Skip to main content
SpringerLink
Log in

Computational Approaches and Simulation

  • Chapter
Biomateriomics

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 165))

  • 1788 Accesses

Abstract

Computation and simulation provides a means to investigate complex materiomic systems with unparalleled control and accuracy. At the same time, a holistic description of a material system necessitates knowledge of the lowest possible scale—atomistic and molecular interactions. While quantum level resolution provides a means to understand atom-to-atom interactions, molecular interactions provides the foundation for deterministic (or predictable) mechanistic behavior. In recent years, molecular dynamics has developed into a powerful tool to investigate biological systems such as the stretching of proteins and other macromolecules. The advent of reactive molecular dynamics (wherein chemical bonds can be formed or ruptured) has extended the range of applications at the nanoscale. Being said, the limitations of full atomistic simulation (in terms of accessible time and length scales) has necessitated coarse-grain and other multiscale methods, in a bottom-up “fine-trains-coarse” paradigm. Not unlike the reduction of engineering analysis to critical components, such multiscale methods can be used to bridge each structural hierarchy, characterize performance and behavior, and successfully explore the entire materiome via simulation.

Computers are useless. They can only give you answers.

Pablo Picasso (1881–1973)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We note that the computational and simulation techniques discussed herein are by no means intended to be exhaustive, presented in full depth, or canonical. Focus is particularly given to molecular dynamics approaches and coarse-grain methodologies insofar as they are relevant to biological materials. The intent is to illustrate a multiscale paradigm necessary to a materiomic perspective, and not provide a robust technical guide or resource. Interested readers are directed to the suggested readings at the end of the chapter.

  2. 2.

    This does not imply that all models are useful, merely the fact that models are more akin to a theory or piece of knowledge—abstract and nonphysical—than a tangible experimental specimen. At times “failed models” are most useful as they teach us what is missing but other models, like failed theories, are best forgotten.

  3. 3.

    They can, however, be inferred by clever modeling.

  4. 4.

    http://www.gromacs.org/.

  5. 5.

    http://www.ks.uiuc.edu/research/namd/.

  6. 6.

    http://lammps.sandia.gov/.

References

  1. W. Goddard, A perspective of materials modeling, in Handbook of Materials Modeling, ed. by S. Yip (Springer, Berlin, 2006)

    Google Scholar 

  2. N. Metropolis, S. Ulam, The Monte Carlo method. J. Am. Stat. Assoc. 44(247), 335–341 (1949)

    Article  CAS  Google Scholar 

  3. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, E. Teller, Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)

    Article  CAS  Google Scholar 

  4. B.J. Alder, T.E. Wainwright, Phase transition for a hard sphere system. J. Chem. Phys. 27(5), 1208–1209 (1957)

    Article  CAS  Google Scholar 

  5. B.J. Alder, T.E. Wainwright, Studies in molecular dynamics. 1. General method. J. Chem. Phys. 31(2), 459–466 (1959)

    Article  CAS  Google Scholar 

  6. B.J. Alder, T.E. Wainwright, Studies in molecular dynamics. 2. Behavior of a small number of elastic spheres. J. Chem. Phys. 33(5), 1439–1451 (1960)

    Article  CAS  Google Scholar 

  7. A. Rahman, Correlations in the motion of atoms in liquid argon. Phys. Rev. 136, 405–411 (1964)

    Article  CAS  Google Scholar 

  8. A. Rahman, Fh. Stilling, Molecular dynamics study of liquid water. J. Chem. Phys. 55(7), 3336 (1971)

    Article  CAS  Google Scholar 

  9. P.Y. Chou, G.D. Fasman, Prediction of protein conformation. Biochemistry 13(2), 222–245 (1974)

    Article  CAS  Google Scholar 

  10. M. Levitt, A. Warshel, Computer-simulation of protein folding. Nature 253(5494), 694–698 (1975)

    Article  CAS  Google Scholar 

  11. B.R. Gelin, M. Karplus, Sidechain torsional potentials and motion of amino-acids in proteins—bovine pancreatic trypsin-inhibitor. Proc. Natl. Acad. Sci. USA 72(6), 2002–2006 (1975)

    Article  CAS  Google Scholar 

  12. J.A. Mccammon, B.R. Gelin, M. Karplus, Dynamics of folded proteins. Nature 267(5612), 585–590 (1977)

    Article  CAS  Google Scholar 

  13. D. Van der Spoel, E. Lindahl, B. Hess, G. Groenhof, A.E. Mark, H.J.C. Berendsen, Gromacs: fast, flexible, and free. J. Comput. Chem. 26(16), 1701–1718 (2005)

    Article  CAS  Google Scholar 

  14. M.T. Nelson, W. Humphrey, A. Gursoy, A. Dalke, L.V. Kale, R.D. Skeel, K. Schulten, Namd: a parallel, object oriented molecular dynamics program. Int. J. Supercomput. Appl. High Perform. Comput. 10(4), 251–268 (1996)

    Google Scholar 

  15. S. Plimpton, Fast parallel algorithms for short-range molecular-dynamics. J. Comput. Phys. 117(1), 1–19 (1995)

    Article  CAS  Google Scholar 

  16. M.P. Allen, D.J. Tildesley, Computer Simulation of Liquids (Oxford University Press, Oxford, 1987)

    Google Scholar 

  17. P.M. Morse, Diatomic molecules according to the wave mechanics. ii. vibrational levels. Phys. Rev. 34(1), 57–64 (1929)

    Article  CAS  Google Scholar 

  18. J. Tersoff, Empirical interatomic potential for carbon, with applications to amorphous-carbon. Phys. Rev. Lett. 61(25), 2879–2882 (1988)

    Article  CAS  Google Scholar 

  19. F.H. Stillinger, T.A. Weber, Computer-simulation of local order in condensed phases of silicon. Phys. Rev. B 31(8), 5262–5271 (1985)

    Article  CAS  Google Scholar 

  20. D.J. Oh, R.A. Johnson, Simple embedded atom method model for fcc and hcp metals. J. Mater. Res. 3(3), 471–478 (1988)

    Article  CAS  Google Scholar 

  21. J.E. Angelo, M.I. Baskes, Interfacial studies using the eam and meam. Interface Sci. 4(1–2), 47–63 (1996)

    CAS  Google Scholar 

  22. M.S. Daw, M.I. Baskes, Embedded-atom method—derivation and application to impurities, surfaces, and other defects in metals. Phys. Rev. B 29(12), 6443–6453 (1984)

    Article  CAS  Google Scholar 

  23. Z. Qin, M.J. Buehler, Molecular dynamics simulation of the alpha-helix to beta-sheet transition in coiled protein filaments: evidence for a critical filament length scale. Phys. Rev. Lett. 104(19) (2010)

    Google Scholar 

  24. A.D. MacKerell, D. Bashford, M. Bellott, R.L. Dunbrack, J.D. Evanseck, M.J. Field, S. Fischer, J. Gao, H. Guo, S. Ha, D. Joseph-McCarthy, L. Kuchnir, K. Kuczera, F.T.K. Lau, C. Mattos, S. Michnick, T. Ngo, D.T. Nguyen, B. Prodhom, W.E. Reiher, B. Roux, M. Schlenkrich, J.C. Smith, R. Stote, J. Straub, M. Watanabe, J. Wirkiewicz-Kuczera, D. Yin, M. Karplus, All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 102(18), 3586–3616 (1998)

    Article  CAS  Google Scholar 

  25. J.W. Ponder, D.A. Case, Force fields for protein simulations. Protein Simul. 66, 27 (2003)

    Article  CAS  Google Scholar 

  26. A.D. Mackerell, Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 25(13), 1584–1604 (2004)

    Article  CAS  Google Scholar 

  27. A.K. Rappe, C.J. Casewit, K.S. Colwell, W.A. Goddard, W.M. Skiff, Uff, a full periodic-table force-field for molecular mechanics and molecular-dynamics simulations. J. Am. Chem. Soc. 114(25), 10024–10035 (1992)

    Article  CAS  Google Scholar 

  28. D.A. Pearlman, D.A. Case, J.W. Caldwell, W.S. Ross, I. Cheatham, S. DeBolt, D. Ferguson, G. Seibel, P. Kollman, Amber, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comput. Phys. Commun. 91(1), 1–41 (1995)

    Article  CAS  Google Scholar 

  29. W. Wang, O. Donini, C.M. Reyes, P.A. Kollman, Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu. Rev. Biophys. Biomol. Struct. 30, 211–243 (2001)

    Article  CAS  Google Scholar 

  30. H.A. Scheraga, M. Khalili, A. Liwo, Protein-folding dynamics: overview of molecular simulation techniques. Annu. Rev. Biophys. Bioeng. 58, 57–83 (2007)

    CAS  Google Scholar 

  31. A.A. Deniz, S. Mukhopadhyay, E.A. Lemke, Single-molecule biophysics: at the interface of biology, physics and chemistry. J. R. Soc. Interface 5(18), 15–45 (2008)

    Article  CAS  Google Scholar 

  32. M.J. Buehler, S. Keten, Colloquium: failure of molecules, bones, and the earth itself. Rev. Mod. Phys. 82(2), 1459 (2010)

    Article  Google Scholar 

  33. H.J. Gao, A theory of local limiting speed in dynamic fracture. J. Mech. Phys. Solids 44(9), 1453–1474 (1996)

    Article  CAS  Google Scholar 

  34. M.J. Buehler, F.F. Abraham, H.J. Gao, Hyperelasticity governs dynamic fracture at a critical length scale. Nature 426(6963), 141–146 (2003)

    Article  CAS  Google Scholar 

  35. A.C.T. van Duin, S. Dasgupta, F. Lorant, W.A. Goddard, Reaxff: a reactive force field for hydrocarbons. J. Phys. Chem. A 105(41), 9396–9409 (2001)

    Article  CAS  Google Scholar 

  36. A.C.T. van Duin, A. Strachan, S. Stewman, Q.S. Zhang, X. Xu, W.A. Goddard, Reaxff(sio) reactive force field for silicon and silicon oxide systems. J. Phys. Chem. A 107(19), 3803–3811 (2003)

    Article  CAS  Google Scholar 

  37. D.W. Brenner, O.A. Shenderova, J.A. Harrison, S.J. Stuart, B. Ni, S.B. Sinnott, A second-generation reactive empirical bond order (rebo) potential energy expression for hydrocarbons. J. Phys., Condens. Matter 14(4), 783–802 (2002)

    Article  CAS  Google Scholar 

  38. S.J. Stuart, A.B. Tutein, J.A. Harrison, A reactive potential for hydrocarbons with intermolecular interactions. J. Chem. Phys. 112(14), 6472–6486 (2000)

    Article  CAS  Google Scholar 

  39. A. Strachan, E.M. Kober, A.C.T. van Duin, J. Oxgaard, W.A. Goddard, Thermal decomposition of rdx from reactive molecular dynamics. J. Chem. Phys. 122(5), (2005)

    Article  CAS  Google Scholar 

  40. K. Chenoweth, S. Cheung, A.C.T. van Duin, W.A. Goddard, E.M. Kober, Simulations on the thermal decomposition of a poly(dimethylsiloxane) polymer using the reaxff reactive force field. J. Am. Chem. Soc. 127(19), 7192–7202 (2005)

    Article  CAS  Google Scholar 

  41. K.D. Nielson, A.C.T. van Duin, J. Oxgaard, W.Q. Deng, W.A. Goddard, Development of the reaxff reactive force field for describing transition metal catalyzed reactions, with application to the initial stages of the catalytic formation of carbon nanotubes. J. Phys. Chem. A 109(3), 493–499 (2005)

    Article  CAS  Google Scholar 

  42. S.S. Han, A.C.T. van Duin, W.A. Goddard, H.M. Lee, Optimization and application of lithium parameters for the reactive force field, reaxff. J. Phys. Chem. A 109(20), 4575–4582 (2005)

    Article  CAS  Google Scholar 

  43. S. Cheung, W.Q. Deng, A.C.T. van Duin, W.A. Goddard, Reaxff(mgh) reactive force field for magnesium hydride systems. J. Phys. Chem. A 109(5), 851–859 (2005)

    Article  CAS  Google Scholar 

  44. M.J. Buehler, Hierarchical chemo-nanomechanics of proteins: entropic elasticity, protein unfolding and molecular fracture. J. Mech. Mater. Struct. 2(6), 1019–1057 (2007)

    Article  Google Scholar 

  45. A.N. Parbhu, W.G. Bryson, R. Lal, Disulfide bonds in the outer layer of keratin fibers confer higher mechanical rigidity: correlative nano-indentation and elasticity measurement with an afm. Biochemistry 38(36), 11755–11761 (1999)

    Article  CAS  Google Scholar 

  46. H. Wang, D.A.D. Parry, L.N. Jones, W.W. Idler, L.N. Marekov, P.M. Steinert, In vitro assembly and structure of trichocyte keratin intermediate filaments: a novel role for stabilization by disulfide bonding. J. Cell Biol. 151(7), 1459–1468 (2000)

    Article  CAS  Google Scholar 

  47. O. Mayans, J. Wuerges, S. Canela, M. Gautel, M. Wilmanns, Structural evidence for a possible role of reversible disulphide bridge formation in the elasticity of the muscle protein titin. Structure 9(4), 331–340 (2001)

    Article  CAS  Google Scholar 

  48. N. Mucke, L. Kreplak, R. Kirmse, T. Wedig, H. Herrmann, U. Aebi, J. Langowski, Assessing the flexibility of intermediate filaments by atomic force microscopy. J. Mol. Biol. 335(5), 1241–1250 (2004)

    Article  CAS  Google Scholar 

  49. F. Aslund, J. Beckwith, Bridge over troubled waters: sensing stress by disulfide bond formation. Cell 96(6), 751–753 (1999)

    Article  CAS  Google Scholar 

  50. P.J. Hogg, Disulfide bonds as switches for protein function. Trends Biochem. Sci. 28(4), 210–214 (2003)

    Article  CAS  Google Scholar 

  51. S. Keten, C.-C. Chou, A.C.T. van Duin, M.J. Buehler, Tunable nanomechanics of protein disulfide bonds in redox microenvironments. J. Mech. Behav. Biomed. Mater. 5(1), 32–40 (2012)

    Article  CAS  Google Scholar 

  52. A.P. Wiita, S.R.K. Ainavarapu, H.H. Huang, J.M. Fernandez, Force-dependent chemical kinetics of disulfide bond reduction observed with single-molecule techniques. Proc. Natl. Acad. Sci. USA 103(19), 7222–7227 (2006)

    Article  CAS  Google Scholar 

  53. M. Bonomi, D. Branduardi, G. Bussi, C. Camilloni, D. Provasi, P. Raiteri, D. Donadio, F. Marinelli, F. Pietrucci, R.A. Broglia, M. Parrinello, Plumed: a portable plugin for free-energy calculations with molecular dynamics. Comput. Phys. Commun. 180(10), 1961–1972 (2009)

    Article  CAS  Google Scholar 

  54. M. Bonomi, M. Parrinello, Enhanced sampling in the well-tempered ensemble. Phys. Rev. Lett. 104(19), 190601 (2010)

    Article  CAS  Google Scholar 

  55. J. Kubelka, J. Hofrichter, W.A. Eaton, The protein folding ‘speed limit’. Curr. Opin. Struct. Biol. 14(1), 76–88 (2004)

    Article  CAS  Google Scholar 

  56. A. Laio, M. Parrinello, Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 99(20), 12562–12566 (2002)

    Article  CAS  Google Scholar 

  57. A.F. Voter, F. Montalenti, T.C. Germann, Extending the time scale in atomistic simulation of materials. Annu. Rev. Mater. Res. 32, 321–346 (2002)

    Article  CAS  Google Scholar 

  58. A. Kushima, X. Lin, J. Li, J. Eapen, J.C. Mauro, X.F. Qian, P. Diep, S. Yip, Computing the viscosity of supercooled liquids. J. Chem. Phys. 130(22), (2009)

    Google Scholar 

  59. M.J. Alava, P.K.V.V. Nukalaz, S. Zapperi, Statistical models of fracture. Adv. Phys. 55(3–4), 349–476 (2006)

    Article  Google Scholar 

  60. Y. Sugita, Y. Okamoto, Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314, 141–151 (1999)

    Article  CAS  Google Scholar 

  61. A. Gautieri, S. Vesentini, A. Redaelli, M.J. Buehler, Hierarchical structure and nanomechanics of collagen microfibrils from the atomistic scale up. Nano Lett. 11(2), 757–766 (2011)

    Article  CAS  Google Scholar 

  62. K.Y. Sanbonmatsu, C.S. Tung, High performance computing in biology: multimillion atom simulations of nanoscale systems. J. Struct. Biol. 157(3), 470–480 (2007)

    Article  CAS  Google Scholar 

  63. K. Kadau, T.C. Germann, P.S. Lomdahl, Molecular dynamics comes of age: 320 billion atom simulation on bluegene/l. Int. J. Mod. Phys. C 17(12), 1755–1761 (2006)

    Article  CAS  Google Scholar 

  64. V. Tozzini, Coarse-grained models for proteins. Curr. Opin. Struct. Biol. 15, 144–150 (2005)

    Article  CAS  Google Scholar 

  65. D.W. Brenner, The art and science of an analytic potential. Phys. Status Solidi B 217(1), 23–40 (2000)

    Article  CAS  Google Scholar 

  66. R. Car, M. Parrinello, Unified approach for molecular dynamics and density-functionaly theory. Phys. Rev. Lett. 55(22), 2471–2474 (1985)

    Article  CAS  Google Scholar 

  67. B.R. Brooks, R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, M. Karplus, Charmm: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4(2), 187–217 (1983)

    Article  CAS  Google Scholar 

  68. P. Sherwood, B.R. Brooks, M.S.P. Sansom, Multiscale methods for macromolecular simulations. Curr. Opin. Struct. Biol. 18, 630–640 (2008)

    Article  CAS  Google Scholar 

  69. F. Tama, I.C.L. Brooks, Symmetry, form, and shape: guiding principles for robustness in macromolecular machines. Annu. Rev. Biophys. Biomol. Struct. 35, 115–133 (2006)

    Article  CAS  Google Scholar 

  70. I. Bahar, A.J. Rader, Coarse-grain normal model analysis in structural biology. Curr. Opin. Struct. Biol. 15, 586–592 (2005)

    Article  CAS  Google Scholar 

  71. M.M. Tirion, Large amplitude elastic motions in proteins from a single-parameter, atomic analysis. Phys. Rev. Lett. 77(9), 1905–1908 (1996)

    Article  CAS  Google Scholar 

  72. T. Haliloglu, I. Bahar, B. Erman, Gaussian dynamics of folded proteins. Phys. Rev. Lett. 79(16), 3090–3093 (1997)

    Article  CAS  Google Scholar 

  73. S. Hayward, N. Go, Collective variable description of native protein dynamics. Annu. Rev. Biophys. Bioeng. 46, 223–250 (1995)

    CAS  Google Scholar 

  74. L. Meireles, M. Gur, A. Bakan, I. Bahar, Pre-existing soft modes of motion uniquely defined by native contact topology facilitate ligand binding to proteins. Protein Sci. 20(10), 1645–1658 (2011)

    Article  CAS  Google Scholar 

  75. A.R. Atilgan, S.R. Durell, R.L. Jernigan, M.C. Demirel, O. Keskin, I. Bahar, Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 80, 505–515 (2001)

    Article  CAS  Google Scholar 

  76. P. Doruker, R.L. Jernigan, I. Bahar, Dynamics of large proteins through hierarchical levels of coarse-grained structures. J. Comput. Chem. 23(1), 119–127 (2002)

    Article  CAS  Google Scholar 

  77. I. Navizet, R. Lavery, R.L. Jernigan, Myosin flexibility: structural domains and collective vibrations. Protein. Struct. Funct. Bioinform. 54, 384–393 (2004)

    Article  CAS  Google Scholar 

  78. W. Zheng, S. Doniach, A comparative study of motor-protein motions by using a simple elastic-network model. Proc. Natl. Acad. Sci. USA 100(23), 13253–13258 (2003)

    Article  CAS  Google Scholar 

  79. H. Dietz, M. Rief, Elastic bond network model for protein unfolding mechanics. Phys. Rev. Lett. 100, 098101 (2008)

    Article  CAS  Google Scholar 

  80. D.K. West, D.J. Brockwell, P.D. Olmsted, S.E. Radford, E. Paci, Mechanical resistance of proteins explained using simple molecular models. Biophys. J. 90(1), 287–297 (2006)

    Article  CAS  Google Scholar 

  81. J.I. Sulkowska, M. Cieplak, Mechanical stretching of proteins—a theoretical survey of the protein data bank. J. Phys., Condens. Matter 19, 283201 (2007)

    Article  CAS  Google Scholar 

  82. M. Bathe, A finite element framework for computation of protein normal modes and mechanical response. Protein. Struct. Funct. Bioinform. 70(4), 1595–1609 (2007)

    Article  CAS  Google Scholar 

  83. I. Bahar, R.L. Jernigan, Inter-residue potentials in globular proteins and the dominance of highly specific hydrophilic interactions at close separation. J. Mol. Biol. 266(1), 195–214 (1997)

    Article  CAS  Google Scholar 

  84. H.D. Nguyen, C.K. Hall, Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides. Proc. Natl. Acad. Sci. USA 101(46), 16180–16185 (2004)

    Article  CAS  Google Scholar 

  85. H.D. Nguyen, C.K. Hall, Spontaneous fibril formation by polyalanines; discontinuous molecular dynamic simulations. J. Am. Chem. Soc. 128(6), 1890–1901 (2006)

    Article  CAS  Google Scholar 

  86. A. Arkhipov, P.L. Freddolino, K. Imada, K. Namba, K. Schulten, Coarse-grained molecular dynamics simulations of a rotating bacterial flagellum. Biophys. J. 91, 4589–4597 (2006)

    Article  CAS  Google Scholar 

  87. M.J. Buehler, Nature designs tough collagen: explaining the nanostructure of collagen fibrils. Proc. Natl. Acad. Sci. USA 103(33), 12285–12290 (2006)

    Article  CAS  Google Scholar 

  88. M.J. Buehler, Molecular nanomechanics of nascent bone: fibrillar toughening by mineralization. Nanotechnology 18, 295102 (2007)

    Article  CAS  Google Scholar 

  89. S.J. Marrink, H.J. Risselada, S. Yefimov, D.P. Tieleman, A.H. de Vries, The martini force filed: coarse grained model for biomolecular structures. J. Phys. Chem. B 111, 7812–7824 (2007)

    Article  CAS  Google Scholar 

  90. S.J. Marrink, A.H. de Vries, A.E. Mark, Coarse grained model for semiquantitative lipid simulations. J. Phys. Chem. B 108, 750–760 (2004)

    Article  CAS  Google Scholar 

  91. L. Monticelli, S.K. Kandasamy, X. Periole, R.G. Larson, D.P. Tieleman, S.J. Marrink, The martini coarse-grained force field: extension to proteins. J. Chem. Theory Comput. 4, 819–834 (2008)

    Article  CAS  Google Scholar 

  92. J.C. Shelley, M.Y. Shelley, R.C. Reeder, S. Bandyopadhyay, M.L. Klein, A coarse grain model for phospholipid simulations. J. Phys. Chem. B 105, 4464–4470 (2001)

    Article  CAS  Google Scholar 

  93. J.C. Shelley, M.Y. Shelley, R.C. Reeder, S. Bandyopadhyay, P.B. Moore, M.L. Klein, Simulations of phospholipids using a coarse-grain model. J. Phys. Chem. B 105, 9785–9792 (2001)

    Article  CAS  Google Scholar 

  94. S.O. Nielson, C.F. Lopez, G. Srinivas, M.L. Klein, Coarse grain models and the computer simulation of soft materials. J. Phys., Condens. Matter 16, 481–512 (2004)

    Article  CAS  Google Scholar 

  95. M. Venturoli, M.M. Sperotto, M. Kranenburg, B. Smit, Mesoscopic models of biological membranes. Phys. Rep. 437, 1–54 (2006)

    Article  CAS  Google Scholar 

  96. A.B. Liel, C.B. Haselton, G.G. Deierlein, J.W. Baker, Incorporating modeling uncertainties in the assessment of seismic collapse risk of buildings. Struct. Saf. 31, 197–211 (2009)

    Article  Google Scholar 

  97. T. Ackbarow, D. Sen, C. Thaulow, M.J. Buehler, Alpha-helical protein networks are self-protective and flaw-tolerant. PLoS ONE 4(6), e6015 (2009)

    Article  CAS  Google Scholar 

  98. Z. Qin, L. Kreplak, M.J. Buehler, Hierarchical structure controls nanomechanical properties of vimentin intermediate filaments. PLoS ONE 4(10), e7294 (2009)

    Article  CAS  Google Scholar 

  99. M. Neri, C. Anselmi, M. Cascella, A. Maritan, P. Carloni, Coarse-grained model of proteins incorporating atomistic detail of the active site. Phys. Rev. Lett. 95(21), 218102 (2005)

    Article  CAS  Google Scholar 

  100. G. Stefanou, M. Fragiadakis, Nonlinear dynamic analysis of frames with stochastic non-gaussian material properties. Eng. Struct. 31(8), 1841–1850 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Steven W. Cranford & Markus J. Buehler

Authors
  1. Steven W. Cranford
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus J. Buehler
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Cranford, S.W., Buehler, M.J. (2012). Computational Approaches and Simulation. In: Biomateriomics. Springer Series in Materials Science, vol 165. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1611-7_6

Download citation

Publish with us

Policies and ethics

Search

Navigation