A DNA-based memory with in vitro learning and associative recall

被引:13
作者
Chen J. [1 ]
Deaton R. [2 ]
Wang Y.-Z. [1 ]
机构
[1] Department of Chemistry and Biochemistry, The University of Delaware, Newark
[2] Department of Computer Science and Engineering, The University of Arkansas, Fayetteville
基金
美国国家科学基金会;
关键词
Associative memory; Biotechnology; DNA computing; Learning;
D O I
10.1007/s11047-004-4002-3
中图分类号
学科分类号
摘要
A DNA-based memory was implemented with in vitro learning and associative recall.The learning protocol stored the sequences to which it was exposed, and memories were recalled by sequence content through DNA-to-DNA template annealing reactions. Experiments demonstrated that biological DNA could be learned, that sequences similar to the training DNA were recalled correctly, and that unlike sequences were differentiated. Theoretically, the memory has a pattern separation capability that is very large, and can learn long DNA sequences. The learning and recall protocols are massively parallel, as well as simple, inexpensive, and quick. The memory has several potential applications in detection and classification of biological sequences, as well as a massive storage capacity for non-biological data. © Springer 2005.
引用
收藏
页码:83 / 101
页数:18
相关论文
共 26 条
  • [1] Adleman L.M., Molecular computation of solutions to combinatorial problems, Science, 266, pp. 1021-1024, (1994)
  • [2] Baum E., Building an associative memory vastly larger than the brain, Science, 268, pp. 583-585, (1995)
  • [3] Braich R.S., Chelyapov N., Johnson C., Rothemund P.W.K., Adleman L., Solution of a 20-variable 3-sat problem on a DNA computer, Science, 296, pp. 499-502, (2002)
  • [4] Brennenman A., Condon A.E., Strand design for bio-molecular computation, (2001)
  • [5] Brenner S., Williams S.R., Vermaas E.H., Storck T., Moon K., McCollum C., Mao J., Luo S., Kirchner J.J., Eletr S., DuBridge R.B., Burcham T., Albrecht G., Vitro cloning of complex mixtures of DNA on microbeads: Physical separation of differentially expressed cDNAs, Proceedings National Academy Science, 97, pp. 1665-1670, (2000)
  • [6] Cover T.M., Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition, IEEE Transactions on Electronic Computers, 14, pp. 326-334, (1965)
  • [7] Deaton R., Chen J., Bi H., Rose J.A., A software tool for generating non-crosshybridizing libraries of DNA oligonucleotides, DNA Computing: 8th International Workshop on DNA-Based Computers, pp. 252-261, (2003)
  • [8] Deaton R., Murphy R.C., Garzon M., Franceschetti D.R., Stevens Jr. S.E., Good encodings for DNA-based solutions to combinatorial problems, Proceedings of the Second Annual Meeting on DNA Based Computers, 44, pp. 247-259, (1996)
  • [9] Hagiya M., Arita M., Kiga D., Sakamoto K., Yokoyama S., Towards parallel evaluation and learning of Boolean μ-formulas with molecules, pp. 57-72, (1997)
  • [10] Hartmanis J., On the weight of computations, Bulletin of the European Association for Theoretical Computer Science, 55, pp. 136-138, (1995)