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rel="nofollow" href="#fb3_img_img_a3c80e84-89d4-56bd-945a-64dbd7dbb750.png" alt="images"/> will lead to the maximum “1”s in the binary string and will have the shortest length in base pairs. Such strands are analyzed by cloning and sequencing to obtain the maximal clique. For the given illustrative example (Figure 1.12b), {cc11c} and {c11cc} sequences will be removed. Therefore, the sequences that will lead to the maximal clique will be {11011}. From these, the maximal size of the clique for the given illustrative example is four, which corresponds to the clique V1–V2–V4–V5.
2.2.6 Chao's Model
Chao et al. [13] developed a single‐molecule “DNA navigator” to solve a maze (tree graph) of 10 vertices with three junctions. In this, the desired path is explored out of all possible paths of the maze present on an origami that is used as a substrate. On this origami, some sites are specifically used for the binding of the vertices of the tree graph. This helps in the propagation of the path on the origami.
The DNA used for the process is very specific in size and design. The designing of DNA is described next. Hairpin DNA Y is attached to the origami and has a typical sequence layout of the structure
Figure 2.7 Chao's single‐molecule DNA navigator [13] for solving the maze.
After the binding of the initiator, a hairpin loop of the DNA Y opens to make it free to bind to DNA Z and vice versa as both have complementary sites for free form of each other. This type of hybridization continues until it reaches the exit DNA. This hybridization chain also produces those paths that are not the solution to the maze. The exit DNA corresponding to an end vertex of the maze is biotin labeled. If the path is correct, then this biotin‐labeled DNA is free from the exit vertex; otherwise, biotin remains attached to the DNA corresponding to the exit vertex. All the biotin‐labeled sequences are then removed from the solution by streptavidin magnetic beads. The correct path sequence remained in the solution as it is not attached to the biotin. This solution is then analyzed by AFM for identifying the final path.
2.2.7 DNA Origami
Self‐assembly of DNA is one of the most popular techniques used to create a variety of two‐dimensional and three‐dimensional structures. The DNA origami is an excellent example of self‐assembly of DNA, which was introduced by Rothemund [14]. The procedure of DNA origami is shown in Figure 2.8. DNA origami is generated from a thousand base pair long ssDNA, known as a scaffold. Some shorter and linear ssDNA called staples are mixed with the scaffold to create DNA origami. For this purpose, the solution mixture is first heated to 95 °C. The solution is then cooled down to room temperature from 95 °C; throughout the cooling, the staples bind to the scaffold through Watson–Crick complementary base pairing. The scaffold then becomes a static structure or pattern of DNA. The sequence of DNA staples results in the formation of various shapes, for example, squares, smiley faces, cube, hexagon, star, and many more.
Figure 2.8 DNA origami is a group of linked dsDNA. The structure consists of one scaffold and many staple strands that are complementary to one or two domains on the scaffold.
Based on the structure or pattern of the DNA origami, it has multiple applications in different fields of biology, chemistry, physics, computer science, and materials science. Broad application of DNA origami can be seen as in molecular robotics [15,16], DNA walkers [17], protein function [18,19] and structure determination [20,21], single‐molecule force spectroscopy [22,23], nanopore construction [24–26], drug delivery [27,28], nucleic acid analysis [29–31], enzymatic nanostructure formations [32,33], and many more.
2.2.8 DNA‐Based Data Storage
DNA carries all the genetic information such as the color of eyes and hairs in the form of A, T, G, and C, which is transferred from one generation to another. Inspired by this, the researchers are now looking at the capabilities of the DNA to store the data. Today we are dealing with a large amount of data that is increasing day by day. DNA appears to be the right choice as an alternative for storing the information. In DNA‐based data storage, first, the digital information present in the form of bits is encoded in DNA. This encoded information is then processed and decoded back to original binary data.
Church et al. [34] performed an experiment for storing 5.27 MB data generated from his book (contains 53 426 words, 11 images, and 1 JavaScript program) on the DNA. A simple encoding of one bit to one base is used to represent the data on the DNA. The result of this experiment clearly showed that DNA is a good material for storing digital information in addition to other storage media. Goldman et al. [35] stored over five million bits of digital information of the DNA that is later retrieved and reproduced the information with an accuracy between 99.99% and 100%.
Researchers are now looking for high data storage with high accuracy in recovery. Blawat et al. [36] stored and recovered 22 MB of a MPEG compressed movie from DNA with zero errors. Erlich and Zielinski [37] reported another encoding method that can store the 215 petabytes digital data in one gram of DNA. Recently, Organick et al. [38] demonstrated approximately 220 MB digital data storage with random access on the DNA with successful retrieval from it. In 2019, researchers from Microsoft and the University of Washington have demonstrated a fully automated system to encode and decode data in DNA [39].
2.3 Challenges
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.