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Готові списки джерел за темами / Biophysical dynamics

Добірка наукової літератури з теми "Biophysical dynamics"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 5 лютого 2022

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Статті в журналах з теми "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.

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

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.

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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Дисертації з теми "Biophysical dynamics":

1

Brandt, Erik G. "Interactions and dynamics in biophysical model systems /." Stockholm : Skolan för teknikvetenskap, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10300.

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2

Elmlund, Hans. "Protein structure dynamics and interplay : by single-particle electron microscopy." Doctoral thesis, Stockholm : Teknik och hälsa, Technology and Health, Kungliga Tekniska högskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4669.

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3

Dale, Michael Anthony Joseph. "Global Energy Modelling : A Biophysical Approach (GEMBA)." Thesis, University of Canterbury. Mechanical Engineering, 2010. http://hdl.handle.net/10092/5156.

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The aim of this thesis is to take a broad conceptual overview of the global energy system and investigate what the aims of sustainability might entail for such a system. The work presented uses a biophysical economic approach in that the dynamics of the global economy are investigated using the tool box of the physical sciences, including the laws of thermodynamics and the methods of energy analysis. Modern society currently uses approximately 500 exajoules (EJ = 10^18 J) of total primary energy supply (TPES) each year. This energy consumption has been increasing at roughly 2% per year for the past two hundred years. TPES is currently dominated by three non-renewable energy sources: coal, oil and gas which, together with energy from nuclear fission of uranium, make up around 85% of the energy market. Consumption of finite resources at a continuously growing rate is not sustainable in the long-term. A trend in policy direction is to seek a transition to renewable sources of energy. This thesis seeks to explore two questions: are the technical potentials of renewable energy sources enough to supply the current and/or projected demand for energy and what would be the effect on the physical resource economy of a transition to an energy supply system run entirely on renewable energy sources? The Global Energy Model using a Biophysical Approach (GEMBA) methodology developed here is compared and contrasted with other approaches that are used to study the global energy-economy system, including the standard neoclassical economic approach used in such models as MESSAGE and MARKAL. A number of meta-analyses have been conducted in support of the GEMBA model. These include: meta-analysis of historic energy production from all energy sources; meta-analysis of global energy resources for all energy sources; meta-analysis of energy-return-on-investment (EROI) for all energy sources. The GEMBA methodology uses a systems dynamic modelling approach utilising stocks and flows, feedback loops and time delays to capture the behaviour of the global energy-economy system. The system is decomposed into elements with simple behaviour that is known through energy analysis. The interaction of these elements is captured mathematically and run numerically via the systems dynamics software package, VenSim. Calibration of the model has been achieved using historic energy production data from 1800 to 2005. The core of the GEMBA methodology constitutes the description of a dynamic EROI function over the whole production cycle of an energy resource from initial development, through maturation to decline in production, in the case of non-renewable resources, or to the technical potential in the case of renewable resources. Using the GEMBA methodology, the global energy-economy system is identified as a self-regulating system. The self-regulating behaviour acts to constrain the amount of total primary energy supply that the system can produce under a renewable-only regime. A number of analyses are conducted to test the sensitivity of the system to such changes as: an increase of the technical potential of renewable resources; technological breakthroughs which would significantly increase the EROI of renewable resources; a decrease in the capital intensity of renewable resources and; an increase in the energy intensity of the economy, A statistical analysis reflecting the wide range of values of both the estimates of EROI and technical potentials of renewable energy sources has also been undertaken using a Monte Carlo approach. The results from the modelling suggest that not all levels of energy demand projected by the WEA can be supplied by an energy system running solely on renewable energy. The Monte Carlo analyses suggest that reduction in total energy yield over current (2010) levels might occur with a 20-30% possibility. The middle and high growth scenarios from the WEA are greater than 95% of all scenarios modelled, hence seem unlikely to be sustained by an energy system running solely on renewable energy. This finding has implications for the future direction of both engineering and technology research as well as for energy policy. These implications are discussed.

4

Pearson, Joshua Thomas. "A biophysical study of protein dynamics and protein-ligand interactions /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8173.

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5

Stollar, Elliott Jonathan. "Biophysical and crystallographic investigation of homeodomain stability, dynamics, and recognition." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615778.

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6

Zerlaut, Yann. "Biophysical and circuit properties underlying population dynamics in neocortical networks." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066095/document.

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Le néocortex possède un état activé dans lequel l'activité corticalemanifeste un comportement complexe. Au niveau cellulaire, l'activitéest caractérisée par de fortes fluctuations sous-liminaires dupotential membranaire et une décharge irrégulière à bassefréquence. Au niveau du réseau, l'activité est marquée par un faibleniveau de synchronie et une dynamique chaotique. Néanmoins, c'est dansce régime que l'information est traitée de manière fiable par lesréseaux neuronaux. Ce régime est donc crucial pour le traitement del'information par le cortex. Dans cette thèse, nous contribuons à sacompréhension en examinant comment les propriétés biophysiques auniveau cellulaire combinées avec les propriétés d'architecture desréseaux façonnent cette dynamique asynchrone.Cette thèse repose sur les modèles de dynamique de réseaux appelésmodèles de champ moyen, un formalisme théorique qui décrit ladynamique de population grâce à une approche auto-consistante. Aucoeur de ce formalisme se trouve la fonction de transfertneuronale : la fonction entrée-sortie d'un neurone. La première partiede cette thèse s'attache à dériver des fonctions de transfertbiologiquement réalistes en incorporant des caractérisationsexpérimentales.Dans un premier temps, nous avons examiné in vitro comment lesneurones néocorticaux pyramidaux de la couche V du cortex visuelrépondent à des fluctuations du potentiel membranaire. Nous avonsobservé que les neurones individuels ne diffèrent pas seulement entermes d'excitabilité, mais qu'ils diffèrent aussi par leurssensibilités aux paramètres des fluctuations. Dans un deuxième temps,nous avons étudié de manière théorique comment l'intégrationdendritique dans des structures arborescentes façonne les fluctuationsau soma. Nous avons observé que, en fonction des propriétés del'activité présynaptique, différentes comodulations des paramètres desfluctuations pouvaient être obtenues. En combinant cette observationavec nos mesures expérimentales, nous avons observé que cela induisaitdes couplages différents entre activité synaptique et déchargeneuronale pour chaque neurone. Nous proposons donc que, puisque cemécanisme offre un moyen d'activer spécifiquement certains neurones enfonction des propriétés de l'entrée, l'hétérogénéité biophysiquepourrait contribuer à l'encodage de propriétés des stimuli dans lestraitements de l'information sensorielle.La deuxième partie de cette thèse examine comment les propriétésd'architecture des réseaux neuronaux se combinent avec les propriétésbiophysiques et affectent les réponses sensorielles via des effets dedynamiques de populations.Nous avons tout d'abord examiné de manière théorique comment un hautniveau d'activité spontanée impactait les réponses post-synaptiquesdans le cortex. Nous avons observé que la compétition entre lerecrutement dans le réseau cortical activé et les effets deconductances associés prédisaient une relation non-triviale entrel'intensité des stimuli et l'amplitude des réponses. Cette prédictionfut observée dans des enregistrements de réponses post-synaptiquesdans le cortex auditif du rat in vivo en réponse à des stimulicorticaux, thalamiques et auditifs.Pour finir, en tirant avantage des approches de champ moyen, nousavons construit un modèle grande échelle du réseau des couches II-IIIincluant le réseau des fibres horizontales. Nous avons examiné lespropriétés intégratives spatio-temporelles du modèle et nous les avonscomparées avec des mesures par imagerie optique de l'activitécérébrale chez le singe éveillé. En particulier, nous avonsreconstruit une expérience typique du traitement sensoriel: lemouvement apparent. Le modèle prédit un fort signal suppressif dont leprofil spatio-temporel correspond quantitativement à celui observé invivo
The neocortex of awake animals displays an activated state in whichcortical activity manifests highly complex, seemingly noisybehavior. At the level of single neurons the activity is characterizedby strong subthreshold fluctuations and irregular firing at lowrate. At the network level, the activity is weakly synchronized andexhibits a chaotic dynamics. Yet, it is within this regime thatinformation is processed reliably through neural networks. This regimeis thus crucial to neural computation. In this thesis, we contributeto its understanding by investigating how the biophysical propertiesat the cellular level combined with the properties of the networkarchitecture shapes this asynchronous dynamics.This thesis builds up on the so-called mean-field models of networkdynamics, a theoretical formalism that describes population dynamicsvia a self-consistency approach. At the core of this formalism lie theneuronal transfer function: the input-output description of individualneurons. The first part of this thesis focuses on derivingbiologically-realistic neuronal transfer functions. We firstformulate a two step procedure to incorporate biological details (suchas an extended dendritic structure and the effect of various ionicchannels) into this transfer function based on experimentalcharacterizations.First, we investigated in vitro how layer V pyramidal neocorticalneurons respond to membrane potential fluctuations on a cell-by-cellbasis. We found that, not only individual neurons strongly differ interms of their excitability, but also, and unexpectedly, in theirsensitivities to fluctuations. In addition, using theoreticalmodeling, we attempted to reproduce these results. The model predictsthat heterogeneous levels of biophysical properties such as sodiuminactivation, sharpness of sodium activation and spike frequencyadaptation account for the observed diversity of firing rateresponses.Then, we studied theoretically how dendritic integration in branchedstructures shape the membrane potential fluctuations at the soma. Wefound that, depending on the type of presynaptic activity, variouscomodulations of the membrane potential fluctuations could beachieved. We showed that, when combining this observation with theheterogeneous firing responses found experimentally, individual neuronsdifferentially responded to the different types of presynapticactivities. We thus propose that, because this mechanism offers a wayto produce specific activation as a function of the input properties,biophysical heterogeneity might contribute to the encoding of the stimulusproperties during sensory processing in neural networks.The second part of this thesis investigates how circuit properties,such as recurrent connectivity and lateral connectivity, combine withbiophysical properties to impact sensory responses through effectsmediated by population dynamics.We first investigated what was the effect of a high level of ongoingdynamics (the Up-state compared to the Down-state) on the scaling ofpost-synaptic responses. We found that the competition between therecruitment within the active recurrent network (in favor of highresponses in the Up-state) and the increased conductance level due tobackground activity (in favor of reduced responses in the Up-state)predicted a non trivial stimulus-response relationship as a functionof the intensity of the stimulation. This prediction was shown toaccurately capture measurements of post-synaptic membrane potentialresponses in response to cortical, thalamic or auditory stimulation inrat auditory cortex in vivo.Finally, by taking advantage of the mean-field approach, weconstructed a tractable large-scale model of the layer II-III networkincluding the horizontal fiber network. We investigate thespatio-temporal properties of this large-scale model and we compareits predictions with voltage sensitive dye imaging in awake fixatingmonkey

7

Doerdelmann, Thomas. "Structural and Biophysical Studies of the Pitx2 Homeodomain." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1307443112.

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8

Pathmasiri, Wimal. "Structural and Biophysical Studies of Nucleic Acids." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8245.

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9

Reyns, Nathalie Brigitte. "Biophysical dispersal dynamics of the blue crab in Pamlico Sound, North Carolina." NCSU, 2004. http://www.lib.ncsu.edu/theses/available/etd-10312004-143755/.

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For many species such as the blue crab, Callinectes sapidus, successful estuarine recruitment to juvenile nursery habitats is dependent on the biophysical processes experienced during dispersal of the early life stages. The goal of this study was to determine how blue crab primary (postlarval) and secondary (early juvenile) dispersal occurs within a predominately wind-driven estuary, Pamlico Sound, North Carolina, USA. We (1) characterized circulation patterns in Pamlico Sound during the fall blue crab recruitment months over two consecutive years using current meters (2) sampled during multiple 24 h periods to relate spatiotemporal water column distributions of postlarval and early juveniles blue crabs with circulation patterns, and used a hydrodynamic model to recreate dispersal trajectories from eastern (inlet) to western sound nursery habitats and (3) examined the environmental (wind, diel cycle, tidal phase) and biological (ontogenetic, density-dependent) factors that contribute to early juvenile blue crab secondary dispersal from near-inlet nursery habitats. During our study, surface currents responded synchronously to wind-forcing by generally flowing in the same direction as the wind. Particle-tracking simulations suggested that dispersal from Oregon and Hatteras Inlets to across-sound nursery habitats resulted from the combined use of tidal and wind-driven currents. Simulation results and observed crab distributions further indicated that Oregon Inlet was the primary supplier of postlarval blue crabs (dispersing in surface waters at night) throughout Pamlico Sound, as postlarvae ingressing through Hatteras Inlet were not retained within our study area. Furthermore, Oregon Inlet supplied early juvenile blue crabs (dispersing in bottom waters at night) to northwestern sound habitats, while crabs from Hatteras Inlet dispersed to mid- and eastern-sound regions. Results from our study in near-inlet settlement habitats confirmed the importance of tides to mediating dispersal partway into Pamlico Sound, as early juvenile blue crabs responded to increasing conspecific density in settlement habitats by using flood-tide transport near the inlets to rapidly leave these habitats. Based on our findings, we make recommendations regarding the prioritization of nursery habitats for conservation and fisheries management.

10

Chimatiro, Sloans Kalumba. "The biophysical dynamics of the Lower Shire River Floodplain fisheries in Malawi /." Connect to this title online, 2004. http://eprints.ru.ac.za/177/.

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Книги з теми "Biophysical dynamics":

1

Kraikivski, Pavel. 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.

3

Pozrikidis, C. 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

Babloyantz, A. 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

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

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Частини книг з теми "Biophysical dynamics":

1

Kammerdiner, Alla, Nikita Boyko, Nong Ye, Jiping He, and Panos Pardalos. "Integration of Signals in Complex Biophysical Systems." In Dynamics of Information Systems, 197–211. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5689-7_10.

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2

Kosztin, Ioan, and Klaus Schulten. "Molecular Dynamics Methods for Bioelectronic Systems in Photosynthesis." In Biophysical Techniques in Photosynthesis, 445–64. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8250-4_22.

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3

Timofeeva, Yulia. "Intracellular Calcium Dynamics: Biophysical and Simplified Models." In Springer Series in Computational Neuroscience, 69–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00817-8_3.

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4

Buda, Francesco. "Density Functional Theory and Car-Parrinello Molecular Dynamics Methods." In Biophysical Techniques in Photosynthesis, 487–99. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8250-4_24.

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Gallego, Alejandro. "Biophysical Models: An Evolving Tool in Marine Ecological Research." In Modelling Complex Ecological Dynamics, 279–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-05029-9_20.

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6

Plaxco, Kevin W., and Christopher M. Dobson. "Monitoring Protein Folding Using Time-Resolved Biophysical Techniques." In Protein Dynamics, Function, and Design, 163–72. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-4895-9_11.

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7

Leigh, Brian S., Diana E. Schlamadinger, and Judy E. Kim. "Structures and Dynamics of Proteins Probed by UV Resonance Raman Spectroscopy." In Biophysical Methods for Biotherapeutics, 243–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118354698.ch9.

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8

Cardullo, Richard A., Robert M. Mungovan, and David E. Wolf. "Imaging Membrane Organization and Dynamics." In Biophysical and Biochemical Aspects of Fluorescence Spectroscopy, 231–60. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4757-9513-4_8.

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Gierasch, Lila M. "Signal Sequences: Roles and Interactions by Biophysical Methods." In Biological Membranes: Structure, Biogenesis and Dynamics, 191–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78846-8_18.

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Konermann, Lars, Johannes Messinger, and Warwick Hillier. "Mass Spectrometry-Based Methods for Studying Kinetics and Dynamics in Biological Systems." In Biophysical Techniques in Photosynthesis, 167–90. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8250-4_9.

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Тези доповідей конференцій з теми "Biophysical dynamics":

1

Feng, Jianfeng. "A comparison between abstract and biophysical neuron models." In Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302375.

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2

Du, Y., and A. M. Al-Jumaily. "Modified Fading Memory Model to Describe ASM Dynamics." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41179.

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A modified fading memory model is introduced in this work to describe the behavior of airway smooth muscle dynamics. The model is used to simulate two biophysical cases: a finite duration for the step change in length and a case for external longitudinal oscillations. For both cases, the model describes the cross-bridge behaviour well and indicates that the muscle length change is the most important factor to determine the degree of cross-bridge detachment. However, the frequency of oscillation represents the velocity of the length change, which affects the cross-bridge cycling rate as reflected in the lower frequency range. The model is intended to interpret certain biophysical processes and not to accurately model the biophysical events underlying muscle contraction.

3

Majumdar, Anindya, and Sean J. Kirkpatrick. "Optical vortices as potential indicators of biophysical dynamics." In SPIE BiOS, edited by Valery V. Tuchin, Kirill V. Larin, Martin J. Leahy, and Ruikang K. Wang. SPIE, 2017. http://dx.doi.org/10.1117/12.2251026.

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4

Yu, Theodore, Terrence J. Sejnowski, and Gert Cauwenberghs. "Biophysical neural spiking and bursting dynamics in reconfigurable analog VLSI." In 2010 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2010. http://dx.doi.org/10.1109/biocas.2010.5709602.

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5

Al-Jumaily, A. M., and Y. Du. "Simplified Model for ASM Dynamics." In ASME 2011 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2011. http://dx.doi.org/10.1115/sbc2011-53133.

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The dynamic response of contracted airway smooth muscles to a finite length change and longitudinal oscillations is described using a simplified model. The model is intended to interpret the biophysical events but not to accurately describe them. It shows that the value of tissue length changes have pronounced indications of cross-bridge detachment. However, the frequency of oscillations represents the velocity of the length change, which affects the cross-bridge cycling rate reflected in the low frequency range.

6

Spiliotis, Konstantinos G., Hari Radhakrishnan, and George C. Georgiou. "Randomness switches the dynamics in a biophysical model for Parkinson Disease." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756429.

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7

Yu, T., and G. Cauwenberghs. "Biophysical synaptic dynamics in an analog VLSI network of hodgkin-huxley neurons." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5333272.

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8

Shcheglova, S. N., and B. O. Shcheglov. "Development of a mathematical model for assessing the biophysical effect of radiation on human health in the North." In XXV REGIONAL SCIENTIFIC CONFERENCE STUDENTS, APPLICANTS AND YOUNG RESEARCHERS. Знание-М, 2020. http://dx.doi.org/10.38006/907345-63-8.2020.155.162.

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The aim of the study is to create a mathematical-biophysical model for modeling the dynamics of the action of solar radiation on the environment of the northern regions of the Earth. Using the Schrödinger wave function, the propagation of the intensity of solar radiation is presented. As a result, a biophysical model of the action of electromagnetic radiation is obtained.

9

Dimitrov, Petar. "Investigation of dynamics of some biophysical parameters of Norway spruce stands by MODIS data." In 2009 4th International Conference on Recent Advances in Space Technologies (RAST). IEEE, 2009. http://dx.doi.org/10.1109/rast.2009.5158232.

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10

Deb, Saswati, and Arun Chakraborty. "Simulation of plankton dynamics in the Hooghly Estuary using a high resolution biophysical model." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6350952.

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Звіти організацій з теми "Biophysical dynamics":

1

Koch, Christof. Dynamic Biophysical Theory for the Role of Hippocampal Neural Networks in the Declarative Memory System. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada279961.

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2

Verburg, Peter H., Žiga Malek, Sean P. Goodwin, and Cecilia Zagaria. The Integrated Economic-Environmental Modeling (IEEM) Platform: IEEM Platform Technical Guides: User Guide for the IEEM-enhanced Land Use Land Cover Change Model Dyna-CLUE. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003625.

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The Conversion of Land Use and its Effects modeling framework (CLUE) was developed to simulate land use change using empirically quantified relations between land use and its driving factors in combination with dynamic modeling of competition between land use types. Being one of the most widely used spatial land use models, CLUE has been applied all over the world on different scales. In this document, we demonstrate how the model can be used to develop a multi-regional application. This means, that instead of developing numerous individual models, the user only prepares one CLUE model application, which then allocates land use change across different regions. This facilitates integration with the Integrated Economic-Environmental Modeling (IEEM) Platform for subnational assessments and increases the efficiency of the IEEM and Ecosystem Services Modeling (IEEMESM) workflow. Multi-regional modelling is particularly useful in larger and diverse countries, where we can expect different spatial distributions in land use changes in different regions: regions of different levels of achieved socio-economic development, regions with different topographies (flat vs. mountainous), or different climatic regions (dry vs humid) within a same country. Accounting for such regional differences also facilitates developing ecosystem services models that consider region specific biophysical characteristics. This manual, and the data that is provided with it, demonstrates multi-regional land use change modeling using the country of Colombia as an example. The user will learn how to prepare the data for the model application, and how the multi-regional run differs from a single-region simulation.