e.g., for solving complex combinatorial problems [28], potentially promising to have an advantage over silicon‐based electronic computers due to parallel computing performed by numerous biomolecular units, the present level of technology does not allow any practical use of biomolecular systems for real computational applications. For achieving any practical result soon, some other applications, different from making a biocomputer, should be considered using the (bio)molecular systems with a limited complexity. One of the immediate possible applications for molecular logic systems is a special kind of biosensing [29–31] where the multiple input signals are logically processed through chemical reactions resulting in YES/NO decisions in the binary (0,1) format. In this subarea of biomolecular logic systems, practical results are already possible at the present level of the system complexity, particularly for biomedical applications [32–35]. Overall, the research in molecular/biomolecular information processing, which has been motivated originally to progress unconventional computing applications, is broadly developing to areas not directly related to computing in its narrow definition. This research is bringing us to novel areas in sensing/biosensing [29–31], switchable “smart” materials controlled by logically processed signals [32–36], bioelectronic devices (e.g., biofuel cells) controlled by external signals [37,38], signal‐controlled release processes [39–43], etc.
Figure 1.1 Moore's law – exponential increase of transistors on integrated circuit chips. (The plot shown in the figure is based on the data provided by Wikipedia: https://en.wikipedia.org/wiki/Moore%27s_law.)
Source: From Katz [2]. Reprinted with the permission of John Wiley and Sons.
Figure 1.2 Biomolecular computing systems mimicking operation of different Boolean logic gates and circuitries can be based on various species including oligopeptides, enzymes/proteins, DNA/RNA, antibodies, and even whole biological (e.g., microbial) cells.
Source: From Katz 2019 [2], Boolean Logic Gates Realized with Enzyme‐Catalyzed Reactions – Unusual Look at Usual Chemical Reactions. ChemPhysChem © 2018. Reproduced with the permission of John Wiley & Sons.
1.2 DNA‐ and RNA‐Based Biocomputing Systems in Progress
While the general topics of the biomolecular computing [12] and specifically the enzyme‐based computing [44] have been covered with recently published books, the present book is concentrated on the use of DNA and RNA molecules in computing systems, broadly defined as information processing systems. From the time (1953) when James D. Watson and Francis H.C. Crick (Figure 1.3) discovered chemical structure of DNA (Figure 1.4) [45], the progress in the DNA study resulted in many novel fundamental scientific concepts [46–48] and highly important practical applications [49]. Among many other, mostly biomedical applications, DNA molecules have been extensively studied over last two decades for unconventional biomolecular computing [13,15,50–55], following the pioneer work (1994) by Leonard M. Adleman [28,56] (Figure 1.5). In his seminal work Adleman demonstrated for the first time computational use of DNA molecules for solving a “traveling salesman problem,” Hamiltonian path problem. Actually, this work initiated (bio)molecular computing research not necessary using DNA molecules.
Figure 1.3 The discoverers of the structure of DNA. James Watson (b.1928) at left and Francis Crick (1916–2004), with their model of part of a DNA molecule in 1953. Photographed in the Cavendish Laboratory, University of Cambridge, UK, in May 1953.
Source: From Watson and Crick [45]. https://cnx.org/contents/8M7b3dzJ@2/DNA-Structure. Licensed Under CC BY 4.0.
Figure 1.4 The structure of the DNA double helix. The atoms in the structure are color‐coded by element, and the detailed structures of two base pairs are shown in the bottom right.
Source: From Watson and Crick [45]. Also adapted from Zephyris, DNA Structure, Wikimedia commons, 2011. Public Domain.
Figure 1.5 Leonard Adleman – a pioneer of the biomolecular computing; the photo of 1993 when the first experiments on DNA computing were running.
Source: Courtesy of Prof. Leonard Adleman.
The “traveling salesman problem” asks the following question [57–59]: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?” It is a problem in combinatorial optimization, important in theoretical computer science. It is frequently used to test computational algorithms and computer hardware. In general, the traveling salesman problem is hard to solve, particularly when the number of the visited cities is increasing. Adleman solved the problem for seven cities only (Figure 1.6), which was rather a trivial task, but importantly it was solved using computational power of DNA reactions [28,56]. The DNA molecules hybridized in a special way to solve the problem, and the computation was performed by numerous DNA sequences (actually rather short oligonucleotides) operating in parallel. This was important conceptual difference from Si‐based electronic computers that perform all operations in a sequence. The computation in Adleman's experiment was performed at the speed of 1014 operations per second, a rate of 100 teraflops or 100 trillion floating point operations per second (comparable to the fastest presently available quantum computer) – all because of massively parallel processing capabilities of the DNA computing operation [54,60]. The promise for extremely fast computation ignited the interest to the DNA computing concept, then being extended to a broader area of molecular [8] and biomolecular [12] computing. Despite the fact that the practical results have not been obtained after almost 25 years of the active research, optimistic expectations for building DNA computers are still present [27,55,61,62]. The advantages of the DNA and RNA computing systems are not only in their potentially high speed of operation due to the parallel information processing but also in their ability to operate in a biological environment
