DNA- and RNA-Based Computing Systems. Группа авторов
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Figure 1.11 PCR method for copying DNA molecules: a thermal cycler, components of the reaction mixture, and reaction steps. For detailed description of the method and instrument see Refs. [89–92].
Source: From Keagile Bati, Polymerase Chain Reaction: Innovation that Revolutionized Molecular Biology, Nov 2018. Public Domain.
Figure 1.12 Apparatus operating as the end‐to‐end automatic DNA data storage allowing automatic “writing”–“reading” DNA processes.
Source: From Takahashi et al. [97]. https://www.nature.com/articles/s41598-019-41228-8. Licensed Under CC BY 4.0.
1.4 Short Conclusions and Comments on the Book
Overall, the DNA computing is a multidisciplinary research area with major contributions from synthetic biology, nanotechnology, computer science, chemical engineering, biosensing and biotechnology, biology and medicine, etc. Some of the research areas are already reaching the mature states, while others are still in the infancy. It is still not easy to predict in what direction the research will go and what applications will be more benefiting from the DNA computing. In the most probability, practical applications will be in two major subareas: medicine with the DNA information processing nanorobotic systems operating in vivo [103,104] and large data storage systems providing extremely high density of the information storage [84,105]. Many other applications of the DNA computing are in the research and discussion [106,107]. However, it is quite unexpected that the DNA computing will come to the end users instead of standard electronic computers, at least in the short perspective.
The present book, composed of the chapters written by the best experts in the field, covers all subtopics of the DNA computing, including the design of Boolean logic gates and circuitries, programming the DNA information processing systems, their biomedical applications and operation in vivo, DNA data storage and nanopore DNA decoding, and interfacing of the DNA computing with enzyme logic systems, and many more detailed explanations on the DNA and RNA computing with many references and illustrations.
References
1 1 Moore, G.E. (1998). Proc. IEEE 86: 82–85.
2 2 Katz, E. (2019). ChemPhysChem 20: 9–22.
3 3 Calude, C.S., Costa, J.F., Dershowitz, N. et al. (eds.) (2009). Unconventional Computation, Lecture Notes in Computer Science, vol. 5715. Berlin: Springer.
4 4 Adamatzky, A. (ed.) (2017). Advances in Unconventional Computing, Emergence, Complexity and Computation, 2 volumes. Switzerland: Springer.
5 5 Mermin, N.D. (2007). Quantum Computer Science: An Introduction. Cambridge: Cambridge University Press.
6 6 Szacilowski, K. (2012). Infochemistry – Information Processing at the Nanoscale. Chichester: Wiley.
7 7 de Silva, A.P. (2013). Molecular Logic‐Based Computation. Cambridge: Royal Society of Chemistry.
8 8 Katz, E. (ed.) (2012). Molecular and Supramolecular Information Processing – From Molecular Switches to Logic Systems. Weinheim: Willey‐VCH.
9 9 Sienko, T. (ed.) (2003). Molecular Computing (Series Eds.: Adamatzky, A., Conrad, M., and Rambidi, N.G.). Cambridge, MA: MIT Press.
10 10 Spitzer, N.C. and Sejnowski, T.J. (1997). Science 277: 1060–1061.
11 11 Kampfner, R.R. (1989). BioSystems 22: 223–230.
12 12 Katz, E. (ed.) (2012). Biomolecular Computing – From Logic Systems to Smart Sensors and Actuators. Weinheim: Wiley‐VCH.
13 13 Stojanovic, M.N., Stefanovic, D., and Rudchenko, S. (2014). Acc. Chem. Res. 47: 1845–1852.
14 14 Stojanovic, M.N. and Stefanovic, D. (2011). J. Comput. Theor. Nanosci. 8: 434–440.
15 15 Ezziane, Z. (2006). Nanotechnology 17: R27–R39.
16 16 Xie, Z., Wroblewska, L., Prochazka, L. et al. (2011). Science 333: 1307–1311.
17 17 Ashkenasy, G., Dadon, Z., Alesebi, S. et al. (2011). Isr. J. Chem. 51: 106–117.
18 18 Unger, R. and Moult, J. (2006). Proteins 63: 53–64.
19 19 Katz, E. and Privman, V. (2010). Chem. Soc. Rev. 39: 1835–1857.
20 20 Katz, E. (2015). Curr. Opin. Biotechnol. 34: 202–208.
21 21 Halámek, J., Tam, T.K., Chinnapareddy, S. et al. (2010). J. Phys. Chem. Lett. 1: 973–977.
22 22 Rinaudo, K., Bleris, L., Maddamsetti, R. et al. (2007). Nat. Biotechnol. 25: 795–801.
23 23 Arugula, M.A., Shroff, N., Katz, E., and He, Z. (2012). Chem. Commun. 48: 10174–10176.
24 24 Adamatzky, A. (2010). Physarum Machines – Computers from Slime Mould. London: World Scientific.
25 25 Kahan, M., Gil, B., Adar, R., and Shapiro, E. (2008). Physica D 237: 1165–1172.
26 26 Alon, U. (2006). An Introduction to Systems Biology: Design Principles of Biological Circuits. Boca Raton, FL: Chapman & Hall/CRC.
27 27 Benenson, Y. (2009). Mol. Biosyst. 5: 675–685.
28 28 Adleman, L.M. (1994). Science 266: 1021–1024.
29 29 Katz, E., Wang, J., Privman, M., and Halámek, J. (2012). Anal. Chem. 84: 5463–5469.
30 30 Wang, J. and Katz, E. (2011). Isr. J. Chem. 51: 141–150.
31 31 Wang, J. and Katz, E. (2010). Anal. Bioanal. Chem. 398: 1591–1603.
32 32 Halámková, L., Halámek, J., Bocharova, V.