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Bibliografias temáticas / Biophysical dynamics / Livros

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Livros sobre o tema "Biophysical dynamics"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 5 de fevereiro de 2022

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1

Trends in biophysics: From cell dynamics toward multicellular growth phenomena. Toronto: Apple Academic Press, 2013.

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2

Kostyukov, Viktor. Molecular mechanics of biopolymers. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1010677.

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The monograph is devoted to molecular mechanics simulations of biologically important polymers like proteins and nucleic acids. It is shown that the algorithms based on the classical laws of motion of Newton, with high-quality parameterization and sufficient computing resources is able to correctly reproduce and predict the structure and dynamics of macromolecules in aqueous solution. Summarized the development path of biopolymers molecular mechanics, its theoretical basis, current status and prospects for further progress.  It may be useful to researchers specializing in molecular Biophysics and molecular biology, as well as students of senior courses of higher educational institutions, studying the biophysical and related areas of training.

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3

Computational hydrodynamics of capsules and biological cells. Boca Raton: Chapman & Hall/CRC, 2010.

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4

Brooks, Charles L. Proteins: A theoretical perspective of dynamics, structure, and thermodynamics. New York: J. Wiley, 1988.

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5

Molecules, dynamics, and life: An introduction to self-organization of matter. New York: Wiley, 1986.

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6

Glass, Leon. Theory of Heart: Biomechanics, Biophysics, and Nonlinear Dynamics of Cardiac Function. New York, NY: Springer New York, 1991.

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7

Nicolis, J. Chaotic dynamics applied to biological information processing. Berlin: Akademie-Verlag, 1987.

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8

Sansom, M. S. P., and Philip Charles Biggin. Molecular simulations and biomembranes: From biophysics to function. Cambridge: Royal Society of Chemistry, 2010.

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9

Inoué, Shinya. Collected works of Shinya Inoué: Microscopes, living cells, and dynamic molecules. Hackensack, NJ: World Scientific, 2008.

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10

J, Eyles Stephen, ed. Mass spectrometry in structural biology and biophysics: Architecture, dynamics, and interaction of biomolecules. 2nd ed. Hoboken, N.J: Wiley, 2012.

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11

University), Canberra International Physics Summer School (22nd 2008 Australian National. Complex physical, biophysical and econophysical systems: Proceedings of the 22nd Canberra International Physics Summer School, the Australian National University, Canberra, 8 - 19 December 2008. Singapore: World Scientific, 2010.

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12

Ciofalo, Michele. Nanoscale fluid dynamics in physiological processes: A review study. Southampton, England: WIT Press, 1999.

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13

Mikhailov, A. S. From cells to societies: Models of complex coherent action. Berlin: Springer, 2002.

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14

NATO Advanced Research Workshop on Complex Dynamics and Biological Evolution (1990 Hindsgavl, Middelfart, Denmark). Complexity, chaos, and biological evolution. New York: Plenum Press, 1991.

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15

Vakulenko, Sergey. Complexity and evolution of dissipative systems: An analytical approach. Berlin: Walter de Gruyter, 2014.

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16

Denny, Mark W. Air and water: The biology and physics of life's media. Princeton, N.J: Princeton University Press, 1993.

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17

Denny, Mark W. Air and water: The biology and physics of life's media. New Jersey: Princeton University Press, 1993.

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18

Layton, Anita T., and Sarah D. Olson. Biological fluid dynamics: Modeling, computations, and applications : AMS Special Session, Biological Fluid Dynamics : Modeling, Computations, and Applications : October 13, 2012, Tulane University, New Orleans, Louisiana. Providence, Rhode Island: American Mathematical Society, 2014.

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19

N, Drabovich Konstantin, Akadėmii͡a︡ navuk Belarusi, and Society of Photo-optical Instrumentation Engineers., eds. ICONO 2001: Nonlinear optical phenomena and Nonlinear dynamics of optical systems : 26 June-1 July 2001, Minsk, Belarus. Bellingham, Washington: SPIE, 2002.

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20

service), SpringerLink (Online, ed. Cochlear Mechanics: Introduction to a Time Domain Analysis of the Nonlinear Cochlea. Boston, MA: Springer US, 2012.

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21

Thiriet, Marc. Intracellular Signaling Mediators in the Circulatory and Ventilatory Systems. New York, NY: Springer New York, 2013.

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22

Doursat, René. Morphogenetic Engineering: Toward Programmable Complex Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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23

Michel, Olivier, Hiroki Sayama, and René Doursat. Morphogenetic engineering: Toward programmable complex systems. Heidelberg: Springer, 2013.

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24

service), SpringerLink (Online, ed. Coarse-Grained Modelling of DNA and DNA Self-Assembly. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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25

1947-, Rittgers Stanley E., and Yoganathan A. P. 1951-, eds. Biofluid mechanics: The human circulation. Boca Raton: CRC/Taylor & Francis, 2007.

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26

Thiriet, Marc. Tissue Functioning and Remodeling in the Circulatory and Ventilatory Systems. New York, NY: Springer New York, 2013.

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27

F, Liebman Joel, and Greenberg Arthur, eds. Biophysical aspects. Deerfield Beach, Fla: VCH, 1987.

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28

Wendling, Fabrice, and Fernando H. Lopes da Silva. Dynamics of EEGs as Signals of Neuronal Populations. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0003.

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This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.

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29

Computational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods. Taylor & Francis Group, 2018.

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30

Computational Approaches to Protein Dynamics: From Quantum to Coarse-Grained Methods. Taylor & Francis Group, 2014.

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31

Sergey, Bezrukov, Society of Photo-optical Instrumentation Engineers., European Optical Society, Società italiana di ottica e fotonica., and SPIE Europe, eds. Noise and fluctuations in biological, biophysical, and biomedical systems: 21-23 May 2007, Florence, Italy. Bellingham, Wash: SPIE, 2007.

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32

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Phenomenological Modelling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0009.

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Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

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33

Methods in Molecular Biophysics: Structure, Dynamics, Function. Cambridge University Press, 2017.

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34

Methods in Molecular Biophysics: Structure, Dynamics, Function. Cambridge University Press, 2007.

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35

W, Alt, Deutsch Andreas 1960-, and Dunn Graham 1944-, eds. Dynamics of cell and tissue motion. Basel: Birkhauser, 1997.

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36

Kaltashov, Igor A., and Stephen J. Eyles. Mass Spectrometry in Biophysics: Conformation and Dynamics of Biomolecules. Wiley & Sons, Incorporated, John, 2005.

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37

Kaltashov, Igor A., and Stephen J. Eyles. Mass Spectrometry in Biophysics: Conformation and Dynamics of Biomolecules. Wiley & Sons, Incorporated, John, 2007.

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38

Kaltashov, Igor A., and Stephen J. Eyles. Mass Spectrometry in Biophysics : Conformation and Dynamics of Biomolecules. Wiley-Interscience, 2005.

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39

Gueron, Shay, and Lisa J. Fauci. Computational Modeling in Biological Fluid Dynamics. Springer, 2011.

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40

Brooks, Charles L., Martin Karplus, and B. Montgomery Pettitt. Proteins: A Theoretical Perspective of Dynamics, Structure, and Thermodynamics. Wiley-Interscience, 1988.

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41

Brooks, Charles L. Proteins: A theoretical perspective of dynamics, structure and thermodynamics. Wiley, 1990.

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42

Brooks, Charles L., B. Montgomery Pettitt, Martin Karplus, Ilya Prigogine, and Stuart A. Rice. Proteins: A Theoretical Perspective of Dynamics, Structure, and Thermodynamics. Wiley & Sons, Incorporated, John, 2009.

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43

1943-, Glass Leon, Hunter Peter 1948-, McCulloch Andrew 1961-, and Institute for Nonlinear Science, eds. Theoryof heart: Biomechanics, biophysics, and nonlinear dynamics of cardiac function. New York: Springer-Verlag, 1991.

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44

1943-, Glass Leon, Hunter Peter 1948-, McCulloch Andrew, and Institute for Nonlinear Science, eds. Theory of heart: Biomechanics, biophysics, and nonlinear dynamics of cardiac function. New York: Springer-Verlag, 1991.

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45

Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

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46

Biodynamics: Why the Wirewalker Doesn't Fall. Wiley-Liss, 2003.

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47

Barrett, T. W. Energy Transfer Dynamics: Studies and Essays in Honor of Herbert Frohlich. Springer-Verlag, 1987.

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48

Vogel, Steven. Life in Moving Fluids. HarperCollins Publishers Inc, 1998.

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49

1952-, Cheer A. Y., Van Dam, C. P. 1954-, and AMS-IMS-SIAM Joint Summer Research Conference on Biofluiddynamics (1991 : University of Washington), eds. Fluid dynamics in biology: Proceedings of an AMS-IMS-SIAM Joint Summer Research Conference held July 6-12, 1991 with support from the National Science Foundation and NASA Headquarters. Providence, RI: American Mathematical Society, 1993.

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50

Vogel, Steven. Life in Moving Fluids. 2nd ed. Princeton University Press, 1996.

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