Abstract

Ongoing development in nanotechnology and bioinformatics will enable the construction of nanorobot which will work at nano-scale. Nanorobot development has many challenges and limitations such as its control and behavior in different environments. In this proposed work we present DNA nanorobot design, methodology for identification of metastatic tumour cells and DNA nanorobot control techniques for its movement in dynamic environment are described using Fuzzy Logic (FL) rules. Proposed model will identify the tumour cells in vivo.

References

• Cavalcanti, A., Hogg. T., Shirinzadeh. Bijan., Hwee C. Liaw, Nanorobot Communication techniques: A Comprehensive Tutorial, IEEE International Conference on Control, Automation, Robotics and vision, 2006.
• Hamdi. M., and Ferreira. V., DNA Nanorobotics, Laboratoire Vision et Robotique, ENSI Bourges – University d Orleans, 18000, Bourges, France, 14-15 December 2006.
• Ito. Yoshihiro., and Fukusaki. E., DNA as Nanomaterial, Science Direct, Journal of Molecular Catalysis B: Enzymatics, pp. 155- 166, 2004.
• Haymar. Khin., and Park. J.S., Obstacle Avoidance Algorithm for Collective Movement in Nanorobots, Department of Computer Engineering, Korea Aerospace University, Korea, IJCSNS International Journal of Computer Science and Network security, Vol.8 No.11, November 2008.
• Beem. H.,, Nanorobotics Design, Assembly, and Role in Medicine, Oklahoma State University, Nanotechnology: From Synthesis to Self-Assembly, Fall, 2006.
• Huai-wei. J., WANG shi-gang’ XU Wei, ZHANG Zhi-zhou’ HE Lin, Construction of Medical Nanorobot, IEEE International Conference on Robotics and Biomimetics, 2005.
• Cavalcanti. A., Freitas. R.A., Collective Robotics Coherent Behaviour for Nanosystems with Sensor-Based Motion,”IEEE International Conference on Artificial Intelligence system, 2002.
• Requicha. Aristides A. G., Nanorobots, NEMS, and Nanoassembly, Proceedings of the IEEE, VOL.91, No. 11, November 20003.
• Carvalcanti. Adriano., Rosen. Lior., Kretly. Luiz C., Nanorobotic challenges in Biomedical applications, Design and Control, IEEE ICECS Int’l conf. on Electronics, Circuits and systems, Tel-Aviv, Israel, December 2004.
• Robert A Freitas, Jr., “Current Status of Nanomedicine and Medical Nanorobotics,” ASP American scientific Publishers, Journal of Computational and Theoretical Nanoscience, Vol. 2, pp. 1-25, 2005.
• Ummat, A. Dubey, and Marvroidis. C., Bio- Nanorobotics-A Field Inspired by Nature, Bar Cohen, Biommetics: Biologically Inspired Technologies DK3163 c007, pp. 2001-226, 22.6.2005.
• Cavalcanti. Adriano., Shirinzadeh. Bijan., Nanorobots for Laparoscopic cancer Surgery, IEEE ICIS International conference on Computer and Information Science, 2007.
• Myrna I. Merced Serrano, “Study of Stable Conformations CNT-DNA Hybrids by Means of Principle Component Analysis”, Proceedings of The National Conference on Undergraduate Research (NCUR) Salisbury University, Salisbury, Maryland, April 10-12, 2008.
• Cavalcanti. Adriano., shirinzaeh. Bijan., Mingjun Zhang, and Luiz C. Kretly, Nanorobot Hardware Architecture for Medical Defense, sensor, ISSN 1424-8220, May, 2008.
• Harvey Crick F., Central dogma of Molecular Biology, Nature 227, 561-563, 1970.
• Weinberg. Robert A., The Biology of Cancer, Garland Science, 2006.
• Guerra. J., Nobuhara. Hajime., Fuzzy Configuration Space for Moving Obstacle Avoidance of Autonomous Mobile Robots, Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan, July 29, 2005.
• Ramos. Nelson., Lima. Pedro U., Sousa, Joao M.C., Robot Behavior Coordinate based in Fuzzy Decision-Making , Technical University of Lisbon Av. Rovisco Pais, 1049- 001 Lisbon, PORTUGAL.
• Kauffman, R.J(1999). stress signalling from the Stress signaling from the lumen of the endoplasmic reticulum: coordination of gene transcriptional and translational controls. Genes Dev. 13, 1211–1233.
• Lee, A.S. (2001). The glucose-regulated proteins: stress induction and clinical applications. Trends Biochem. Sci. 26, 504– 510.
• Arap, M.A. et.al.(2004). Cell surface expression of the stress response chaperone GRP78 enables tumor targeting by circulating ligands.
• Jamora, C., Dennert, G., and Lee, A.S. (1996). Inhibition of tumor progression by suppression of stress protein GRP78/BiP induction in fibrosarcoma B/C10ME. Proc. Natl. Acad. Sci. USA 93, 7690– 7694.
• Koong, A.C., Chen, E.Y., Lee, A.S., Brown, J.M., and Giaccia, A.J. (1994). Increased cytotoxicity of chronic hypoxic cells by molecular inhibition of GRP78 induction. Int. J. Radiat. Oncol. Biol. Phys. 28, 661–666.
• Li, L.J., Li, X., Ferrario, A., Rucker, N., Liu, E.S., Wong, S., Gomer, C.J., and Lee, A.S. (1992). Establishment of a Chinese hamster ovary cell line that expresses grp78 antisense transcripts and suppresses A23187 induction of both GRP78 and GRP94. J. Cell. Physiol. 153, 575–582.
• Miyake, H., Hara, I., Arakawa, S., and Kamidono, S. (2000). Stress protein GRP78 prevents apoptosis induced by calcium ionophore, ionomycin, but not by glycosylation inhibitor, tunicamycin, in human prostate cancer cells