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Reoccurring Patterns in Hierarchical Protein Materials and Music: The Power of Analogies

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Abstract

Complex hierarchical structures composed of simple nanoscale building blocks form the basis of most biological materials. Here, we demonstrate how analogies between seemingly different fields enable the understanding of general principles by which functional properties in hierarchical systems emerge, similar to an analogy learning process. Specifically, natural hierarchical materials like spider silk exhibit properties comparable to classical music in terms of their hierarchical structure and function. As a comparative tool, here, we apply hierarchical ontology logs that follow a rigorous mathematical formulation based on category theory to provide an insightful system representation by expressing knowledge in a conceptual map. We explain the process of analogy creation, draw connections at several levels of hierarchy, and identify similar patterns that govern the structure of the hierarchical systems silk and music and discuss the impact of the derived analogy for nanotechnology.

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References

  1. Gentner, D., Holyoak, K. J., Kokinov, B. N. (2001). The analogical mind: perspectives from cognitive science (Vol. xii, p. 541). Cambridge: MIT Press.

    Google Scholar 

  2. Bransford, J., National Research Council (U.S.), Committee on Developments in the Science of Learning., National Research Council (U.S.), & Committee on Learning Research and Educational Practice. (2000). How people learn: brain, mind, experience, and school (Vol. x, p. 374). Washington: National Academy Press.

    Google Scholar 

  3. Oppenheimer, R. (1956). Analogy in science. American Psychologist, 11, 127–135.

    Article  Google Scholar 

  4. Vosniadou, S., & Ortony, A. (1989). Similarity and analogical reasoning (Vol. xiv, p. 592). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  5. Taylor, P. C., Fraser, B. J., Fisher, D. L. (1997). Monitoring constructivist classroom learning environments. International Journal of Educational Research, 27, 293–302.

    Article  Google Scholar 

  6. Tsai, C.-C. (1999). Overcoming junior high school students’ misconceptions about microscopic views of phase change: a study of an analogy activity. Journal of Science Education and Technology, 8, 83–91.

    Article  Google Scholar 

  7. Stavy, R. (1991). Using analogy to overcome misconceptions about conservation of matter. Journal of Research in Science Teaching, 28, 305–313.

    Article  Google Scholar 

  8. Novick, L. R., & Holyoak, K. J. (1991). Mathematical problem-solving by analogy. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 398–415.

    Article  Google Scholar 

  9. Kaniel, S., Harpaz-Itay, Y., Ben-Amram, E. (2006). Analogy construction versus analogy solution, and their influence on transfer. Learning and Instruction, 16, 583–591.

    Article  Google Scholar 

  10. Spivak, D. I., & Kent, R. E. (2011). Ologs: a categorical framework for knowledge representation. PLoS ONE, e24274. doi:10.1371/journal.pone.0024274.

  11. Eilenberg, S., & Maclane, S. (1945). General theory of natural equivalences. Transactions of the American Mathematical Society, 58, 231–294.

    MathSciNet  MATH  Google Scholar 

  12. Ellis, N. C., Larsen-Freeman, D., Research Club in Language Learning (Ann Arbor Mich.). (2009). Language as a complex adaptive system (Vol. viii, p. 275). Chichester: Wiley.

    Google Scholar 

  13. Croft, W. (2010). Pragmatic functions, semantic classes, and lexical categories. Linguistics, 48, 787–796.

    Article  Google Scholar 

  14. Croft, W. (2003). Typology and universals (Vol. xxv, p. 341). Cambridge: Cambridge University Press.

    Google Scholar 

  15. Sica, G. (2006). What is category theory? (p. 290). Monza: Polimetrica.

    Google Scholar 

  16. Awodey, S. (2010). Category theory. London: Oxford University Press.

    MATH  Google Scholar 

  17. Cranford, S. W., & Buehler, M. J. (2010). Materiomics: biological protein materials, from nano to macro. Nanotechnology, Science and Applications, 3, 127–148.

    Google Scholar 

  18. Spivak, D. I., Giesa, T., Wood, E., Buehler, M. J. (2011). Category theoretic analysis of hierarchical protein materials and social networks. PLoS One, 6, e23911.

    Article  Google Scholar 

  19. Csermely, P. (2008). Creative elements: network-based predictions of active centres in proteins and cellular and social networks. Trends in Biochemical Sciences, 33, 569–576.

    Article  Google Scholar 

  20. Pugno, N. M. (2007). A statistical analogy between collapse of solids and death of living organisms: proposal for a ‘law of life’. Medical Hypotheses, 69, 441–447.

    Article  Google Scholar 

  21. Gimona, M. (2006). Protein linguistics—a grammar for modular protein assembly? Nature Reviews Molecular Cell Biology, 7, 68–73.

    Article  Google Scholar 

  22. Ji, S. C. (1997). Isomorphism between cell and human languages: molecular biological, bioinformatic and linguistic implications. Biosystems, 44, 17–39.

    Article  Google Scholar 

  23. Chomsky, N. (2002). Syntactic structures (Vol. xviii, p. 117). Berlin: Mouton de Gruyter.

    Book  Google Scholar 

  24. Nijholt, A. (1979). From left-regular to Greibach normal form grammars. Information Processing Letters, 9, 51–55.

    Article  MathSciNet  MATH  Google Scholar 

  25. Deline, G., Lin, F., Wen, D., Gagevic, D., Kinshuk, A. (2007). Ontology-driven development of intelligent educational systems. 2007 Ieee Pacific Rim Conference on Communications, Computers and Signal Processing, Vols. 1 and 2: 34–37.

  26. Halle, M. (2002). From memory to speech and back: papers on phonetics and phonology, 1954–2002 (Vol. vi, p. 261). Berlin: Mouton de Gruyter.

    Google Scholar 

  27. International Phonetic Association. (1999). Handbook of the International Phonetic Association: a guide to the use of the International Phonetic Alphabet (Vol. viii, p. 204). Cambridge: Cambridge University Press.

    Google Scholar 

  28. Clements, G. N. (1985). The geometry of phonological features. Phonology, 2, 225–252.

    Article  Google Scholar 

  29. Abramson, A. S. (1977). Laryngeal timing in consonant distinctions. Phonetica, 34, 295–303.

    Article  Google Scholar 

  30. Jessen, M., & Ringen, C. (2002). Laryngeal features in German. Phonology, 19, 189–218.

    Article  Google Scholar 

  31. Espinosa, H. D., & Bao, G. (Eds.). (2012). Nano and cell mechanics. New York: Wiley.

    Google Scholar 

  32. Moorer, J. A. (1977). Signal-processing aspects of computer music—survey. Proceedings of the IEEE, 65, 1108–1137.

    Article  Google Scholar 

  33. Cutting, J. E., & Rosner, B. S. (1974). Categories and boundaries in speech and music. Perception & Psychophysics, 16, 564–570.

    Article  Google Scholar 

  34. Buehler, M. J., & Yung, Y. C. (2009). Deformation and failure of protein materials in physiologically extreme conditions and disease. Nature Materials, 8, 175–188.

    Article  Google Scholar 

  35. Frishman, D., & Argos, P. (1995). Knowledge-based protein secondary structure assignment. Proteins-Structure Function and Genetics, 23, 566–579.

    Article  Google Scholar 

  36. Sun, Z. R., & Hua, S. J. (2001). A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. Journal of Molecular Biology, 308, 397–407.

    Article  Google Scholar 

  37. Nova, A., Keten, S., Pugno, N. M., Redaelli, A., Buehler, M. J. (2010). Molecular and nanostructural mechanisms of deformation, strength and toughness of spider silk fibrils. Nano Letters, 10, 2626–2634.

    Article  Google Scholar 

  38. Keten, S., & Buehler, M. J. (2010). Nanostructure and molecular mechanics of spider dragline silk protein assemblies. Journal of the Royal Society, Interface, 7, 1709–1721.

    Article  Google Scholar 

  39. Keten, S., & Buehler, M. J. (2010). Atomistic model of the spider silk nanostructure. Applied Physics Letters, 96, 153701.

    Article  Google Scholar 

  40. Keten, S., & Buehler, M. J. (2008). Asymptotic strength limit of hydrogen-bond assemblies in proteins at vanishing pulling rates. Physical Review Letters, 100(19), 198301.

    Article  Google Scholar 

  41. Keten, S., & Buehler, M. J. (2008). Geometric confinement governs the rupture strength of H-bond assemblies at a critical length scale. Nano Letters, 8, 743–748.

    Article  Google Scholar 

  42. Keten, S., Xu, Z., Ihle, B., Buehler, M. J. (2010). Nanoconfinement controls stiffness, strength and mechanical toughness of beta-sheet crystals in silk. Nature Materials, 9, 359–367.

    Article  Google Scholar 

  43. Erickson, R. (1975). Sound structure in music (Vol. ix, p. 205). Berkeley: University of California Press.

    Google Scholar 

  44. Bharucha, J., & Krumhansl, C. L. (1983). The representation of harmonic structure in music—hierarchies of stability as a function of context. Cognition, 13, 63–102.

    Article  Google Scholar 

  45. Shell, A., & Ellis, D. P. (2003). Chord segmentation and recognition using EM-trained hidden Markov models, pp. 185–191.

  46. Pardo, B., & Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26, 27–49.

    Article  Google Scholar 

  47. Pardo, B., & Birmingham, W. P. (2001). The chordal analysis of tonal music.

  48. Deutsch, D. (1969). Music recognition. Psychological Review, 76, 300.

    Article  Google Scholar 

  49. Jensen, K. (2007). Multiple scale music segmentation using rhythm, timbre, and harmony. Eurasip Journal on Advances in Signal Processing.

  50. Randel, D. M. (2003). The Harvard dictionary of music (Vol. xxvii, p. 978). Cambridge: Belknap Press of Harvard University Press.

    Google Scholar 

  51. Krumhansl, C. L., & Shepard, R. N. (1979). Quantification of the hierarchy of tonal functions within a diatonic context. Journal of Experimental Psychology. Human Perception and Performance, 5, 579–594.

    Article  Google Scholar 

  52. Izar, P., Ferreira, R. G., Sato, T. (2006). Describing the organization of dominance relationships by dominance-directed tree method. American Journal of Primatology, 68, 189–207.

    Article  Google Scholar 

  53. Tymoczko, D. (2011). A geometry of music: harmony and counterpoint in the extended common practice (Vol. xviii, p. 450). New York: Oxford University Press.

    MATH  Google Scholar 

  54. Maddage, N. C., Xu, C., Kankanhalli, M. S., Shao X. (2004). Content-based music structure analysis with applications to music semantics understanding. pp. 112–119.

  55. Schafer, T., & Sedlmeier, P. (2009). From the functions of music to music preference. Psychology of Music, 37, 279–300.

    Article  Google Scholar 

  56. Sloboda, J. A. (1991). Music structure and emotional response: some empirical findings. Psychology of Music, 19, 110–120.

    Article  Google Scholar 

  57. Hartmann, W. M. (1997). Signals, sound, and sensation (Vol. xvii, p. 647). Woodbury: American Institute of Physics.

    Google Scholar 

  58. Giesa, T., Arslan, M., Pugno, N., Buehler, M. J. (2011). Nano- confinement of spider silk fibrils begets superior strength, extensibility and toughness. Nano Letters. doi:10.1021/nl203108t.

  59. Rohrmeier, M. (2007). A generative grammar approach to diatonic harmonic structure. In: Anagnostopoulou Georgaki K, editor. Proceedings of the 4th Sound and Music Computing Conference. pp. 97–100.

  60. Bigand, E., Parncutt, R., Lerdahl, F. (1996). Perception of musical tension in short chord sequences: the influence of harmonic function, sensory dissonance, horizontal motion, and musical training. Perception & Psychophysics, 58, 125–141.

    Article  Google Scholar 

  61. Cranford, S. W., Tarakanova, A., Pugno N, Buehler M. J. (2011). Nonlinear behaviour of spider silk begets web robustness from the molecules up. In submission.

  62. Rohrmeier, M. (2011). Towards a generative syntax of tonal harmony. Journal of Mathematics and Music, 5, 35–53.

    Article  Google Scholar 

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Acknowledgments

We acknowledge support from AFOSR and DOD-PECASE (funded by ONR grant # N00014-10-1-0562). Additional support was received from the German National Academic Foundation (Studienstiftung des deutsches Volkes) and ONR grant # N00014-10-1-0841.

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Correspondence to Markus J. Buehler.

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Giesa, T., Spivak, D.I. & Buehler, M.J. Reoccurring Patterns in Hierarchical Protein Materials and Music: The Power of Analogies. BioNanoSci. 1, 153–161 (2011). https://doi.org/10.1007/s12668-011-0022-5

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