Статті в журналах: "Biophysical dynamics"

1

Berendsen, H. J. C. "Biophysical applications of molecular dynamics." Computer Physics Communications 44, no. 3 (June 1987): 233–42. http://dx.doi.org/10.1016/0010-4655(87)90078-6.

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2

Nelson, David R. "Biophysical Dynamics in Disorderly Environments." Annual Review of Biophysics 41, no. 1 (June 9, 2012): 371–402. http://dx.doi.org/10.1146/annurev-biophys-042910-155236.

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3

Abarbanel, Henry D. I., Leif Gibb, R. Huerta, and M. I. Rabinovich. "Biophysical model of synaptic plasticity dynamics." Biological Cybernetics 89, no. 3 (September 1, 2003): 214–26. http://dx.doi.org/10.1007/s00422-003-0422-x.

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4

Sataric, M. V., and J. A. Tuszynski. "Nonlinear Dynamics of Microtubules: Biophysical Implications." Journal of Biological Physics 31, no. 3-4 (December 2005): 487–500. http://dx.doi.org/10.1007/s10867-005-7288-1.

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5

Su, Qian Peter, and Lining Arnold Ju. "Biophysical nanotools for single-molecule dynamics." Biophysical Reviews 10, no. 5 (August 18, 2018): 1349–57. http://dx.doi.org/10.1007/s12551-018-0447-y.

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6

Fernandez, Fernando R., Jordan D. T. Engbers, and Ray W. Turner. "Firing Dynamics of Cerebellar Purkinje Cells." Journal of Neurophysiology 98, no. 1 (July 2007): 278–94. http://dx.doi.org/10.1152/jn.00306.2007.

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Knowledge of intrinsic neuronal firing dynamics is a critical first step to establishing an accurate biophysical model of any neuron. In this study we examined cerebellar Purkinje cells to determine the bifurcations likely to underlie firing dynamics within a biophysically realistic and experimentally supported model. We show that Purkinje cell dynamics are consistent with a system undergoing a saddle-node bifurcation of fixed points in the transition from rest to firing and a saddle homoclinic bifurcation from firing to rest. Our analyses account for numerous observed Purkinje cell firing properties that include bistability, plateau potentials, specific aspects of the frequency–current ( F– I) relationship, first spike latency, and the ability for climbing fiber input to induce state transitions in the bistable regime. We also experimentally confirm new properties predicted from our model and analysis that include the presence of a depolarizing afterpotential (DAP), the ability to fire at low frequencies (<50 Hz) and with a high gain in the F– I relationship, and a bistable region limited to low-frequency firing. Purkinje cell dynamics, including bistability, prove to arise from numerous biophysical factors that include the DAP, fast refractory dynamics, and a long membrane time constant. A hyperpolarizing activated cation current ( IH) is shown not to be directly involved in establishing bistable dynamics but rather reduces the range for bistability. A combined electrophysiological and modeling approach thus accounts for several properties of Purkinje cells, providing a firm basis from which to assess Purkinje cell output patterns.

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7

Flomenbom, Ophir. "Single File Dynamics Advances with a Focus on Biophysical Relevance." Biophysical Reviews and Letters 09, no. 04 (December 2014): 307–31. http://dx.doi.org/10.1142/s1793048014400013.

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In this review (appearing in the Special Issue on single file dynamics in biophysics and related extensions), three recently treated variants in file dynamics are presented: files with density that is not fixed, files with heterogeneous particles, and files with slow particles. The results in these files include:• In files with a density law that is not fixed, but decays as a power law with an exponent a the distance from the origin, the particle in the origin mean square displacement (MSD) scales like MSD ~ t[1+a]/2, with a Gaussian probability density function (PDF). This extends the scaling, MSD ~ t1/2, seen in a constant density file.• When, in addition, the particles' diffusion coefficients are distributed like a power law with an exponent γ (around the origin), the MSD follows MSD ~ t[1-γ]/[2/(1+a) - γ], with a Gaussian PDF.• In anomalous files that are renewal, namely, when all particles attempt a jump together, yet, with jump times taken from a PDF that decays as a power law with an exponent -1 - ε, ψ(t) ~ t-1-ε, the MSD scales like the MSD of the corresponding normal file, in the power ε.• In anomalous files of independent particles, the MSD is very slow and scales like MSD ~ log2(t). Even more exciting, the particles form clusters, defining a dynamical phase transition: depending on the anomaly power ε, the percentage of particles in clusters ξ follows [Formula: see text], yet when ε > 1, fluidity rather than clusters is seen.We talk about utilizing these results while focusing on biophysical processes and applications: dynamics in channels, membranes, biosensors, etc.[Formula: see text] Special Issue Comments: In this article, results about recently suggested variants in single file dynamics appear: heterogeneous files and slow files, yet also, the relevance with biophysical processes. It is related to the Special Issue articles about expansions in files,61files with force,62and the zig zag occurrences in files.63

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8

Sikosek, Tobias, and Hue Sun Chan. "Biophysics of protein evolution and evolutionary protein biophysics." Journal of The Royal Society Interface 11, no. 100 (November 6, 2014): 20140419. http://dx.doi.org/10.1098/rsif.2014.0419.

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The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

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9

Tortora, Maxime MC, Hossein Salari, and Daniel Jost. "Chromosome dynamics during interphase: a biophysical perspective." Current Opinion in Genetics & Development 61 (April 2020): 37–43. http://dx.doi.org/10.1016/j.gde.2020.03.001.

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10

Chiu, Wah, and Keith Moffat. "Biophysical methods: structure, dynamics and gorgeous images." Current Opinion in Structural Biology 17, no. 5 (October 2007): 546–48. http://dx.doi.org/10.1016/j.sbi.2007.09.008.

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11

Miller, T. F., M. Eleftheriou, P. Pattnaik, A. Ndirango, D. Newns, and G. J. Martyna. "Symplectic quaternion scheme for biophysical molecular dynamics." Journal of Chemical Physics 116, no. 20 (May 22, 2002): 8649–59. http://dx.doi.org/10.1063/1.1473654.

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12

Nagel, Katherine I., and Rachel I. Wilson. "Biophysical mechanisms underlying olfactory receptor neuron dynamics." Nature Neuroscience 14, no. 2 (January 9, 2011): 208–16. http://dx.doi.org/10.1038/nn.2725.

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13

Munro, James B., and Kelly K. Lee. "Probing Structural Variation and Dynamics in the HIV-1 Env Fusion Glycoprotein." Current HIV Research 16, no. 1 (April 19, 2018): 5–12. http://dx.doi.org/10.2174/1570162x16666171222110025.

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Background: Recent advances in structural characterization of the HIV envelope glycoprotein (Env) have provided a high-resolution glimpse of the architecture of this target for neutralizing antibodies and the machinery responsible for mediating receptor binding and membrane fusion. These structures primarily capture the detailed organization of the receptor-naive, prefusion conformation of Env, but under native solution conditions Env is highly dynamic, sampling multiple conformational states as well as exhibiting local protein flexibility. Methods: Special emphasis is placed on the use of biophysical methods, including single-molecule fluorescence microscopy and hydrogen/deuterium-exchange mass spectrometry. Results: Using novel biophysical approaches, striking isolate-specific differences in Env’s dynamic profile have been revealed that appear to underlie phenotypic differences of the viral isolates such as neutralization sensitivity and CD4 receptor reactivity. Conclusion: Structural studies are complemented by novel biophysical investigations that enable visualization of the dynamics of HIV-1 Env under native conditions. These approaches will also enable us to gain new insights into the mechanisms of action of antibodies and drugs.

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14

Tsegaye, Solomon, Gobena Dedefo, and Mohammed Mehdi. "Biophysical applications in structural and molecular biology." Biological Chemistry 402, no. 10 (July 7, 2021): 1155–77. http://dx.doi.org/10.1515/hsz-2021-0232.

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Abstract The main objective of structural biology is to model proteins and other biological macromolecules and link the structural information to function and dynamics. The biological functions of protein molecules and nucleic acids are inherently dependent on their conformational dynamics. Imaging of individual molecules and their dynamic characteristics is an ample source of knowledge that brings new insights about mechanisms of action. The atomic-resolution structural information on most of the biomolecules has been solved by biophysical techniques; either by X-ray diffraction in single crystals or by nuclear magnetic resonance (NMR) spectroscopy in solution. Cryo-electron microscopy (cryo-EM) is emerging as a new tool for analysis of a larger macromolecule that couldn’t be solved by X-ray crystallography or NMR. Now a day’s low-resolution Cryo-EM is used in combination with either X-ray crystallography or NMR. The present review intends to provide updated information on applications like X-ray crystallography, cryo-EM and NMR which can be used independently and/or together in solving structures of biological macromolecules for our full comprehension of their biological mechanisms.

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15

Van Dyke, Chris. "Boxing daze – using state-and-transition models to explore the evolution of socio-biophysical landscapes." Progress in Physical Geography: Earth and Environment 39, no. 5 (May 17, 2015): 594–621. http://dx.doi.org/10.1177/0309133315581700.

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Critical physical geography (CPG) proposes to bridge the lingering gap between human and physical geographers. To rejuvenate conversations among different corners of the discipline about the possibility of trans-disciplinary collaboration, CPG must provide unique epistemological, methodological, and conceptual frameworks that human and physical geographers alike will find appealing, relevant, and timely. These should help them perceptively characterize, narrate, and anticipate changes in socio-biophysical landscapes. This paper outlines a conceptual framework that can be harnessed in future CPG studies and reflects on what it means to be a critical geographer. To solve the epistemological dilemmas confronting CPG, this paper demonstrates that state-and-transition models (STMs) can provide a unifying framework to address questions about socio-biophysical landscape evolution. Originally developed to account for nonlinear dynamics in rangeland ecosystems, STMs have been used to analyze a variety of ecological, geomorphic, and hydrological transitions in complex biophysical landscapes. STMs have epistemological commonalities with explanatory frameworks pioneered by political ecologists, and while thus far they have been used to account for complex biophysical dynamics, they can be expanded to accommodate critical investigations of the social dynamics underpinning landscape change. By foregrounding the transitional dynamics of socio-biophysical landscape – a theme that has interested physical and critical human geographers – STMs establish a conceptual space in which to holistically interpret the interacting drivers that underwrite socio-biophysical landscape change.

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16

Spill, Fabian, and Muhammad H. Zaman. "Multiscale dynamics of the biophysical and biochemical microenvironment." Physics of Life Reviews 22-23 (December 2017): 127–29. http://dx.doi.org/10.1016/j.plrev.2017.07.004.

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17

Wang, Jun, Daniel Breen, Abraham Akinin, Frederic Broccard, Henry D. I. Abarbanel, and Gert Cauwenberghs. "Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI." IEEE Transactions on Biomedical Circuits and Systems 11, no. 6 (December 2017): 1258–70. http://dx.doi.org/10.1109/tbcas.2017.2776198.

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18

Forzieri, Giovanni, and Filippo Catani. "Scale-dependent relations in land cover biophysical dynamics." Ecological Modelling 222, no. 17 (September 2011): 3285–90. http://dx.doi.org/10.1016/j.ecolmodel.2011.06.010.

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19

Molines, Arthur T., Joel Lemiere, Claire H. Edrington, Chieh-Ting Hsu, Ida E. Steinmark, Klaus Suhling, Gohta Goshima, Liam J. Holt, Gary Brouhard, and Fred Chang. "Cytoplasm Biophysical Properties Limit Cytoskeleton Dynamics In Vivo." Biophysical Journal 120, no. 3 (February 2021): 347a. http://dx.doi.org/10.1016/j.bpj.2020.11.2159.

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20

Sahu, Indra D., and Gary A. Lorigan. "Probing Structural Dynamics of Membrane Proteins Using Electron Paramagnetic Resonance Spectroscopic Techniques." Biophysica 1, no. 2 (March 30, 2021): 106–25. http://dx.doi.org/10.3390/biophysica1020009.

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Membrane proteins are essential for the survival of living organisms. They are involved in important biological functions including transportation of ions and molecules across the cell membrane and triggering the signaling pathways. They are targets of more than half of the modern medical drugs. Despite their biological significance, information about the structural dynamics of membrane proteins is lagging when compared to that of globular proteins. The major challenges with these systems are low expression yields and lack of appropriate solubilizing medium required for biophysical techniques. Electron paramagnetic resonance (EPR) spectroscopy coupled with site directed spin labeling (SDSL) is a rapidly growing powerful biophysical technique that can be used to obtain pertinent structural and dynamic information on membrane proteins. In this brief review, we will focus on the overview of the widely used EPR approaches and their emerging applications to answer structural and conformational dynamics related questions on important membrane protein systems.

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21

Sahu, Indra D., and Gary A. Lorigan. "Electron Paramagnetic Resonance as a Tool for Studying Membrane Proteins." Biomolecules 10, no. 5 (May 13, 2020): 763. http://dx.doi.org/10.3390/biom10050763.

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Membrane proteins possess a variety of functions essential to the survival of organisms. However, due to their inherent hydrophobic nature, it is extremely difficult to probe the structure and dynamic properties of membrane proteins using traditional biophysical techniques, particularly in their native environments. Electron paramagnetic resonance (EPR) spectroscopy in combination with site-directed spin labeling (SDSL) is a very powerful and rapidly growing biophysical technique to study pertinent structural and dynamic properties of membrane proteins with no size restrictions. In this review, we will briefly discuss the most commonly used EPR techniques and their recent applications for answering structure and conformational dynamics related questions of important membrane protein systems.

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22

Skinner, F. K., J. Y. J. Chung, I. Ncube, P. A. Murray, and S. A. Campbell. "Using Heterogeneity to Predict Inhibitory Network Model Characteristics." Journal of Neurophysiology 93, no. 4 (April 2005): 1898–907. http://dx.doi.org/10.1152/jn.00619.2004.

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From modeling studies it has been known for >10 years that purely inhibitory networks can produce synchronous output given appropriate balances of intrinsic and synaptic parameters. Several experimental studies indicate that synchronous activity produced by inhibitory networks is critical to the production of population rhythms associated with various behavioral states. Heterogeneity of inputs to inhibitory networks strongly affect their ability to synchronize. In this paper, we explore how the amount of input heterogeneity to two-cell inhibitory networks affects their dynamics. Using numerical simulations and bifurcation analyses, we find that the ability of inhibitory networks to synchronize in the face of heterogeneity depends nonmonotonically on each of the synaptic time constant, synaptic conductance and external drive parameters. Because of this, an optimal set of parameters for a given cellular model with various biophysical characteristics can be determined. We suggest that this could be a helpful approach to use in determining the importance of different, underlying biophysical details. We further find that two-cell coherence properties are maintained in larger 10-cell networks. As such, we think that a strategy of “embedding” small network dynamics in larger networks is a useful way to understand the contribution of biophysically derived parameters to population dynamics in large networks.

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23

Chignola, Roberto, Michela Sega, Sabrina Stella, Vladislav Vyshemirsky, and Edoardo Milotti. "From Single-Cell Dynamics to Scaling Laws in Oncology." Biophysical Reviews and Letters 09, no. 03 (September 2014): 273–84. http://dx.doi.org/10.1142/s1793048014300035.

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We are developing a biophysical model of tumor biology. We follow a strictly quantitative approach where each step of model development is validated by comparing simulation outputs with experimental data. While this strategy may slow down our advancements, at the same time it provides an invaluable reward: we can trust simulation outputs and use the model to explore territories of cancer biology where current experimental techniques fail. Here, we review our multi-scale biophysical modeling approach and show how a description of cancer at the cellular level has led us to general laws obeyed by both in vitro and in vivo tumors.

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24

Duarte, Jorge, Luís Silva, and J. Sousa Ramos. "Computation of the topological entropy in chaotic biophysical bursting models for excitable cells." Discrete Dynamics in Nature and Society 2006 (2006): 1–18. http://dx.doi.org/10.1155/ddns/2006/60918.

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One of the interesting complex behaviors in many cell membranes is bursting, in which a rapid oscillatory state alternates with phases of relative quiescence. Although there is an elegant interpretation of many experimental results in terms of nonlinear dynamical systems, the dynamics of bursting models is not completely described. In the present paper, we study the dynamical behavior of two specific three-variable models from the literature that replicate chaotic bursting. With results from symbolic dynamics, we characterize the topological entropy of one-dimensional maps that describe the salient dynamics on the attractors. The analysis of the variation of this important numerical invariant with the parameters of the systems allows us to quantify the complexity of the phenomenon and to distinguish different chaotic scenarios. This work provides an example of how our understanding of physiological models can be enhanced by the theory of dynamical systems.

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25

Warshel, Arieh, and William W. Parson. "Dynamics of biochemical and biophysical reactions: insight from computer simulations." Quarterly Reviews of Biophysics 34, no. 4 (November 2001): 563–679. http://dx.doi.org/10.1017/s0033583501003730.

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1. Introduction 5632. Obtaining rate constants from molecular-dynamics simulations 5642.1 General relationships between quantum electronic structures and reaction rates 5642.2 The transition-state theory (TST) 5692.3 The transmission coefficient 5723. Simulating biological electron-transfer reactions 5753.1 Semi-classical surface-hopping and the Marcus equation 5753.2 Treating quantum mechanical nuclear tunneling by the dispersed-polaron/spin-boson method 5803.3 Density-matrix treatments 5833.4 Charge separation in photosynthetic bacterial reaction centers 5844. Light-induced photoisomerizations in rhodopsin and bacteriorhodopsin 5965. Energetics and dynamics of enzyme reactions 6145.1 The empirical-valence-bond treatment and free-energy perturbation methods 6145.2 Activation energies are decreased in enzymes relative to solution, often by electrostatic effects that stabilize the transition state 6205.3 Entropic effects in enzyme catalysis 6275.4 What is meant by dynamical contributions to catalysis? 6345.5 Transmission coefficients are similar for corresponding reactions in enzymes and water 6365.6 Non-equilibrium solvation effects contribute to catalysis mainly through Δg[Dagger], not the transmission coefficient 6415.7 Vibrationally assisted nuclear tunneling in enzyme catalysis 6485.8 Diffusive processes in enzyme reactions and transmembrane channels 6516. Concluding remarks 6587. Acknowledgements 6588. References 658Obtaining a detailed understanding of the dynamics of a biochemical reaction is a formidable challenge. Indeed, it might appear at first sight that reactions in proteins are too complex to analyze microscopically. At room temperature, even a relatively small protein can have as many as 1034 accessible conformational states (Dill, 1985). In many cases, however, we have detailed structural information about the active site of an enzyme, whereas such information is missing for corresponding chemical systems in solution. The atomic coordinates of the chromophore in bacteriorhodopsin, for example, are known to a resolution of 1–2 Å. In addition, experimental studies of biological processes such as photoisomerization and electron transfer have provided a wealth of detailed information that eventually may make some of these processes classical problems in chemical physics as well as biology.

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26

Wang, Lili, Marco A. Allodi, and Gregory S. Engel. "Quantum coherences reveal excited-state dynamics in biophysical systems." Nature Reviews Chemistry 3, no. 8 (June 24, 2019): 477–90. http://dx.doi.org/10.1038/s41570-019-0109-z.

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27

Gerken, Thomas A. "Biophysical Approaches to Salivary Mucin Structure, Conformation and Dynamics." Critical Reviews in Oral Biology & Medicine 4, no. 3 (April 1993): 261–70. http://dx.doi.org/10.1177/10454411930040030201.

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Our understanding of the origins of the physical and biochemical properties of mucous glycoproteins is incomplete and not with out controversy. Recent molecular biological and biophysical studies revealing the architecture and solution structure and dynamics of a series of salivary mucins, invaluable toward resolving many of these questions, are discussed. Mucins are very large, structurally heterogeneous, and highly expanded molecules with the carbohydrate playing a key role in maintaining the extended mucin conformation.

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28

Saengpayab, Yaowapa, Pisan Kanthang, Stefan Schreier, Charin Modchang, Narin Nuttavut, Darapond Triampo, and Wannapong Triampo. "Biophysical approach to investigate temperature effects on protein dynamics." European Physical Journal Applied Physics 71, no. 3 (August 2015): 31201. http://dx.doi.org/10.1051/epjap/2015150180.

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29

Olson, Donald B. "Biophysical dynamics of western transition zones: a preliminary synthesis." Fisheries Oceanography 10, no. 2 (June 2001): 133–50. http://dx.doi.org/10.1046/j.1365-2419.2001.00161.x.

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30

Saini, Anuj, and Lydia Kisley. "Fluorescence microscopy of biophysical protein dynamics in nanoporous hydrogels." Journal of Applied Physics 126, no. 8 (August 28, 2019): 081101. http://dx.doi.org/10.1063/1.5110299.

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31

MORSHED, B. I., M. SHAMS, and T. MUSSIVAND. "DERIVING AN ELECTRIC CIRCUIT EQUIVALENT MODEL OF CELL MEMBRANE PORES IN ELECTROPORATION." Biophysical Reviews and Letters 08, no. 01n02 (June 2013): 21–32. http://dx.doi.org/10.1142/s1793048012500099.

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Electroporation is the formation of reversible pores in cell membranes under a brief pulse of high electric field. Dynamics of pore formation during electroporation suggests that the transmembrane potential would settle approximately at the threshold transmembrane potential and the transmembrane resistance would decrease significantly from the state of relaxation. The current electric circuit equivalent models for electroporation containing time-invariant, static and passive components are unable to capture the pore dynamics. A biophysically-inspired electric circuit equivalent model containing dynamic components for membrane pores has been derived using biological parameters. The model contains a voltage-controlled resistor driven by a two-stage cascaded integrator that is activated through a voltage-gated switch. Simulation results with the derived model showed higher accuracy compared to a commonly used model, where the transmembrane resistance decreased million-fold at the onset of electroporation and the transmembrane potential settled at 99.5% of the critical transmembrane potential, thus enabling improved dynamic behavior modeling ability of the pores in electroporation. The derived model allows fast and reliable analysis of this biophysical phenomenon and potentially aids in optimization of various parameters involved in electroporation.

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32

Sikora, Mateusz, Utz H. Ermel, Anna Seybold, Michael Kunz, Giulia Calloni, Julian Reitz, R. Martin Vabulas, Gerhard Hummer, and Achilleas S. Frangakis. "Desmosome architecture derived from molecular dynamics simulations and cryo-electron tomography." Proceedings of the National Academy of Sciences 117, no. 44 (October 16, 2020): 27132–40. http://dx.doi.org/10.1073/pnas.2004563117.

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Desmosomes are cell–cell junctions that link tissue cells experiencing intense mechanical stress. Although the structure of the desmosomal cadherins is known, the desmosome architecture—which is essential for mediating numerous functions—remains elusive. Here, we recorded cryo-electron tomograms (cryo-ET) in which individual cadherins can be discerned; they appear variable in shape, spacing, and tilt with respect to the membrane. The resulting sub-tomogram average reaches a resolution of ∼26 Å, limited by the inherent flexibility of desmosomes. To address this challenge typical of dynamic biological assemblies, we combine sub-tomogram averaging with atomistic molecular dynamics (MD) simulations. We generate models of possible cadherin arrangements and perform an in silico screening according to biophysical and structural properties extracted from MD simulation trajectories. We find a truss-like arrangement of cadherins that resembles the characteristic footprint seen in the electron micrograph. The resulting model of the desmosomal architecture explains their unique biophysical properties and strength.

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Brown, Kathryn, and Andrew Hansen. "A Landscape Approach to Aspen Restoration: Understanding the Role of Biophysical Setting in Aspen Community Dynamics." UW National Parks Service Research Station Annual Reports 25 (January 1, 2001): 135–39. http://dx.doi.org/10.13001/uwnpsrc.2001.3479.

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The aim of this study is to better understand the relationship of biotic and abiotic variables to the distribution, performance, and rates of loss of aspen in the Greater Yellowstone Ecosystem. Aspen commumtles, though critically important for maintaining biodiversity, soil quality, and nutrient cycling, are declining rapidly in the Northern Rockies. Fire suppression, elk browsing, and climatic change are the most widely advanced explanations for this widespread loss of aspen. The role of biophysical factors (e.g. topography, climate, soils, and competing vegetation) in determining aspen performance, however, is poorly understood. Knowledge of these relationships may provide a basis for tailoring aspen restoration efforts to specific landscape settings. To better understand the influence of biophysical variables on aspen dynamics, this study addresses three hypotheses: 1. The aerial distribution of aspen is not random across the landscape and varies as a function of biophysical setting. 2. Within its distribution, growth rates and productivity of aspen stands differ relative to biotic and abiotic variables. 3. Rates of aspen loss in the landscape differ relative to biophysical setting. Here we report progress on the first year of the two-year study.

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34

McPEAK, JOHN G., DAVID R. LEE, and CHRISTOPHER B. BARRETT. "Introduction: The dynamics of coupled human and natural systems." Environment and Development Economics 11, no. 1 (January 30, 2006): 9–13. http://dx.doi.org/10.1017/s1355770x05002664.

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This essay introduces a special section of this issue containing a set of papers on the dynamics of coupled human and natural systems. We frame this introduction by setting out some of the major issues confronting researchers who wish to incorporate both economic and biophysical dynamics in their analysis. We contrast the three papers contained in this section in terms of how they respond to these different issues. We conclude that these papers provide important new insights on both how to model and analyze dynamic coupled human and natural systems and how to define policies that will lead to improved human well being and environmental conditions.

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35

Coombes, Stephen, Brent Doiron, Krešimir Josić, and Eric Shea-Brown. "Towards blueprints for network architecture, biophysical dynamics and signal transduction." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, no. 1849 (October 20, 2006): 3301–18. http://dx.doi.org/10.1098/rsta.2006.1903.

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We review mathematical aspects of biophysical dynamics , signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

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36

Yamamori, Yu, Kazuhiro Takemura, and Akio Kitao. "1PT174 Molecular Dynamics Simulation of Protein Using Robot Dynamics Algorithm(The 50th Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 52, supplement (2012): S98. http://dx.doi.org/10.2142/biophys.52.s98_5.

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37

Lychko, V. "BIOPHYSICAL MARKERS OF ISCHEMIC STROKE." Eastern Ukrainian Medical Journal 8, no. 3 (2020): 334–38. http://dx.doi.org/10.21272/eumj.2020;8(3):334-338.

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An important and influential factor that directly affects the severity of ischemic stroke (IS) and determines its outcome is the functional state of the membrane-receptor complex (MRC) of cells. One of the most important criteria for assessing this parameter is the β‑adrenergic activity of cytoplasmic membranes (β‑ARM), which plays a leading role in the pathogenesis of IS. The article presents the results of a comprehensive study of the peculiarities of changes in the structural and functional characteristics of brain tissue and β‑adrenoceptors in the acute period of IS to optimize diagnosis. Measurement of changes in the osmotic resistance of erythrocytes (ORE) under the action of β‑blockers was determined by photoelectron colorimetry. The work was based on the materials of a comprehensive examination of 350 patients with the new-onset IS on the 1st, 10th and 21st day of the disease. The severity of the condition and the degree of neurological deficit were objectified using the stroke scale of the National Institutes of Health (NIHSS) with a score in the first hours of the disease, in the dynamics of treatment on the 10th and 21st day. All patients were divided into 2 clinical groups: 1st (n = 183) – patients in moderate severity condition (mean score on the NIHSS scale 11.74 ± 0.33); 2nd (n = 167) – patients in severe condition (mean score on the NIHSS scale 24.06 ± 0.29). As a result of the study, the indicators of β‑ARM of the control group were within normal limits (15.3 ± 4.4 SU), which corresponds to normal β‑ARM. In patients with moderate IS, the indicator exceeded the control values by 1.97 times, which is typical for the average degree of β‑ARM (21–40 SU), and in severe – 2.8 times and was characterized by a low degree of β‑ARM (> 41 SU). An unfavorable sign for the prognosis of the acute period of IS was represented by a further increase in the levels of β‑ARM in the dynamics, which was observed in clinically severe patients. Keywords adrenergic activity, ischemia, erythrocyte, receptor.

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38

Yakushevich, L. V. "Nonlinear dynamics of biopolymers: theoretical models, experimental data." Quarterly Reviews of Biophysics 26, no. 2 (May 1993): 201–23. http://dx.doi.org/10.1017/s0033583500004078.

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Nonlinear dynamics of biopolymers is a new and rapidly developing field of biophysical science. It can be considered as a part of the general dynamics which deals with the internal mobility of biopolymers. Theoreticians define it also as the next (anharmonic or nonlinear) approximation after the first harmonic or linear one.

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39

Silveira, Célia M., María A. Castro, Joana M. Dantas, Carlos Salgueiro, Daniel H. Murgida, and Smilja Todorovic. "Structure, electrocatalysis and dynamics of immobilized cytochrome PccH and its microperoxidase." Physical Chemistry Chemical Physics 19, no. 13 (2017): 8908–18. http://dx.doi.org/10.1039/c6cp08361g.

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40

Drüke, Markus, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke. "CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model." Geoscientific Model Development 14, no. 6 (July 1, 2021): 4117–41. http://dx.doi.org/10.5194/gmd-14-4117-2021.

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Abstract. The terrestrial biosphere is exposed to land-use and climate change, which not only affects vegetation dynamics but also changes land–atmosphere feedbacks. Specifically, changes in land cover affect biophysical feedbacks of water and energy, thereby contributing to climate change. In this study, we couple the well-established and comprehensively validated dynamic global vegetation model LPJmL5 (Lund–Potsdam–Jena managed Land) to the coupled climate model CM2Mc, the latter of which is based on the atmosphere model AM2 and the ocean model MOM5 (Modular Ocean Model 5), and name it CM2Mc-LPJmL. In CM2Mc, we replace the simple land-surface model LaD (Land Dynamics; where vegetation is static and prescribed) with LPJmL5, and we fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. These include a sub-daily cycle for calculating energy and water fluxes, conductance of the soil evaporation and plant interception, canopy-layer humidity, and the surface energy balance in order to calculate the surface and canopy-layer temperature within LPJmL5. Exchanging LaD with LPJmL5 and, therefore, switching from a static and prescribed vegetation to a dynamic vegetation allows us to model important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the impacts of managed land (crop growth and irrigation). Our results show that CM2Mc-LPJmL has similar temperature and precipitation biases to the original CM2Mc model with LaD. The performance of LPJmL5 in the coupled system compared to Earth observation data and to LPJmL offline simulation results is within acceptable error margins. The historical global mean temperature evolution of our model setup is within the range of CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The comparison of model runs with and without land-use change shows a partially warmer and drier climate state across the global land surface. CM2Mc-LPJmL opens new opportunities to investigate important biophysical vegetation–climate feedbacks with a state-of-the-art and process-based dynamic vegetation model.

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41

AHN, Kang-Hun. "Biophysical Mechanism of Hearing: From Clinical Studies to Molecular Dynamics." Physics and High Technology 25, no. 10 (October 31, 2016): 13–17. http://dx.doi.org/10.3938/phit.25.051.

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42

Ribeiro-Oliveira, J. P., M. A. Ranal, and M. A. Boselli. "Water Dynamics on Germinating Diaspores: Physiological Perspectives from Biophysical Measurements." Plant Phenomics 2020 (December 6, 2020): 1–16. http://dx.doi.org/10.34133/2020/5196176.

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We demonstrated that classical biophysical measurements of water dynamics on germinating diaspores (seeds and other dispersal units) can improve the understanding of the germination process in a simpler, safer, and newer way. This was done using diaspores of cultivated species as a biological model. To calculate the water dynamics measurements (weighted mass, initial diffusion coefficient, velocity, and acceleration), we used the mass of diaspores recorded over germination time. Weighted mass of germinating diaspores has a similar pattern, independent of the physiological quality, species, or genetic improvement degree. However, the initial diffusion coefficient (related to imbibition per se), velocity, and acceleration (related to the whole germination metabolism) are influenced by species characters, highlighting the degree of genetic improvement and physiological quality. Changes in the inflection of velocity curves demonstrated each phase of germination sensu stricto. There is no pattern related to the number of these phases, which could range between three and six. Regression models can demonstrate initial velocity and velocity increments for each phase, giving an idea of the management of germinative metabolism. Our finds demonstrated that germination is a polyphasic process with a species-specific pattern but still set by the degree of genetic improvement and (or) physiological quality of diaspores. Among the biophysical measurements, velocity has the greatest potential to define the germination metabolism.

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43

Chen, Xiaoli, and Jinqiao Duan. "Nonlocal Dynamics for Non-Gaussian Systems Arising in Biophysical Modeling." Communications on Applied Mathematics and Computation 2, no. 2 (September 23, 2019): 201–13. http://dx.doi.org/10.1007/s42967-019-00046-5.

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44

Porat, N., D. Gill, and A. H. Parola. "Adenosine deaminase in cell transformation. Biophysical manifestation of membrane dynamics." Journal of Biological Chemistry 263, no. 29 (October 1988): 14608–11. http://dx.doi.org/10.1016/s0021-9258(18)68077-9.

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45

Zdravković, S., M. Satarić, and J. Tuszyński. "Biophysical Implications of the Peyrard-BishopDauxois Model of DNA Dynamics." Journal of Computational and Theoretical Nanoscience 1, no. 2 (September 1, 2004): 169–79. http://dx.doi.org/10.1166/jctn.2004.013.

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46

Lin, Congping, Yiwei Zhang, Imogen Sparkes, and Peter Ashwin. "Structure and Dynamics of ER: Minimal Networks and Biophysical Constraints." Biophysical Journal 107, no. 3 (August 2014): 763–72. http://dx.doi.org/10.1016/j.bpj.2014.06.032.

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47

Jussupow, Alexander, Ana C. Messias, Ralf Stehle, Arie Geerlof, Sara M. Ø. Solbak, Cristina Paissoni, Anders Bach, Michael Sattler, and Carlo Camilloni. "The dynamics of linear polyubiquitin." Science Advances 6, no. 42 (October 2020): eabc3786. http://dx.doi.org/10.1126/sciadv.abc3786.

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Polyubiquitin chains are flexible multidomain proteins, whose conformational dynamics enable them to regulate multiple biological pathways. Their dynamic is determined by the linkage between ubiquitins and by the number of ubiquitin units. Characterizing polyubiquitin behavior as a function of their length is hampered because of increasing system size and conformational variability. Here, we introduce a new approach to efficiently integrating small-angle x-ray scattering with simulations allowing us to accurately characterize the dynamics of linear di-, tri-, and tetraubiquitin in the free state as well as of diubiquitin in complex with NEMO, a central regulator in the NF-κB pathway. Our results show that the behavior of the diubiquitin subunits is independent of the presence of additional ubiquitin modules and that the dynamics of polyubiquitins with different lengths follow a simple model. Together with experimental data from multiple biophysical techniques, we then rationalize the 2:1 NEMO:polyubiquitin binding.

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48

Hinrichsen, Hans-Harald, Mark Dickey-Collas, Martin Huret, Myron A. Peck, and Frode B. Vikebø. "Evaluating the suitability of coupled biophysical models for fishery management." ICES Journal of Marine Science 68, no. 7 (April 21, 2011): 1478–87. http://dx.doi.org/10.1093/icesjms/fsr056.

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Abstract Hinrichsen, H-H., Dickey-Collas, M., Huret, M., Peck, M. A., and Vikebø, F. B. 2011. Evaluating the suitability of coupled biophysical models for fishery management. – ICES Journal of Marine Science, 68: 1478–1487. The potential role of coupled biophysical models in enhancing the conservation, management, and recovery of fish stocks is assessed, with emphasis on anchovy, cod, herring, and sprat in European waters. The assessment indicates that coupled biophysical models are currently capable of simulating transport patterns, along with temperature and prey fields within marine ecosystems; they therefore provide insight into the variability of early-life-stage dynamics and connectivity within stocks. Moreover, the influence of environmental variability on potential recruitment success may be discerned from model hindcasts. Based on case studies, biophysical modelling results are shown to be capable of shedding light on whether stock management frameworks need re-evaluation. Hence, key modelling products were identified that will contribute to the development of viable stock recovery plans and management strategies. The study also suggests that approaches combining observation, process knowledge, and numerical modelling could be a promising way forward in understanding and simulating the dynamics of marine fish populations.

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King, Michael R., Kevin G. Phillips, Annachiara Mitrugno, Tae-Rin Lee, Adelaide M. E. de Guillebon, Siddarth Chandrasekaran, Matthew J. McGuire, et al. "A physical sciences network characterization of circulating tumor cell aggregate transport." American Journal of Physiology-Cell Physiology 308, no. 10 (May 15, 2015): C792—C802. http://dx.doi.org/10.1152/ajpcell.00346.2014.

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Circulating tumor cells (CTC) have been implicated in the hematogenous spread of cancer. To investigate the fluid phase of cancer from a physical sciences perspective, the multi-institutional Physical Sciences-Oncology Center (PS-OC) Network performed multidisciplinary biophysical studies of single CTC and CTC aggregates from a patient with breast cancer. CTCs, ranging from single cells to aggregates comprised of 2–5 cells, were isolated using the high-definition CTC assay and biophysically profiled using quantitative phase microscopy. Single CTCs and aggregates were then modeled in an in vitro system comprised of multiple breast cancer cell lines and microfluidic devices used to model E-selectin mediated rolling in the vasculature. Using a numerical model coupling elastic collisions between red blood cells and CTCs, the dependence of CTC vascular margination on single CTCs and CTC aggregate morphology and stiffness was interrogated. These results provide a multifaceted characterization of single CTC and CTC aggregate dynamics in the vasculature and illustrate a framework to integrate clinical, biophysical, and mathematical approaches to enhance our understanding of the fluid phase of cancer.

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50

Sekhar, Ashok, and Lewis E. Kay. "An NMR View of Protein Dynamics in Health and Disease." Annual Review of Biophysics 48, no. 1 (May 6, 2019): 297–319. http://dx.doi.org/10.1146/annurev-biophys-052118-115647.

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Biological molecules are often highly dynamic, and this flexibility can be critical for function. The large range of sampled timescales and the fact that many of the conformers that are continually explored are only transiently formed and sparsely populated challenge current biophysical approaches. Solution nuclear magnetic resonance (NMR) spectroscopy has emerged as a powerful method for characterizing biomolecular dynamics in detail, even in cases where excursions involve short-lived states. Here, we briefly review a number of NMR experiments for studies of biomolecular dynamics on the microsecond-to-second timescale and focus on applications to protein and nucleic acid systems that clearly illustrate the functional relevance of motion in both health and disease.

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