DNA- and RNA-Based Computing Systems. Группа авторовЧитать онлайн книгу.
Parker, J. (2003). EMBO Rep. 4: 7–10.
55 55 Mehrotra, A. (2015). Int. J. Latest Technol. Eng. Manage. Appl. Sci. (IJLTEMAS) 4 (12): 62–67.
56 56 Adleman, L.M. (1998). Sci. Am. 279 (2): 54–61.
57 57 Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., and Shmoys, D.B. (eds.) (1995). The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization. Chichester: Wiley‐Interscience.
58 58 Laporte, G. (1992). Eur. J. Oper. Res. 59: 231–247.
59 59 Applegate, D.L., Bixby, R.E., Chvátal, V., and Cook, W.J. (2006). The Traveling Salesman Problem – A Computational Study. Princeton, NJ: Princeton University Press.
60 60 Reif, J.H. (1999). Algorithmica 25: 142–175.
61 61 Kumar, S.N. (2015). Am. J. Nanomater. 3 (1): 1–14.
62 62 Moe‐Behrens, G.H.‐G. (2013). Comput. Struct. Biotechnol. J. 7 (Art. No.: e201304003).
63 63 Bonnet, J., Yin, P., Ortiz, M.E. et al. (2013). Science 340: 599–603.
64 64 Benenson, Y., Gil, B., Ben‐Dor, U. et al. (2004). Nature 429: 423–429.
65 65 Green, A.A., Kim, J., Ma, D. et al. (2017). Nature 548: 117–121.
66 66 Qian, L., Winfree, E., and Bruck, J. (2011). Nature 475: 368–372.
67 67 Macdonald, J., Stefanovic, D., and Stojanovic, M.N. (2008). Sci. Am. 299 (5): 84–91.
68 68 Stojanovic, M.N. and Stefanovic, D. (2003). Nat. Biotechnol. 21: 1069–1074.
69 69 Elstner, M. and Schiller, A. (2015). J. Chem. Inf. Model. 55: 1547–1551.
70 70 Seeman, N.C. (1982). J. Theor. Biol. 99: 237–247.
71 71 Rothemund, P.W.K. (2006). Nature 440: 297–302.
72 72 Jun, H., Zhang, F., Shepherd, T. et al. (2019). Sci. Adv. 5 (Art. No.: eaav0655).
73 73 Hong, F., Zhang, F., Liu, Y., and Yan, H. (2017). Chem. Rev. 117: 12584–12640.
74 74 Wang, D.F., Fu, Y.M., Yan, J. et al. (2014). Anal. Chem. 86: 1932–1936.
75 75 Amir, Y., Ben‐Ishay, E., Levner, D. et al. (2014). Nat. Nanotechnol. 9: 353–357.
76 76 Kogikoski, S. Jr. Paschoalino, W.J., and Kubota, L.T. (2018). TrAC, Trends Anal. Chem. 108 (Art. No.: 88e97).
77 77 Endo, M. and Sugiyama, H. (2018). Molecules 23 (Art. No.: 1766).
78 78 Ramsay, G. (1998). Nat. Biotechnol. 16: 40–44.
79 79 Phillips, A. and Cardelli, L. (2009). J. R. Soc. Interface 6: S419–S436.
80 80 Spaccasassi, C., Lakin, M.R., and Phillips, A. (2019). ACS Synth. Biol. 8: 1530–1547.
81 81 Nielsen, A.A.K., Der, B.S., Shin, J. et al. (2016). Science 352 (6281): 53.
82 82 Panda, D., Molla, K.A., Baig, M.J. et al. (2018). 3 Biotech 8 (Art. No.: 239).
83 83 Baum, E.B. (1995). Science 268: 584–585.
84 84 Zhirnov, V., Zadegan, R.M., Sandhu, G.S. et al. (2016). Nat. Mater. 15: 366–370.
85 85 Ceze, L., Nivala, J., and Strauss, K. (2019). Nat. Rev. Genet. 20: 456–466.
86 86 Zhang, S.F., Huang, B.B., Song, X.M. et al. (2019). 3 Biotech 9 (Art. No.: 342).
87 87 Tomek, K.J., Volkel, K., Simpson, A. et al. (2019). ACS Synth. Biol. 8: 1241–1248.
88 88 Ma, S., Tang, N., and Tian, J. (2012). Curr. Opin. Chem. Biol. 16: 260–267.
89 89 Behlke, M.A., Berghof‐Jäger, K., Brown, T. et al. (2019). Polymerase Chain Reaction: Theory and Technology. Poole: Caister Academic Press.
90 90 Garibyan, L. and Avashia, N. (2013). J. Invest. Dermatol. 133 (3), art No. e6.
91 91 Alves Valones, M.A., Lima Guimarães, R., Cavalcanti Brandão, L.A. et al. (2009). Braz. J. Microbiol. 40 (1): 1–11.
92 92 Powledge, T.M. (2004). Adv. Physiol. Educ. 28: 44–50.
93 93 Shendure, J., Balasubramanian, S., Church, G.M. et al. (2017). Nature 550: 345–353.
94 94 Dolgin, E. (2009). Nature 462: 843–845.
95 95 Agah, S., Zheng, M., Pasquali, M., and Kolomeisky, A.B. (2016). J. Phys. D: Appl. Phys. 49 (Art. No.: 413001).
96 96 Stefano, G.B., Wang, F.Z., and Kream, R.M. (2018). Med. Sci. Monit. 24: 1185–1187.
97 97 Takahashi, C.N., Nguyen, B.H., Strauss, K., and Ceze, L. (2019). Sci. Rep. 9 (Art. No.: 4998).
98 98 Goldman, N., Bertone, P., Chen, S. et al. (2013). Nature 494: 77–80.
99 99 Anavy, L., Vaknin, I., Atar, O. et al. (2019). Nat. Biotechnol. 37: 1229–1236.
100 100 Organick, L., Ang, S.D., Chen, Y.‐J. et al. (2018). Nat. Biotechnol. 36: 242–248.
101 101 Church, G.M., Gao, Y., and Kosuri, S. (2012). Science 337: 1628.
102 102 Shipman, S.L., Nivala, J., Macklis, J.D., and Church, G.M. (2017). Nature 547: 345–349.
103 103 Li, S., Jiang, Q., Liu, S. et al. (2018). Nat. Biotechnol. 36: 258–264.
104 104 Ma, W., Zhan, Y., Zhang, Y. et al. (2019). Nano Lett. 19: 4505–4517.
105 105 De Silva, P.Y. and Ganegoda, G.U. (2016). BioMed Res. Int. 2016 (Art. No.: 8072463).
106 106 Tagore, S., Bhattacharya, S., Islam, M.A., and Islam, M.L. (2010). J. Proteomics Bioinform. 3: 234–243.
107 107 Currin, A., Korovin, K., Ababi, M. et al. (2017). J. R. Soc. Interface 14 (Art. No.: 20160990).
2 DNA Computing: Methodologies and Challenges
Deepak Sharma and Manojkumar Ramteke
Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, Hauz Khas, New Delhi, 110016, India
2.1 Introduction to DNA Computing Methodologies
Humans are looking for new approaches to computing since the starting of civilization. Over the years, researchers have invented many systems for computation, from “counting with abacus” to “complex computing by using modern‐day computers.” According to Moore's observation [1], the number of transistors on a silicon chip is found to be doubling in every 18–24 months, which results in the development of faster computing devices. However, in the coming decades, producing such faster computing devices will be more challenging as the size of the transistor is already approaching to a molecular level. Moreover, engineering such silicon chips is gradually becoming more complex and less cost effective. This compelled the researchers to look for alternative computing devices and methodologies. Biomolecular computing is one such excellent alternative to traditional silicon‐based computing methods.
Biomolecular computing is illustrated for the first time by Adleman in 1994 [2] to solve the Hamiltonian path problem (HPP) using deoxyribonucleic acid (DNA). In such DNA‐based computing (referred to as DNA computing), enzymatic reactions and manipulations such as addition, amplification, and cutting of the DNA are used for performing the computing. Subsequently, DNA computing is used by several researchers [3–8] to solve a variety of combinatorial problems. These studies have exploited high parallelism of DNA reactions over sequential operations occurring in silicon‐based computers to solve the computationally intractable problems. Such parallel processing in DNA computer builds the confidence for solving the problems that are presently not solvable with silicon‐based computers. Moreover, DNA became an effective and efficient material for faster computation, storage, and information processing owing to the significant advancements in biomolecular techniques such as gel electrophoresis, polymerase chain reaction (PCR), affinity separation, restriction enzyme digestion, etc. [9,10].
Despite