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An Analytic Toolbox for Simulated Filament Networks

Published online by Cambridge University Press:  11 September 2014

Ronald J. Pandolfi
Affiliation:
Dept. of Physics, School of Natural Sciences, University of California, Merced, California 95343, USA
Lauren Edwards
Affiliation:
Dept. of Physics, School of Natural Sciences, University of California, Merced, California 95343, USA
Linda S. Hirst
Affiliation:
Dept. of Physics, School of Natural Sciences, University of California, Merced, California 95343, USA
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Abstract

Semi-flexible polymer networks generate a diverse family of structures. The network generating behaviors of specific semi-flexible biological filaments are well known (i.e. F-actin, microtubules, DNA etc.), however recent developments in tunable synthetic filaments extend the range of accessible structures. A similarly tunable model was developed using the molecular dynamics platform NAMD to provide a guide for generating synthetic filament networks. Structural characteristics of simulated networks may be quantitatively examined using connectivity analysis, radial pair distribution functions and scaling analysis. These methods provide a basis to calculate morphological properties, including mesh size, packing order, network connectivity, avg. cluster size, filaments per bundle, and space-filling dimensionality. An analytic toolset for describing the structure of filament networks is thus provided by detailing these methods.

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Articles
Copyright
Copyright © Materials Research Society 2014 

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References

REFERENCES

Lodish, H., Berk, A., Zipursky, S. L., Matsudaira, P., Baltimore, D., and Darnell, J.., Molecular Cell Biology, 4th ed. (Freeman, New York, 1999).Google Scholar
Hirst, L. S. and Safinya, C. R., Phys. Rev. Lett. 93, 018101 (2004).CrossRefGoogle Scholar
Pelletier, O., Pokidysheva, E., Hirst, L. S., Bouxsein, N., Li, Y., and Safinya, C. R., Phys. Rev. Lett. 91, 3 (2003).CrossRefGoogle Scholar
Kouwer, P. H. J., Koepf, M., a a Le Sage, V., Jaspers, M., van Buul, A. M., Eksteen-Akeroyd, Z. H., Woltinge, T., Schwartz, E., Kitto, H. J., Hoogenboom, R., Picken, S. J., Nolte, R. J. M., Mendes, E., and Rowan, A. E., Nature 493, 651 (2013).Google Scholar
Peppas, N. A., Hilt, J. Z., Khademhosseini, A., and Langer, R., Adv. Mater. 18, 1345 (2006).CrossRefGoogle Scholar
Hirst, A. R., Escuder, B., Miravet, J. F., and Smith, D. K., Angew. Chem. Int. Ed. Engl. 47, 8002 (2008).CrossRefGoogle Scholar
Tiller, J. C., Angew. Chem. Int. Ed. Engl. 42, 3072 (2003).CrossRefGoogle Scholar
Place, E. S., Evans, N. D., and Stevens, M. M., Nat. Mater. 8, 457 (2009).CrossRefGoogle Scholar
Agrawal, A., Rahbar, N., and Calvert, P. D., Acta Biomater. 9, 5313 (2013).Google Scholar
Nguyen, L. T., Yang, W., Wang, Q., and Hirst, L. S., Soft Matter 5, 2033 (2009).CrossRefGoogle Scholar
Nguyen, L. T. and Hirst, L. S., Phys. Rev. E 83, 1 (2011).CrossRefGoogle Scholar
Pandolfi, R. J., Edwards, L., Johnston, D., Becich, P., and Hirst, L. S., in press Phys. Rev. E (2014).Google Scholar
De Podesta, M., Understanding the Properties of Matter (CRC Press, 2002).Google Scholar
Mandelbrot, B. B., The Fractal Geometry of Nature (Macmillan, 1983).CrossRefGoogle Scholar
Hirst, L. S., Pynn, R., Bruinsma, R. F., and Safinya, C. R., J. Chem. Phys. 123, 104902 (2005).CrossRefGoogle Scholar