Computational Issues in Biomedical Nanometrics and Nano-Materials

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Biomedical Nanotechnology is an emerging area of great scientific and technological opportunity. It is widely recognized as one of the most potentially beneficial applications of nanotechnology to industry and society to date. Work in this area has a number of computational aspects: information technology based tools and measurement techniques are used to study biosystems with micro- and nano-scale physics and chemistry, and computational methods are helping to generate remarkable new insights into how biological systems function, how metabolic processes interrelate, and how new molecular scale machines can operate. This paper reviews current advances in computational algorithms and tools applied to biomedical nanometrics and nano-materials. We categorize algorithms into three general areas, describe representative methods, and conclude with several promising directions of future investigation.

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Periodical:

Edited by:

N. Ali

Pages:

50-58

DOI:

10.4028/www.scientific.net/JNanoR.1.50

Citation:

H. Huang et al., "Computational Issues in Biomedical Nanometrics and Nano-Materials", Journal of Nano Research, Vol. 1, pp. 50-58, 2008

Online since:

January 2008

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[1] P.W.K. Rothemund, Folding DNA to create nanoscale shapes and patterns, Nature 440 (2006) 297-302.

DOI: 10.1038/nature04586

[2] Z.Z. Zhang, C.H. Fan, L. He, Development of nano-scale DNA computing devices, Curr. Nanosci. 1 (2005) 91-95.

[3] S. Atwell, E. Meggers, G. Spraggon, P.G. Schultz, Structure of a copper-mediated base pair in DNA, J. Am. Chem. Soc. 123 (2001) 12364-12367.

DOI: 10.1021/ja011822e

[4] C. Brotschi, C.J. Leumann, Transition metal Ligands as novel DNA-base substitutes, Nucleosides Nucleotides Nucleic Acids 22 (2003) 1195-1197.

DOI: 10.1081/ncn-120022834

[5] G.H. Clever, K. Polborn, T. Carell, A highly DNA-duplex-stabilizing metal-salen base pair, Angew. Chem. Int. Ed. 44 (2005) 7204-7208.

DOI: 10.1002/anie.200501589

[6] C. Switzer, D. Shin, A pyrimidine-like nickel(II) DNA base pair, Chem. Comm. (2005) 1342-1344.

DOI: 10.1039/b415426f

[7] A.J. Steckl, DNA - a new material for photonics, Nature Photonics 1 (2006) 3 - 5.

[8] A. Condon, RNA molecules: Glimpses through an algorithmic lens, Latin 2006: Theor. Inform., Springer-Verlag Berlin, Berlin, 2006, pp.8-10.

DOI: 10.1007/11682462_2

[9] Z. Ezziane, DNA computing: applications and challenges, Nanotechnol. 17 (2006) R27-R39.

[10] R. Weiss, Challenges and opportunities in programming living cells, Natl. Acad. Eng. Bridge 34 (2003) 39-46.

[11] L.M. Adleman, Molecular computation of solutions to combinatorial problems, Science 266 (1994) 1021-1024.

[12] B. Grunbaum, G.C. Shephard, Tilings and Patterns, New York: Freeman (1986).

[13] N.C. Seeman, Biochemistry and structural DNA nanotechnology: an evolving symbiotic relationship, Biochem. 42 (2003) 7259-7269.

DOI: 10.1021/bi030079v

[14] H. Wang, Bell Systems Technical J., Proving Theorems by Pattern Recognition ii 40 (1961).

[15] J.H. Reif, T.H. LaBean, N.C. Seeman, Challenges and applications for self-assembled DNA nanostructures revised papers from the 6th Int. Workshop on DNA-Based Comput.: DNA Comput. 2000, pp.173-198.

DOI: 10.1007/3-540-44992-2_12

[16] E. Winfree, DNA computing by self-assembly, Natl. Acad. Eng. Bridge 34 (2003) 31-38.

[17] J.D. Le, Y. Pinto, N.C. Seeman, K. Musier-Forsyth, T.A. Taton, R.A. Kiehl, DNA-templated self-assembly of metallic nanocomponent arrays on a surface, Nano Lett. 4 (2004) 2343-2347.

DOI: 10.1021/nl048635+

[18] C. Dwyer, Computer-aided design for DNA self-assembly: process and applications, Proceedings of the IEEE/ACM Int. Conf. on Comput. Aided Design (2005) 662-667.

DOI: 10.1109/iccad.2005.1560149

[19] S.K. Das, A.D. Rey, Computational modelling of multiscale morphologies in polymer-liquid crystal blends, Nanotechnol. 16 (2005) S330-S337.

DOI: 10.1088/0957-4484/16/7/004

[20] G. Srinivas, M.L. Klein, Computational approaches to nanobiotechnology: probing the interaction of synthetic molecules with phospholipid bilayers via a coarse grain model, Nanotechnol. 15 (2004) 1289-1295.

DOI: 10.1088/0957-4484/15/9/030

[21] A. Dubey, C. Mavroidis, M.S. Tomassone, Molecular dynamic studies of viral-protein based nano-actuators, J. Comput. Theor. Nanosci. 3 (2006) 885-897.

[22] A. Dubey, G. Sharma, C. Mavroidis, M.S. Tomassone, K. Nikitczuk, M.L. Yarmush, Computational studies of viral protein nano-actuators. , J. Comput. Theor. Nanosci. 1 (2004) 18-28.

DOI: 10.1109/robot.2004.1308057

[23] L. Adleman, J. Kari, L. Kari, D. Reishus, On the decidability of self-assembly of infinite ribbons, Proceedings of FOCS 2002, IEEE Symp. Found. of Comput. Sci. (2002) 530-537.

DOI: 10.1109/sfcs.2002.1181977

[24] M.F. Cohen, J. Shade, S. Hiller, O. Deussen, Wang Tiles for image and texture generation, ACM Trans. Graph. 22 (2003) 287-294.

DOI: 10.1145/882262.882265

[25] M. Watson, C. Worman, Tiling layouts with dominos, Proc. 16th Canadian Conf. on Comput. Geometry (2004) 86-90.

[26] H.R. Lewis, C.H. Papadimitriou, Elements of the Theory of Computation, Prentice-Hall (1981).

[27] E. Winfree, R.J. Lipton, On the computational power of DNA annealing and ligation, DNA Based Computers, Am. Math. Soc. 27 (1996) 199-221.

[28] N. Jonoska, S. Karl, Ligation experiments in computing with dna, IEEE Int. Conf. on Evol. Comput. (1997) 261-265.

[29] N. Jonoska, S.A. Karl, M. Saito, Graph structures in dna computing, Comput. with Bio-Mol., Theory and Exp. (1998) 93-110.

[30] M.G. Lagoudakis, T.H. LaBean, 2-D DNA self-assembly for satisfiability, DNA Based Comput. 54 141-154.

[31] M. Ogura, H. Akai, Electric field gradients of light impurities in TiO2 calculated by the full potential KKR green's function method, Hyperfine Interact. 158 (2004) 99-103.

DOI: 10.1007/s10751-005-9015-5

[32] E.F. Sheka, V.D. Khavryutchenko, Nanomaterial: Real and Computational Model, Nanostruct. Mater. 6 (1995).

[33] O.V. Salata, Applications of nanoparticles in biology and medicine, J. Nanobiotechnol. 2 (2004) 3.

[34] G.M. Whitesides, The right, size in nanobiotechnology, Nature Biotechnol. 21 (2003) 1161-1165.

[35] S. Subramaniam, J.L. Milne, Three-dimensional electron microscopy at molecular resolution, Annu. Revi. Biophys. Biomol. Struct. 33 (2004) 141-155.

DOI: 10.1146/annurev.biophys.33.110502.140339

[36] J. -J. Fernandez, J. -M. Carazo, I. Garcia, Three-dimensional reconstruction of cellular structures by electron microscope tomography and parallel computing, J. Parallel Distrib. Comput. 64 (2004) 285-300.

DOI: 10.1016/j.jpdc.2003.06.005

[37] L.C. Gontard, R.E. Dunin-Borkowski, D. Ozkaya, T. Hyde, P.A. Midgley, P. Ash, Crystal size and shape analysis of Pt nanoparticles in two and three dimensions, J. Physics: Conf. Series 26 (2006) 367-370.

DOI: 10.1088/1742-6596/26/1/089

[38] M. Jacob, T. Blu, M. Unser, 3-D reconstruction of DNA filaments from stereo Cryo-electron micrographs, Proceedings of the first 2002 IEEE Int. Symp. on. Biomed. Imaging: macro to nano (2002) 597-600.

DOI: 10.1109/isbi.2002.1029328

[39] M. Jacob, T. Blu, C. Vaillant, J.H. Maddock, M. Unser, 3-D shape estimation of DNA moleculesfrom stereo Cryo-electron micro-graphs using a projection-steerable snake, IEEE Trans. on Image Process. 15 (2006) 214-227.

DOI: 10.1109/tip.2005.860310

[40] J.E. Smith, C.D. Medley, Z. Tang, D. Shangguan, C. Lofton, W. Tan, Aptamer-conjugated nanoparticles for the collection and detection of multiple cancer cells, Anal Chem. 79 (2007) 3075-3082.

DOI: 10.1021/ac062151b

[41] V. Dixit, J.V. d. Bossche, D.M. Sherman, D.H. Thompson, R.P. Andres, Synthesis and grafting of thioctic Acid-PEG-Folate conjugates onto Au nanoparticles for selective targeting of Folate receptor-positive tumor cells, Bioconjugate Chem. 17 (2006).

DOI: 10.1021/bc050335b

[42] Z. -L. Magali, L. Norbert, G. Robert, D. Florence, Hypericin-loaded nanoparticles for the photodynamic treatment of ovarian cancer, Int. J. of Pharm. 326 (2006) 174-181.

DOI: 10.1016/j.ijpharm.2006.07.012

[43] J. Wang, G. Liu, M. Jan, Ultrasensitive electrical biosensing of proteins and DNA: Carbon-nanotube derived amplification of the eecognition and transduction events, J. Am. Chem. Soc. 126 (2004) 3010.

DOI: 10.1021/ja031723w

[44] J. Wang, A. Kawde, M. Mustafa, Carbon nanotubes modified glassy Carbon electrodes for amplified detection of DNA hybridization, Analyst 128 (2003) 912.

DOI: 10.1039/b303282e

[45] P. Barone, M. Strano, Reversible control of carbon nanotube aggregation for a glucose affinity sensor, Angewante Chemie 45 (2006) 8138-8141.

DOI: 10.1002/anie.200603138

[46] T. Merryman, J. Kovacevic, An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets, IEEE Trans Image Process. 14 (2005) 1246-1253.

DOI: 10.1109/tip.2005.855861

[47] M. Velliste, R.F. Murphy, Automated determination of protein subcellular locations from 3-D fluorescence microscope images, Proc. IEEE Int. Symp. Biomed. Imaging (2002) 867-870.

DOI: 10.1109/isbi.2002.1029397

[48] K.A. Lidke, B. Rieger, D.S. Lidke, T.M. Jovin, The role of photon statistics in fluorescence anisotropy imaging, IEEE Trans. on Image Process. 14 (2005) 1237-1245.

DOI: 10.1109/tip.2005.852458

[49] Y. Gat, A branch-and-bound technique for nano-structure image segmentation, Proceedings of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshop: Comput. Vis. for the Nano Scale (2003).

DOI: 10.1109/cvprw.2003.10017

[50] X. Luciani, L. Patrone, P. Courmontagne, Nano-domains segmentation on AFM images, 10th Int. Conf. on the Formation of Semiconductor Interfaces 132 (2006) 237-241.

DOI: 10.1051/jp4:2006132045

[51] B.L. Luck, K.D. Carlson, A.C. Bovik, R.R. Richards-Kortum, An image model and segmentation algorithm for reflectance confocal images of in Vivo cervical tissue, IEEE Trans. on Image Process. 14 (2005) 1265-1276.

DOI: 10.1109/tip.2005.852460

[52] H. Scharr, M. Felsberg, P. -E. Forssén, Noise adaptive channel smoothing of low-dose Images, Proceedings of IEEE Computer Society Conf. on Comput. Vis. and Pattern Recognit. Workshop: Comput. Vis. for the Nano Scale (2003).

DOI: 10.1109/cvprw.2003.10018

[53] Z. Yu, C. Bajaj, Automatic ultrastructure segmentation of reconstructed CryoEM maps of icosahedral viruses, IEEE Trans. on Image Process. 14 (2005) 1324-1337.

DOI: 10.1109/tip.2005.852770

[54] M. -A. Abdul-Karim, K. Al-Kofahi, E.B. Brown, R.K. Jain, B. Roysam, Automated tracing and change analysis of angiogenic vasculature from in vivo multiphoton confocal image time series, Microvasc. Res. 66 ( 2003) 113-125.

DOI: 10.1016/s0026-2862(03)00039-6

[55] K.A. Al-Kofahi, A. Can, S. Lasek, D.H. Szarowski, N. Dowell-Mesfin, W. Shain, J.N. Turner, B. Roysam, Median-based robust algorithms for tracing neurons from noisy confocal microscope images, IEEE Trans. Inf. Technol. Biomed. 7 (2003) 302-317.

DOI: 10.1109/titb.2003.816564

[56] K.A. Al-Kofahi, S. Lasek, D.H. Szarowski, C.J. Pace, G. Nagy, J.N. Turner, B. Roysam, Rapid automated three-dimensional tracing of neurons from confocal image stacks, IEEE Trans. Inf. Technol. Biomed. 6 (2002) 171-187.

DOI: 10.1109/titb.2002.1006304

[57] E. Meijering, M. Jacob, J.C. Sarria, P. Steiner, H. Hirling, M. Unser, Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images, Cytometry A 58 (2004) 167-176.

DOI: 10.1002/cyto.a.20022

[58] M. Maddah, A. Afzali-Kusha, H. Soltanian-Zadeh, Efficient center-line extraction for quantification of vessels in confocal microscopy images, Med. Phys. 30 (2003) 204-211.

DOI: 10.1118/1.1782675

[59] M. -A. Abdul-Karim, B. Roysam, N.M. Dowell-Mesfin, A. Jeromin, M. Yuksel, Shivkumar Kalyanaraman, Automatic selection of parameters for vessel/neurite segmentation algorithms, IEEE Trans. on Image Process. 14 (2005) 1338-1350.

DOI: 10.1109/tip.2005.852462

[60] J. Ryu, B.K.P. Horn, M.S. Mermelstein, S. Hong, D.M. Freeman, Application of structured illumination in nano-Scale vision, Proceedings of IEEE Comput. Soc. Conf. on Comput. Vis. and Pattern Recognit. Workshop: Comput. Vis. for the Nano Scale (2003).

DOI: 10.1109/cvprw.2003.10019

[61] T.S. Ralston, D.L. Marks, F. Kamalabadi, Stephen A. Boppart, Deconvolution methods for mitigation of transverse blurring in optical coherence tomography, IEEE Trans. on Image Process. 14 (2005) 1254-1264.

DOI: 10.1109/tip.2005.852469

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