of the theory and the experimental limitations were not investigated 21, 22. Previous work has presented the idea of simulation of the DNA random number generation circuitry by theoretically proposing a scheme for a possible automated workflow for DNA random number generation. ![]() Nowadays, next-generation sequencing methods offer remarkable throughput 18, 19, 20 and enable us to read individual molecules and thus use DNA as a source of random number generation. Sequencing technologies to identify individual nucleotides in strands of DNA have been around since the late 1970s 17. The synthetic production of DNA is a stochastic chemical process with the advantage that the individual molecules in the synthesized DNA sequence can easily be identified and analyzed by next generation sequencing (NGS) technologies. This, however, is different for the synthesis of DNA. Although this is a promising approach, not being able to identify individual molecules results in the loss of randomness when analyzing stochastic chemical processes, which is why chemical reactions cannot typically be used as RNGs 15. have suggested an automated system exploiting the large available pool of entropy of detectable macrostates of growing crystals in chemical reactions, generating random bits 15. Although the expectation of products can be statistically predicted, being able to identify individual molecules after synthesis is rarely possible 15. Chemical reactions are statistical processes where the formation of chemical products follows a certain probability distribution depending on the activation energy for a reaction 16. 14, addressing integrated low-cost, mechanically flexible devices by using semiconducting single-walled carbon nanotubes to digitize thermal noise in order to generate random bits.Īs opposed to existing RNGs that are based on physical phenomena or software algorithms, chemical reactions can also be employed as an entropy source for generating random numbers 15. A more recent example of a true RNG has been shown by Gaviria Rojas et al. However, pseudo-RNGs can have better statistical properties and can oftentimes produce random numbers faster than true RNGs, and are thus still popular today. If the input seed is known, the entire random number sequence can be reproduced. A true RNG uses a non-deterministic (chaotic) source for random number generation 12, 13, whereas a pseudo-RNG creates a deterministic sequence of numbers that depends on an input (seed) 11, 12. It is important to note the distinction between true RNGs and pseudo-RNGs. Such hardware RNGs create bit streams depending on highly unpredictable physical processes, making them useful for secure data transmission as they are less prone to cryptanalytic attacks 8, 9, 10, 11. Of today’s state-of-the-art RNGs, the Intel RNG provides 500 MB/s of throughput. New methods for random number generation were developed, such as the Silicon Valley-developed lava lamp and the Mersenne Twister (a software RNG) 6, 7. Shifting from algorithm to interactions, the modern world required network security services, and thus introduced encryption and decryption schemes for exchanging information securely, requiring high-quality random numbers (generated faster while being less prone to attacks) 4, 5. Thus began a series of technological breakthroughs including the first integration of a hardware random number generator (RNG) into a real computer, the Manchester Mark I, by using electrical noise 3. The increasing necessity of being able to generate large quantities of random numbers for societal needs is made obvious when viewing the technological developments thereafter: About half a century later, solving problems with probabilistic procedures demanded a volume of random numbers much greater than that a dice could produce efficiently 2. These words of Francis Galton published in Nature in 1890, vividly demonstrate one of the simplest methods for generating random numbers. When they are shaken, they tumble wildly about, and their positions at the outset afford no perceptible clue to what they will be after even a single good shake and toss” 1. “As an instrument for selecting at random, I have found nothing superior to dice.
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