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Thermodynamics-Based Weight Encoding Methods for Improving Reliability of Biomolecular Perceptrons  

Lim, Hee-Woong (서울대학교 전기컴퓨터공학부)
Yoo, Suk-I. (서울대학교 전기컴퓨터공학부)
Zhang, Byoung-Tak (서울대학교 전기컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.34, no.12, 2007 , pp. 1056-1064 More about this Journal
Abstract
Biomolecular computing is a new computing paradigm that uses biomolecules such as DNA for information representation and processing. The huge number of molecules in a small volume and the innate massive parallelism inspired a novel computation method, and various computation models and molecular algorithms were developed for problem solving. In the meantime, the use of biomolecules for information processing supports the possibility of DNA computing as an application for biological problems. It has the potential as an analysis tool for biochemical information such as gene expression patterns. In this context, a DNA computing-based model of a biomolecular perceptron has been proposed and the result of its experimental implementation was presented previously. The weight encoding and weighted sum operation, which are the main components of a biomolecular perceptron, are based on the competitive hybridization reactions between the input molecules and weight-encoding probe molecules. However, thermodynamic symmetry in the competitive hybridizations is assumed, so there can be some error in the weight representation depending on the probe species in use. Here we suggest a generalized model of hybridization reactions considering the asymmetric thermodynamics in competitive hybridizations and present a weight encoding method for the reliable implementation of a biomolecular perceptron based on this model. We compare the accuracy of our weight encoding method with that of the previous one via computer simulations and present the condition of probe composition to satisfy the error limit.
Keywords
DNA computing; biomolecular perceptron; weight encoding; hybridization reaction model;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 김은경, 이상용, '해밀톤 경로 문제를 위한 DNA 컴퓨팅에서 코드 최적화', 정보과학회논문지, 제31권, 제4호, pp. 387-393, 2004
2 Stojanovic, M. N. and Stefanovic, D., 'A deoxyribozyme-based molecular automaton,' Nature Biotechnology, Vol.21, pp. 1069-1074, 2003   DOI   ScienceOn
3 Mills Jr., A. P., 'Gene expression profiling diagnosis through DNA molecular computation,' Trends in Biotechnology, Vol.20, No.4, pp. 137- 140, 2002   DOI   ScienceOn
4 Benenson, Y., Gil, B., Ben-Dor, U., Adar, R., and Shapiro, E., 'An autonomous molecular computer for logical control of gene expression,' Nature, Vol.429, No.6990, pp. 423-429, 2004   DOI   ScienceOn
5 Rosenblatt, F., 'The perceptron: a probabilistic model for information storage and organization in the brain,' Psychological Review, Vol.65, No.6, pp. 386-408, 1958   DOI
6 McCulloch, W. S. and Pitts, W., 'A logical calculus of the ideas immanent in nervous activity,' Bulletin of Mathematical. Biology, Vol.5, pp. 115-133, 1943
7 Tyagi, S. and Kramer, F. R., 'Molecular beacons: probes that fluorescence upon hybridization,' Nature Biotechnology, Vol.14, No.3, pp. 303-308, 1996   DOI   ScienceOn
8 Mills Jr., A. P., Yurke, B., and Platzman, P. M., 'Article for analog vector algebra computation,' BioSystems, Vol.52, No.1-3, pp. 175-180, 1999   DOI   ScienceOn
9 Shin, S.-Y., Lee, I.-H., Kim, D., and Zhang, B.-T., 'Multi-objective evolutionary optimzation of DNA sequences for reliable DNA computing,' IEEE Transactions on Evolutionary Computation, Vol.9, pp. 143-158, 2005   DOI   ScienceOn
10 Braich, R. S., Chelyapov, N., Johnson, C., Rothemund, P. W. K., and Adleman, L., 'Solution of a 20-variable 3-SAT problem on a DNA computer,' Science, Vol.296, No.5567, pp. 499-502, 2002   DOI
11 Lim, H.-W., Lee, S. H., Yang, K. A., Lee, J. Y., Yoo, S.-I., Park, T. H., and Zhang, B.-T., 'In vitro molecular pattern classification using DNA- based weighted sum computation,' IEEE Transactions on Nanobioscience, (submitted for publication)
12 Mills, Jr., A. P., Turberfield, M., Turberfield, A. J., Yurke, B., and Platzman, P. M., 'Experimental aspects of DNA neural network computation,' Soft Computing, Vol.5, pp. 10-18, 2001   DOI   ScienceOn
13 신수용, 이인희, 장병탁, '${\varepsilon}-다중목적함수$ 진화 알고리즘을 이용한 DNA 서열 디자인', 정보과학회논문지, 제32권, 제12호, pp. 1217-1228, 2005   과학기술학회마을
14 Sakakibara, Y. and Suyama, A., 'Intelligent DNA chips: logical operation of gene expression profiles on DNA computers,' Genome Informatics, Vol.11, pp. 33-42, 2000
15 Paun, G., Rozenberg, G., and Salomaa, A., DNA Computing: New Computing Paradigms, Springer-Verlag, Berlin Heidelberg, 1998
16 장병탁, '나노바이오지능 분자컴퓨터: 컴퓨터공학과 바이오공학, 나노기술, 인지뇌과학의 만남', 정보과학회지, 제23권, 제5호, pp. 41-56, 2005
17 Rose, J. A., Deaton, R. J., and Suyama, A., 'Statistical thermodynamic analysis and design of DNA-based computers,' Natural Computing, Vol.3, No.4, pp. 443-459, 2004   DOI   ScienceOn
18 Lipton, R. J., 'DNA Solution of hard computational problems,' Science, Vol.268, No.5210, pp. 542-545, 1995   DOI
19 Benenson, Y., Paz-Elizur, T., Adar, R., Keinan, E., Livneh, Z., and Shapiro, E., 'Programmable and autonomous computing machine made of biomolecules,' Nature, Vol.414, pp. 430-434, 2001   DOI   ScienceOn
20 Oliver, J. S., 'Matrix multiplication with DNA,' Journal of Molecular Evolution, Vol.45, No.2, pp. 161-167, 1997   DOI   ScienceOn
21 Adleman, L. M., 'Molecular computation of solution to combinatorial problems,' Science, Vol.266, No.5187, pp. 1021-1024, 1994   DOI
22 이상구, 이광형, 임기형, 'DNA 컴퓨팅 기술의 개요 및 응용', 정보과학회지, 제18권, 제8호, pp. 73-77, 2000
23 Lipshutz, R. J., Fodor, S. P., Gingeras, T. R., and Lockhart, D. J., 'High density synthetic oligonucleotide arrays,' Nature Genetics, Vol.21, pp. 20-24, 1999   DOI
24 Wang, Y., Wang, X., and Goush, S.-W., 'Conditions to ensure competitive hybridization in two- color microarray: a theoretical and experimental analysis,' BioTechniques, Vol.32, No.6, pp. 1342- 1346, 2002