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Статті в журналах з теми "Biological dynamic"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Zhang, Mo, and Hai Shen. "Biological Communication Dynamic Model Research." Applied Mechanics and Materials 556-562 (May 2014): 4975–78. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4975.

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Biological communication behavior is in everywhere, all over the nature, biological system and human society. In simple terms, Swarm intelligence is emerging though information communication and collaboration among some dispersed and simple individuals. Inspired by biological communication behavior, aimed at understanding swarm system collective dynamics behavior, and from the point of system cybernetics, this paper study the relevant biological communication dynamic model, such as the symbiotic model, attractive-repulsive model, external effect model and the multi-population coevolution model and so on. Also introduce the rules of these models, which provide theoretical basis for designing intelligent swarm intelligent system.

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2

Smith, Jeremy C., Pan Tan, Loukas Petridis, and Liang Hong. "Dynamic Neutron Scattering by Biological Systems." Annual Review of Biophysics 47, no. 1 (May 20, 2018): 335–54. http://dx.doi.org/10.1146/annurev-biophys-070317-033358.

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Dynamic neutron scattering directly probes motions in biological systems on femtosecond to microsecond timescales. When combined with molecular dynamics simulation and normal mode analysis, detailed descriptions of the forms and frequencies of motions can be derived. We examine vibrations in proteins, the temperature dependence of protein motions, and concepts describing the rich variety of motions detectable using neutrons in biological systems at physiological temperatures. New techniques for deriving information on collective motions using coherent scattering are also reviewed.

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3

Campelo, F., and A. HernÃ¡ndez-Machado. "Dynamic instabilities in biological membranes." PAMM 7, no. 1 (December 2007): 1121403–4. http://dx.doi.org/10.1002/pamm.200700341.

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4

Zhang, Duzhen, Tielin Zhang, Shuncheng Jia, and Bo Xu. "Multi-Sacle Dynamic Coding Improved Spiking Actor Network for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 59–67. http://dx.doi.org/10.1609/aaai.v36i1.19879.

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With the help of deep neural networks (DNNs), deep reinforcement learning (DRL) has achieved great success on many complex tasks, from games to robotic control. Compared to DNNs with partial brain-inspired structures and functions, spiking neural networks (SNNs) consider more biological features, including spiking neurons with complex dynamics and learning paradigms with biologically plausible plasticity principles. Inspired by the efficient computation of cell assembly in the biological brain, whereby memory-based coding is much more complex than readout, we propose a multiscale dynamic coding improved spiking actor network (MDC-SAN) for reinforcement learning to achieve effective decision-making. The population coding at the network scale is integrated with the dynamic neurons coding (containing 2nd-order neuronal dynamics) at the neuron scale towards a powerful spatial-temporal state representation. Extensive experimental results show that our MDC-SAN performs better than its counterpart deep actor network (based on DNNs) on four continuous control tasks from OpenAI gym. We think this is a significant attempt to improve SNNs from the perspective of efficient coding towards effective decision-making, just like that in biological networks.

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5

Gusain, Pooja, Neha Sharma, Tsuyoshi Yoda, and Masahiro Takagi. "1P220 Dynamic Response of Menthol on Thermo-Induced Cell Membrane: More than Receptors(13B. Biological & Artifical membrane: Dynamics,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S142. http://dx.doi.org/10.2142/biophys.53.s142_3.

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6

Kinugasa, Tetsuya, and Yasuhiro Sugimoto. "Dynamically and Biologically Inspired Legged Locomotion: A Review." Journal of Robotics and Mechatronics 29, no. 3 (June 20, 2017): 456–70. http://dx.doi.org/10.20965/jrm.2017.p0456.

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Анотація:

[abstFig src='/00290003/01.jpg' width='300' text='Passive dynamic walking: RW03 and Jenkka III' ] Legged locomotion, such as walking, running, turning, and jumping depends strongly on the dynamics and the biological characteristics of the body involved. Gait patterns and energy efficiency, for instance, are known to be greatly affected, not only by travel speed and ground contact conditions but also by body structure such as joint stiffness and coordination, and foot sole shape. To understand legged locomotion principles, we must elucidate how the body’s dynamic and biological characteristics affect locomotion. Efforts should also be made to incorporate these characteristics inventively in order to improve locomotion performance with regard to robustness, adaptability, and efficiency, which realize more refined legged locomotion. For this special issue, we invited our readers to submit papers with approaches to achieving legged locomotion based on dynamic and biological characteristics and studies investigating the effects of these characteristics. In this paper, we review studies on dynamically and biologically inspired legged locomotion.

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7

Kinugasa, Tetsuya, Koh Hosoda, Masatsugu Iribe, Fumihiko Asano, and Yasuhiro Sugimoto. "Special Issue on Dynamically and Biologically Inspired Legged Locomotion." Journal of Robotics and Mechatronics 29, no. 3 (June 20, 2017): 455. http://dx.doi.org/10.20965/jrm.2017.p0455.

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Анотація:

Legged locomotion, including walking, running, turning, and jumping, strongly depends on the dynamics and biological characteristics of the body involved. Gait patterns and energy efficiency, for example, are known to be greatly affected by not only travel velocity and ground contact conditions but also by body configuration, such as joint stiffness and coordination, as well as foot sole shape. To understand legged locomotion principles, we must clarify how the body’s dynamic and biological characteristics affect locomotion. Effort must also be made to incorporate these characteristics inventively to improve locomotion performance, such as robustness, adaptability, and efficiency, which further refine the legged locomotion. This special issue on “Dynamically and Biologically Inspired Legged Locomotion,” studies on legged locomotion based on dynamic and biological characteristics, covers a wide range of themes, such as a rimless wheel, a design method for a biped based on passive dynamic walking, the analysis of biped locomotion based on passive dynamic walking and dynamically inspired walking, an analysis of gait generation for a triped robot, and quadruped locomotion with a flexible trunk. Since there are interesting papers on legged robots with different numbers of legs, we basically organized the papers based on the number of legs. Studies on “Dynamically and Biologically Inspired Legged Locomotion” are expected to not only realize and improve legged locomotion as engineering, but also to reveal the locomotion mechanism of various creatures as science.

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8

Marigo, Alessia, and Benedetto Piccoli. "A model for biological dynamic networks." Networks & Heterogeneous Media 6, no. 4 (2011): 647–63. http://dx.doi.org/10.3934/nhm.2011.6.647.

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9

Wu, Wu, Feng Wang, and Maw Chang. "Dynamic sensitivity analysis of biological systems." BMC Bioinformatics 9, Suppl 12 (2008): S17. http://dx.doi.org/10.1186/1471-2105-9-s12-s17.

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10

Cushing, J. M. "Dynamic energy budgets in biological systems." Mathematical Biosciences 137, no. 2 (October 1996): 135–37. http://dx.doi.org/10.1016/s0025-5564(96)00047-8.

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11

McDonald, Daniel, Yoshiki Vázquez-Baeza, William A. Walters, J. Gregory Caporaso, and Rob Knight. "From molecules to dynamic biological communities." Biology & Philosophy 28, no. 2 (February 5, 2013): 241–59. http://dx.doi.org/10.1007/s10539-013-9364-4.

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12

HALLAM, T. "Dynamic Energy Budgets in Biological Systems." Bulletin of Mathematical Biology 57, no. 3 (1995): 504–6. http://dx.doi.org/10.1016/s0092-8240(05)81782-3.

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13

Christini, David J., Jeff Walden, and Jay M. Edelberg. "Direct biologically based biosensing of dynamic physiological function." American Journal of Physiology-Heart and Circulatory Physiology 280, no. 5 (May 1, 2001): H2006—H2010. http://dx.doi.org/10.1152/ajpheart.2001.280.5.h2006.

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Анотація:

Dynamic regulation of biological systems requires real-time assessment of relevant physiological needs. Biosensors, which transduce biological actions or reactions into signals amenable to processing, are well suited for such monitoring. Typically, in vivo biosensors approximate physiological function via the measurement of surrogate signals. The alternative approach presented here would be to use biologically based biosensors for the direct measurement of physiological activity via functional integration of relevant governing inputs. We show that an implanted excitable-tissue biosensor (excitable cardiac tissue) can be used as a real-time, integrated bioprocessor to analyze the complex inputs regulating a dynamic physiological variable (heart rate). This approach offers the potential for long-term biologically tuned quantification of endogenous physiological function.

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14

Gupta, Sudipta, and Rana Ashkar. "The dynamic face of lipid membranes." Soft Matter 17, no. 29 (2021): 6910–28. http://dx.doi.org/10.1039/d1sm00646k.

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Lipid membranes envelope live cells and mediate vital biological functions through regulated spatiotemporal dynamics. This review highlights the role of neutron scattering, among other approaches, in uncovering the dynamic properties of lipid membranes.

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15

Kang, Hyun-Seo, and Michael Sattler. "Capturing dynamic conformational shifts in protein–ligand recognition using integrative structural biology in solution." Emerging Topics in Life Sciences 2, no. 1 (April 20, 2018): 107–19. http://dx.doi.org/10.1042/etls20170090.

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In recent years, a dynamic view of the structure and function of biological macromolecules is emerging, highlighting an essential role of dynamic conformational equilibria to understand molecular mechanisms of biological functions. The structure of a biomolecule, i.e. protein or nucleic acid in solution, is often best described as a dynamic ensemble of conformations, rather than a single structural state. Strikingly, the molecular interactions and functions of the biological macromolecule can then involve a shift between conformations that pre-exist in such an ensemble. Upon external cues, such population shifts of pre-existing conformations allow gradually relaying the signal to the downstream biological events. An inherent feature of this principle is conformational dynamics, where intrinsically disordered regions often play important roles to modulate the conformational ensemble. Unequivocally, solution-state NMR spectroscopy is a powerful technique to study the structure and dynamics of such biomolecules in solution. NMR is increasingly combined with complementary techniques, including fluorescence spectroscopy and small angle scattering. The combination of these techniques provides complementary information about the conformation and dynamics in solution and thus affords a comprehensive description of biomolecular functions and regulations. Here, we illustrate how an integrated approach combining complementary techniques can assess the structure and dynamics of proteins and protein complexes in solution.

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16

DE CARVALHO, KELLY C., and TÂNIA TOMÉ. "PROBABILISTIC CELLULAR AUTOMATA DESCRIBING A BIOLOGICAL TWO-SPECIES SYSTEM." Modern Physics Letters B 18, no. 17 (July 30, 2004): 873–80. http://dx.doi.org/10.1142/s0217984904007396.

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We consider two probabilistic cellular automata to analyze the stochastic dynamics of a biological two-species system. We focus our attention on the characterization of the dynamic patterns exhibited by both models. Performing Monte Carlo simulations, we observe a time oscillating behavior that occurs at a local level.

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17

Zeng, Tao, and Luonan Chen. "Tracing dynamic biological processes during phase transition." BMC Systems Biology 6, Suppl 1 (2012): S12. http://dx.doi.org/10.1186/1752-0509-6-s1-s12.

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18

Kondofersky, Ivan, Christiane Fuchs, and Fabian J. Theis. "Identifying latent dynamic components in biological systems." IET Systems Biology 9, no. 5 (October 1, 2015): 193–203. http://dx.doi.org/10.1049/iet-syb.2014.0013.

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19

Secomb, Timothy W., Jonathan P. Alberding, Richard Hsu, Mark W. Dewhirst, and Axel R. Pries. "Angiogenesis: An Adaptive Dynamic Biological Patterning Problem." PLoS Computational Biology 9, no. 3 (March 21, 2013): e1002983. http://dx.doi.org/10.1371/journal.pcbi.1002983.

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20

Haken, H., J. A. S. Kelso, A. Fuchs, and A. S. Pandya. "Dynamic pattern recognition of coordinated biological motion." Neural Networks 3, no. 4 (January 1990): 395–401. http://dx.doi.org/10.1016/0893-6080(90)90022-d.

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21

Deniz, Ashok A. "Networking and Dynamic Switches in Biological Condensates." Cell 181, no. 2 (April 2020): 228–30. http://dx.doi.org/10.1016/j.cell.2020.03.056.

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22

Kaneko, Kunihiko. "Relevance of dynamic clustering to biological networks." Physica D: Nonlinear Phenomena 75, no. 1-3 (August 1994): 55–73. http://dx.doi.org/10.1016/0167-2789(94)90274-7.

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23

Sillar, Keith. "Dynamic Biological Networks: the Stomatogastric Nervous System." Trends in Neurosciences 16, no. 5 (May 1993): 198–99. http://dx.doi.org/10.1016/0166-2236(93)90153-d.

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24

Ross, M. D., J. E. Dayhoff, and D. H. Mugler. "Toward modeling a dynamic biological neural network." Mathematical and Computer Modelling 13, no. 7 (1990): 97–105. http://dx.doi.org/10.1016/0895-7177(90)90132-7.

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25

Choi, J., M. Y. Choi, and B. G. Yoon. "Dynamic model for failures in biological systems." Europhysics Letters (EPL) 71, no. 3 (August 2005): 501–7. http://dx.doi.org/10.1209/epl/i2004-10544-3.

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26

Specht, Alexandre, Frédéric Bolze, Ziad Omran, Jean‐François Nicoud, and Maurice Goeldner. "Photochemical tools to study dynamic biological processes." HFSP Journal 3, no. 4 (August 2009): 255–64. http://dx.doi.org/10.2976/1.3132954.

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27

Rosenberg, Michael S. "Modeling Dynamic Biological Systems.Bruce Hannon , Matthias Ruth." Quarterly Review of Biology 73, no. 3 (September 1998): 340. http://dx.doi.org/10.1086/420315.

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28

Anselmetti, D. "Biological materials studied with dynamic force microscopy." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 12, no. 3 (May 1994): 1500. http://dx.doi.org/10.1116/1.587272.

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29

Mohler, R. R. "Mathematical Modelling of dynamic and biological systems." Mathematical Biosciences 82, no. 1 (November 1986): 121–22. http://dx.doi.org/10.1016/0025-5564(86)90009-x.

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30

Daun, Silvia, Jonathan Rubin, Yoram Vodovotz, and Gilles Clermont. "Equation-based models of dynamic biological systems." Journal of Critical Care 23, no. 4 (December 2008): 585–94. http://dx.doi.org/10.1016/j.jcrc.2008.02.003.

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31

Bhan, Ashish, and Eric Mjolsness. "Static and dynamic models of biological networks." Complexity 11, no. 6 (2006): 57–63. http://dx.doi.org/10.1002/cplx.20140.

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32

Duso, Lorenzo, and Christoph Zechner. "Stochastic reaction networks in dynamic compartment populations." Proceedings of the National Academy of Sciences 117, no. 37 (August 31, 2020): 22674–83. http://dx.doi.org/10.1073/pnas.2003734117.

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Анотація:

Compartmentalization of biochemical processes underlies all biological systems, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems, but a general and sufficiently effective approach remains lacking. In this work, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes, including subcellular compartmentalization and tissue homeostasis.

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33

Liu, H. "Simulation-Based Biological Fluid Dynamics in Animal Locomotion." Applied Mechanics Reviews 58, no. 4 (July 1, 2005): 269–82. http://dx.doi.org/10.1115/1.1946047.

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This article presents a wide-ranging review of the simulation-based biological fluid dynamic models that have been developed and used in animal swimming and flying. The prominent feature of biological fluid dynamics is the relatively low Reynolds number, e.g. ranging from 100 to 104 for most insects; and, in general, the highly unsteady motion and the geometric variation of the object result in large-scale vortex flow structure. We start by reviewing literature in the areas of fish swimming and insect flight to address the usefulness and the difficulties of the conventional theoretical models, the experimental physical models, and the computational mechanical models. Then we give a detailed description of the methodology of the simulation-based biological fluid dynamics, with a specific focus on three kinds of modeling methods: (1) morphological modeling methods, (2) kinematic modeling methods, and (3) computational fluid dynamic methods. An extended discussion on the verification and validation problem is also presented. Next, we present an overall review on the most representative simulation-based studies in undulatory swimming and in flapping flight over the past decade. Then two case studies, of the tadpole swimming and the hawkmoth hovering analyses, are presented to demonstrate the context for and the feasibility of using simulation-based biological fluid dynamics to understanding swimming and flying mechanisms. Finally, we conclude with comments on the effectiveness of the simulation-based methods, and also on its constraints.

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34

Yang, Simon X., Max Q. H. Meng, Gavin X. Yuan, and Peter X. Liu. "A Biological Inspired Approach to Collision-Free Path Planning and Tracking Control of a Mobile Robot." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 3 (May 20, 2004): 243–51. http://dx.doi.org/10.20965/jaciii.2004.p0243.

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In this paper, a novel biologically inspired neural network approach is proposed for real-time collision-free path planning and tracking control of a nonholonomic mobile robot in a dynamic environment. The real-time collision-free robot trajectory is generated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation derived from Hodgkin and Huxley’s biological membrane equation. The dynamically changing environment is represented by the dynamic activity landscape of the neural network, where the neural activity propagation is subject to the nonholonomic kinematic constraint of the mobile robot. The tracking velocities for the mobile robot are generated by a novel neural dynamics based controller, which is based on two shunting equations and the conventional backstepping technique. Unlike the conventional backstepping controllers suffer from sharp jumps, the proposed tracking controller can generate smooth and continuous commands. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.

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35

HUNG, TIN-KAN. "VORTICES IN BIOLOGICAL FLOWS." Journal of Mechanics in Medicine and Biology 13, no. 05 (October 2013): 1340001. http://dx.doi.org/10.1142/s0219519413400010.

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Анотація:

Vortices in flow past a heart valve, in streams and behind an arrow were realized, sketched and discussed by Leonardo da Vinci. The forced resonance and collapse of the Tacoma Narrows Bridge under 64 km/h. wind in 1940 and the Kármán vortex street are classic examples of dynamic interaction between fluid flow and solid motion. There are similar and dissimilar characteristics of vortices between biological and physical flow processes. They can be analyzed by numerical solutions of the Navier–Stokes equations with moving boundaries. One approach is to transform the time-dependent domain to a fixed domain with the geometric, kinematic and dynamic parameters as forcing functions in the Navier–Stokes equations.

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36

Alex, J., S. G. E. Rönner-Holm, M. Hunze, and N. C. Holm. "A combined hydraulic and biological SBR model." Water Science and Technology 64, no. 5 (September 1, 2011): 1025–31. http://dx.doi.org/10.2166/wst.2011.472.

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A sequencing batch reactor (SBR) model was developed consisting of six continuous stirred tank reactors which describe the hydraulic flow patterns occurring in different SBR phases. The model was developed using the results of computational fluid dynamics (CFD) simulation studies of an SBR reactor under a selection of dynamic operational phases. Based on the CFD results, the model structure was refined and a simplified ‘driver’ model to allow one to mimic the flow pattern driven by the external operational conditions (influent, aeration, mixing) was derived. The resulting model allows the modeling of biological processes, settlement and hydraulic conditions of cylindrical SBRs.

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37

Naumkina, Dina, Alexander Rostovtsev, and Alexandr Abramov. "Digital heterogeneous dynamic model of peled Coregonus peled Gmelin." Fisheries 2020, no. 5 (October 9, 2020): 80–85. http://dx.doi.org/10.37663/0131-6184-2020-5-80-85.

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Анотація:

The article provides an example of constructing a complex dynamic model of a biological and economic system with the commodity two-year-old peled growing in Lake Ik in 2017-2018 as a case study. A brief description of the lake and a detailed description of the principle of constructing a heterogeneous dynamic model are given. A block diagram of a heterogeneous biological and economic system is under construction. The scenario of temporal development of the system is described. As a result, the model itself is presented in the form of graphs showing time dynamics of the amount of food, fish biomass, and working capital of the peled growing business process.

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38

Chen, Hongyan. "Research on Modeling and Dynamic Characteristics of Complex Biological Neural Network Model considering BP Neural Network Method." Advances in Multimedia 2021 (December 11, 2021): 1–8. http://dx.doi.org/10.1155/2021/7646121.

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Анотація:

Biological neural network system is a complex nonlinear dynamic system, and research on its dynamics is an important topic at home and abroad. This paper briefly introduces the dynamic characteristics and influencing factors of the neural network system, including the effects of time delay and noise on neural network synchronization, synchronous transition, and stochastic resonance, and introduces the modeling of the neural network system. There are irregular mixing problems in the complex biological neural network system. The BP neural network algorithm can be used to solve more complex dynamic behaviors and can optimize the global search. In order to ensure that the neural network increases the biological characteristics, this paper adjusts the parameters of the BP neural network to receive EEG signals in different states. It can simulate different frequencies and types of brain waves, and it can also carry out a variety of simulations during the operation of the system. Finally, the experimental analysis shows that the complex biological neural network model proposed in this paper has good dynamic characteristics, and the application of this algorithm to data information processing, data encryption, and many other aspects has a bright prospect.

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39

Shelef, Yaniv, Avihai Yosef Uzan, Ofer Braunshtein, and Benny Bar-On. "Assessing the Interfacial Dynamic Modulus of Biological Composites." Materials 14, no. 12 (June 21, 2021): 3428. http://dx.doi.org/10.3390/ma14123428.

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Анотація:

Biological composites (biocomposites) possess ultra-thin, irregular-shaped, energy dissipating interfacial regions that grant them crucial mechanical capabilities. Identifying the dynamic (viscoelastic) modulus of these interfacial regions is considered to be the key toward understanding the underlying structure–function relationships in various load-bearing biological materials including mollusk shells, arthropod cuticles, and plant parts. However, due to the submicron dimensions and the confined locations of these interfacial regions within the biocomposite, assessing their mechanical characteristics directly with experiments is nearly impossible. Here, we employ composite-mechanics modeling, analytical formulations, and numerical simulations to establish a theoretical framework that links the interfacial dynamic modulus of a biocomposite to the extrinsic characteristics of a larger-scale biocomposite segment. Accordingly, we introduce a methodology that enables back-calculating (via simple linear scaling) of the interfacial dynamic modulus of biocomposites from their far-field dynamic mechanical analysis. We demonstrate its usage on zigzag-shaped interfaces that are abundant in biocomposites. Our theoretical framework and methodological approach are applicable to the vast range of biocomposites in natural materials; its essence can be directly employed or generally adapted into analogous composite systems, such as architected nanocomposites, biomedical composites, and bioinspired materials.

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40

ZENG, Xiangxing, Juan DU, Kaixin WANG, and Xifu ZHENG. "Temporal Dynamic and Biological Mechanism of Memory Reconsolidation." Advances in Psychological Science 23, no. 4 (2015): 582. http://dx.doi.org/10.3724/sp.j.1042.2015.00582.

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41

Zeng, Tao, and Luonan Chen. "Correction: Tracing dynamic biological processes during phase transition." BMC Systems Biology 6, Suppl 1 (2012): S23. http://dx.doi.org/10.1186/1752-0509-6-s1-s23.

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42

Meinhardt, Hans. "Biological Pattern Formation as a Complex Dynamic Phenomenon." International Journal of Bifurcation and Chaos 07, no. 01 (January 1997): 1–26. http://dx.doi.org/10.1142/s0218127497000029.

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Анотація:

Self-enhancement coupled with one or more antagonistic reactions is the crucial element in pattern forming reactions. Depending on the parameter, this can lead to patterns in space and/or in time which can be either extremely robust and reproducible or highly variable. Complex patterns result from a linkage of many pattern forming reactions, one pattern generates the prerequisites for the next. The support these models have obtained recently by molecular-genetic observations give rise to the hope that in the future an interplay between theory and experiment will lead to a still better understanding of this central issue. Free from functional constraints, the diversity of patterns on the shells of mollusks provide a rich source to study the properties of dynamic systems in general. Everyday, we are confronted by systems that have an inherent tendency to change. The weather, the stock market, or the economic situation are examples in which self-enhancing and antagonistic processes also play a decisive role. The shell patterns are sufficiently complex to be a challenge but also sufficiently simple to be accessible to modeling. Their one-dimensional character and the preservation of the history of their formation provide unusual help for deciphering these patterns. They illustrate the range of behavior that can be generated by modifications of a basic mechanism. They can be regarded as a natural exercise book to study dynamic systems.

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43

Moline, Mark, Thomas Frazer, Robert Chant, Scott Glenn, Charles Jacoby, John Reinfelder, Jennifer Yost, Meng Zhou, and Oscar Schofield. "Biological Responses in a Dynamic Buoyant River Plume." Oceanography 21, no. 4 (December 1, 2008): 70–89. http://dx.doi.org/10.5670/oceanog.2008.06.

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44

Meisgen, Frank. "Dynamic Load Balancing for Simulations of Biological Aging." International Journal of Modern Physics C 08, no. 03 (June 1997): 575–82. http://dx.doi.org/10.1142/s0129183197000461.

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The efficient usage of parallel computers and workstation clusters for biologically motivated simulations depends first of all on a dynamic redistribution of the workload. For the development of a parallel algorithm for the Penna model of aging we have used a dynamic load balancing library, called PLB. It turns out that PLB manages a nearly balanced load situation during runtime taking only a low communication overhead. We compare different architectures like parallel computers and nondedicated heterogeneous networks, and give some results for large populations.

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45

Kuzmina, T. B., N. V. Andreeva, and O. V. Andreeva. "Diagnostics of biological fluids using dynamic scattering method." Journal of Physics: Conference Series 1062 (July 2018): 012005. http://dx.doi.org/10.1088/1742-6596/1062/1/012005.

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46

CORNISH-BOWDEN, ATHEL. "Mathematical Modelling of Dynamic Biological Systems, 2nd Edition." Biochemical Society Transactions 14, no. 2 (April 1, 1986): 511. http://dx.doi.org/10.1042/bst0140511.

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47

OUYANG, QI, YiMing LI, HaiYan LIU, XiaoJun MA, Yi YANG, and ChunBo LOU. "Recent Progress in Dynamic Responding Synthetic Biological Systems." SCIENTIA SINICA Vitae 45, no. 10 (October 1, 2015): 935–42. http://dx.doi.org/10.1360/n052015-00051.

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48

Wise, Thomas N. "Psychosomatic Medicine: Integrating Dynamic, Behavioral and Biological Perspectives." Psychotherapy and Psychosomatics 46, no. 1-2 (1986): 85–95. http://dx.doi.org/10.1159/000287965.

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49

Roberts, Patrick D. "Classification of Temporal Patterns in Dynamic Biological Networks." Neural Computation 10, no. 7 (October 1, 1998): 1831–46. http://dx.doi.org/10.1162/089976698300017160.

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A general method is presented to classify temporal patterns generated by rhythmic biological networks when synaptic connections and cellular properties are known. The method is discrete in nature and relies on algebraic properties of state transitions and graph theory. Elements of the set of rhythms generated by a network are compared using a metric that quantifies the functional differences among them. The rhythms are then classified according to their location in a metric space. Examples are given, and biological implications are discussed.

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50

Ueda, Kanji, Jari Vaario, and Kazuhiro Ohkura. "Modelling of Biological Manufacturing Systems for Dynamic Reconfiguration." CIRP Annals 46, no. 1 (1997): 343–46. http://dx.doi.org/10.1016/s0007-8506(07)60839-7.

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