Читать книгу DNA- and RNA-Based Computing Systems - Группа авторов - Страница 27

Though the use of DNA for computing can have the benefit of performing millions of operations simultaneously with very high energy efficiency, it also has several challenges. The amount of DNA required is substantial even for a simple formulation. Therefore, solving the large size problems becomes impractical owing to the requirement of a large amount of DNA. Unlike the traditional silicon‐based computers in which memory reallocation is performed readily, reuse of DNA material is challenging in DNA computing as specific designs of DNA are required.

The success of DNA computing procedures is based on error‐free operations of biochemical steps involved. However, the practical operations do involve experimental errors that increase with the increase in the number of steps. Further, the operations involve human interventions during the process. Therefore, solving the bigger size formulations also involves the higher probability of missing the correct answers. Further, increase in formulation size requires extracting the correct solution from the pool involving large number of incorrect solutions. Therefore, the extraction efficiency decreases with increase in formulation size. Also, the large amount of DNA representing the incorrect solutions is discarded as waste. Complete automation of all the biochemical steps is required for building a reliable DNA computer.

Another challenge for DNA computing is its application to real‐world problems. Since the data of every problem has to be represented in the form of DNA, the design of DNA is specific to each problem and cannot be used for other problems. Further, for error‐free biochemical operations, the DNA designs should have specific GC content with unique (noncomplementary) nucleotide sequence and should lead to a specific structure (i.e. hairpin or linear formation). These requirements reduce the designing flexibility and therefore restrict the application to bigger size formulations. Moreover, real‐world problems often involve continuous search spaces with multiple optimal solutions. For such problems, the existing DNA computing procedures that are originally developed for solving the combinatorial problems involving the discrete search space need to be modified.

In conclusion, DNA computing shows great potential and has many advantages in the field of computing and data storage over conventional computing, primarily due to its ability to perform millions of calculations simultaneously using molecules. Despite this, the DNA computer is far from matching the reliability of conventional silicon‐based computer owing to several challenges such as poor scaling and limited ability to handle real‐world problems. The comparative analysis of existing DNA computing and data storage models illustrated their pros and cons, which is opening up new directions in materials science and biomedical applications.