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Journal articles on the topic 'Biological modelling'

To see the other types of publications on this topic, follow the link: Biological modelling.

Author: Grafiati

Published: 4 June 2021

Last updated: 11 March 2023

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1

Roenneberg, Till, Elaine Jane Chua, Ric Bernardo, and Eduardo Mendoza. "Modelling Biological Rhythms." Current Biology 18, no. 17 (September 2008): R826—R835. http://dx.doi.org/10.1016/j.cub.2008.07.017.

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2

MADDEN, T. D., M. J. HOPE, and P. R. CULLIS. "Modelling the biological membrane." Biochemical Society Transactions 15, no. 1 (February 1, 1987): 75–77. http://dx.doi.org/10.1042/bst0150075.

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3

Vanhooren, Henk, Jurgen Meirlaen, Youri Amerlinck, Filip Claeys, Hans Vangheluwe, and Peter A. Vanrolleghem. "WEST: modelling biological wastewater treatment." Journal of Hydroinformatics 5, no. 1 (January 1, 2003): 27–50. http://dx.doi.org/10.2166/hydro.2003.0003.

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Modelling is considered to be an inherent part of the design and operation of a wastewater treatment system. The models used in practice range from conceptual models and physical design models (laboratory-scale or pilot-scale reactors) to empirical or mechanistic mathematical models. These mathematical models can be used during the design, operation and optimisation of a wastewater treatment system. To do so, a good software tool is indispensable. WEST is a general modelling and simulation environment and can, together with a model base, be used for this task. The model base presented here is specific for biological wastewater treatment and is written in MSL-USER. In this high-level object-oriented language, the dynamics of systems can be represented along with symbolic information. In WEST's graphical modelling environment, the physical layout of the plant can be rebuilt, and each building block can be linked to a specific model from the model base. The graphical information is then combined with the information in the model base to produce MSL-EXEC code, which can be compiled with a C++ compiler. In the experimentation environment, the user can design different experiments, such as simulations and optimisations of, for instance, designs, controllers and model fits to data (calibration).

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4

Keane, John. "Tools for modelling biological processes." Nature 421, no. 6923 (February 2003): 573. http://dx.doi.org/10.1038/421573b.

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5

Mandel, J. J., W. Dubitzky, and N. M. Palfreyman. "Modelling codependence in biological systems." IET Systems Biology 1, no. 1 (January 1, 2007): 18–32. http://dx.doi.org/10.1049/iet-syb:20060002.

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6

Cooling, M. T., E. J. Crampin, and P. Hunter. "Modelling biological modularity with CellML." IET Systems Biology 2, no. 2 (March 1, 2008): 73–79. http://dx.doi.org/10.1049/iet-syb:20070020.

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7

Finkelstein, M. S. "Reliability modelling for biological ageing." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 222, no. 1 (March 1, 2008): 1–6. http://dx.doi.org/10.1243/1748006xjrr65.

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Some stochastic approaches to modelling biological ageing are studied. The assumption is made that a random resource is acquired by an organism at birth. Failure (death) occurs when the accumulated wear exceeds this initial resource, modelled by discrete or continuous random variables. Deterioration in repairable objects is also considered. Two models are discussed. The first one is an imperfect repair model. It is shown that under certain assumptions the accumulated damage in this model is bounded. The second model is based on the shot noise process and takes into account the ‘healing effect’, when an increment of damage after each shock is decreasing with time.

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8

Purmal', A. P., and L. A. Nikolaev. "The Modelling of Biological Catalysts." Russian Chemical Reviews 54, no. 5 (May 31, 1985): 466–75. http://dx.doi.org/10.1070/rc1985v054n05abeh003077.

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9

Osis, J., and L. Beghi. "Topological Modelling of Biological Systems." IFAC Proceedings Volumes 30, no. 2 (March 1997): 337–42. http://dx.doi.org/10.1016/s1474-6670(17)44593-9.

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10

Stein, Gillian Z. "Modelling counts in biological populations." Mathematical and Computer Modelling 12, no. 9 (1989): 1183. http://dx.doi.org/10.1016/0895-7177(89)90259-8.

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11

Vincent, Julian F. V. "Biomimetic modelling." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1437 (September 29, 2003): 1597–603. http://dx.doi.org/10.1098/rstb.2003.1349.

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Biomimetics is seen as a path from biology to engineering. The only path from engineering to biology in current use is the application of engineering concepts and models to biological systems. However, there is another pathway: the verification of biological mechanisms by manufacture, leading to an iterative process between biology and engineering in which the new understanding that the engineering implementation of a biological system can bring is fed back into biology, allowing a more complete and certain understanding and the possibility of further revelations for application in engineering. This is a pathway as yet unformalized, and one that offers the possibility that engineers can also be scientists.

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12

Hertog, M. L. A. T. M., J. Lammerteyn, M. Desmet, N. Scheerlinck, and B. Nicolaï. "INCORPORATING BIOLOGICAL VARIATION IN POSTHARVEST MODELLING." Acta Horticulturae, no. 682 (June 2005): 843–50. http://dx.doi.org/10.17660/actahortic.2005.682.109.

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13

Barlow, N. D., and S. L. Goldson. "Modelling impact of biological control agents." Proceedings of the New Zealand Weed and Pest Control Conference 43 (January 8, 1990): 282–83. http://dx.doi.org/10.30843/nzpp.1990.43.10883.

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14

Ch'ng, Eugene. "Modelling the Adaptability of Biological Systems." Open Cybernetics & Systemics Journal 1, no. 1 (December 26, 2007): 13–20. http://dx.doi.org/10.2174/1874110x00701010013.

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15

Norris, Vic, Maurice Engel, and Maurice Demarty. "Modelling Biological Systems with Competitive Coherence." Advances in Artificial Neural Systems 2012 (June 20, 2012): 1–20. http://dx.doi.org/10.1155/2012/703878.

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Many living systems, from cells to brains to governments, are controlled by the activity of a small subset of their constituents. It has been argued that coherence is of evolutionary advantage and that this active subset of constituents results from competition between two processes, a Next process that brings about coherence over time, and a Now process that brings about coherence between the interior and the exterior of the system at a particular time. This competition has been termed competitive coherence and has been implemented in a toy-learning program in order to clarify the concept and to generate—and ultimately test—new hypotheses covering subjects as diverse as complexity, emergence, DNA replication, global mutations, dreaming, bioputing (computing using either the parts of biological system or the entire biological system), and equilibrium and nonequilibrium structures. Here, we show that a program using competitive coherence, Coco, can learn to respond to a simple input sequence 1, 2, 3, 2, 3, with responses to inputs that differ according to the position of the input in the sequence and hence require competition between both Next and Now processes.

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16

Paton, Ray. "Modelling biological processes using simple matrices." Journal of Biological Education 25, no. 1 (March 1991): 37–43. http://dx.doi.org/10.1080/00219266.1991.9655172.

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17

Paton, R. C. "Diagrammatic representations for modelling biological knowledge." Biosystems 66, no. 1-2 (June 2002): 43–53. http://dx.doi.org/10.1016/s0303-2647(02)00032-1.

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18

Wiebe, Kevin James, and Anup Basu. "Modelling ecologically specialized biological visual systems." Pattern Recognition 30, no. 10 (October 1997): 1687–703. http://dx.doi.org/10.1016/s0031-3203(96)00160-4.

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19

Chaouiya, C. "Petri net modelling of biological networks." Briefings in Bioinformatics 8, no. 4 (March 29, 2007): 210–19. http://dx.doi.org/10.1093/bib/bbm029.

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20

Ciobanu, Gabriel, and Maciej Koutny. "Modelling and analysis of biological systems." Theoretical Computer Science 431 (May 2012): 2–3. http://dx.doi.org/10.1016/j.tcs.2011.12.064.

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21

Ciocchetta, Federica, and Maria Luisa Guerriero. "Modelling Biological Compartments in Bio-PEPA." Electronic Notes in Theoretical Computer Science 227 (January 2009): 77–95. http://dx.doi.org/10.1016/j.entcs.2008.12.105.

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22

Hunter, Peter. "Modelling of Biological Systems Themed Issue." Experimental Physiology 91, no. 2 (March 2006): 283–84. http://dx.doi.org/10.1113/expphysiol.2006.033308.

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23

Walicki, Edward, and Anna Walicka. "Mathematical modelling of some biological bearings." Smart Materials and Structures 9, no. 3 (June 1, 2000): 280–83. http://dx.doi.org/10.1088/0964-1726/9/3/305.

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24

Deakin, Michael A. B. "Catastrophe modelling in the biological sciences." Acta Biotheoretica 38, no. 1 (March 1990): 3–22. http://dx.doi.org/10.1007/bf00047270.

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25

Morozov, Andrew. "Modelling Biological Evolution: Developing Novel Approaches." Bulletin of Mathematical Biology 81, no. 11 (October 15, 2019): 4620–24. http://dx.doi.org/10.1007/s11538-019-00670-5.

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26

Sobhy, Zineb, Mustapha Krim, Jamal Inchaouh, Ismail Ghazi, Meriem Tantaoui, Said Ouaskit, and Hamid Chakir. "Contribution to the Modelling of the Irradiation of the Biological Medium by Protons." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 20, 2019): 1060–66. http://dx.doi.org/10.5373/jardcs/v11sp11/20193137.

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27

Appleton, David, and E. Renshaw. "Modelling Biological Populations in Space and Time." Applied Statistics 42, no. 2 (1993): 411. http://dx.doi.org/10.2307/2986249.

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28

Görgün, E., E. Ubay Çokgör, D. Orhon, F. Germirli, and N. Artan. "Modelling biological treatability for meat processing effluent." Water Science and Technology 32, no. 12 (December 1, 1995): 43–52. http://dx.doi.org/10.2166/wst.1995.0455.

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Biological treatability of major agro-industries wastewaters, such as meat processing effluents can only be evaluated with specific emphasis on slowly biodegradable substrate and using a multi-component modelling approach. This paper reviews the framework of the endogenous decay model and summarizes the necessary COD fractionation and the kinetic information to be incorporated in this model as applied to a meat processing effluent. Model interpretations of the respirometric experiments are used to define the fate of slowly biodegradable COD. Behavior of this wastewater in continuous activated sludge systems is studied by model simulations based upon experimental results.

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29

Speirs, Douglas C., and E. Renshaw. "Modelling Biological Populations in Space and Time." Journal of Applied Ecology 31, no. 3 (August 1994): 596. http://dx.doi.org/10.2307/2404457.

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30

Vepa, Ranjan. "Modelling and Estimation of Chaotic Biological Neurons." IFAC Proceedings Volumes 42, no. 7 (2009): 27–32. http://dx.doi.org/10.3182/20090622-3-uk-3004.00008.

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31

Henze, M., M. C. M. van Loosdrecht, G. A. Ekama, and D. Brdjanovic. "Biological Wastewater Treatment: Principles, Modelling and Design." Water Intelligence Online 7 (December 30, 2015): 9781780401867. http://dx.doi.org/10.2166/9781780401867.

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32

Zeh, Judy, and Eric Renshaw. "Modelling Biological Populations in Space and Time." Journal of the American Statistical Association 90, no. 430 (June 1995): 800. http://dx.doi.org/10.2307/2291097.

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33

Kemp, A. W., and E. Renshaw. "Modelling Biological Populations in Space and Time." Biometrics 50, no. 1 (March 1994): 315. http://dx.doi.org/10.2307/2533231.

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34

Capatana, Cristina. "Cellular Automaton Modelling of Biological Pattern Formation." Acta Endocrinologica (Bucharest) 4, no. 1 (2008): 126. http://dx.doi.org/10.4183/aeb.2008.126.

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35

Coveney, Peter V., and Philip W. Fowler. "Modelling biological complexity: a physical scientist's perspective." Journal of The Royal Society Interface 2, no. 4 (June 2, 2005): 267–80. http://dx.doi.org/10.1098/rsif.2005.0045.

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We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the integration of molecular and more coarse-grained representations of matter. The scope of such integrative approaches to complex systems research is circumscribed by the computational resources available. Computational grids should provide a step jump in the scale of these resources; we describe the tools that RealityGrid, a major UK e-Science project, has developed together with our experience of deploying complex models on nascent grids. We also discuss the prospects for mathematical approaches to reducing the dimensionality of complex networks in the search for universal systems-level properties, illustrating our approach with a description of the origin of life according to the RNA world view.

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36

Hannah, CG. "Future directions in modelling physicalbiological interactions." Marine Ecology Progress Series 347 (October 11, 2007): 301–6. http://dx.doi.org/10.3354/meps06987.

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37

Boudreau, Bernard P., and Roberta L. Marinelli. "A modelling study of discontinuous biological irrigation." Journal of Marine Research 52, no. 5 (September 1, 1994): 947–68. http://dx.doi.org/10.1357/0022240943076902.

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38

Mann, A. T., and T. Stephenson. "Modelling biological aerated filters for wastewater treatment." Water Research 31, no. 10 (October 1997): 2443–48. http://dx.doi.org/10.1016/s0043-1354(97)00095-x.

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39

Barrett, A. N. "Mathematical modelling of biological systems by microcomputer." Mathematical Modelling 7, no. 9-12 (1986): 1601–11. http://dx.doi.org/10.1016/0270-0255(86)90092-8.

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40

Chaouiya, Claudine, Elisabeth Remy, and Denis Thieffry. "Petri net modelling of biological regulatory networks." Journal of Discrete Algorithms 6, no. 2 (June 2008): 165–77. http://dx.doi.org/10.1016/j.jda.2007.06.003.

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41

Nandy, S., and S. P. S. Kushwaha. "Geospatial modelling of biological richness in Sunderbans." Journal of the Indian Society of Remote Sensing 38, no. 3 (September 2010): 431–40. http://dx.doi.org/10.1007/s12524-010-0045-3.

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42

ter Steeg, P. F., and J. E. Ueckert. "Debating the biological reality of modelling preservation." International Journal of Food Microbiology 73, no. 2-3 (March 2002): 409–14. http://dx.doi.org/10.1016/s0168-1605(01)00665-1.

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43

Climent, J., L. Basiero, R. Martínez-Cuenca, J. G. Berlanga, B. Julián-López, and S. Chiva. "Biological reactor retrofitting using CFD-ASM modelling." Chemical Engineering Journal 348 (September 2018): 1–14. http://dx.doi.org/10.1016/j.cej.2018.04.058.

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44

Bilotsky, Y., and M. Gasik. "Modelling of poro-visco-elastic biological systems." Journal of Physics: Conference Series 633 (September 21, 2015): 012134. http://dx.doi.org/10.1088/1742-6596/633/1/012134.

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45

Herrero, Miguel A., and José M. López. "Bone Formation: Biological Aspects and Modelling Problems." Journal of Theoretical Medicine 6, no. 1 (2005): 41–55. http://dx.doi.org/10.1080/10273660412331336883.

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In this work we succintly review the main features of bone formation in vertebrates. Out of the many aspects of this exceedingly complex process, some particular stages are selected for which mathematical modelling appears as both feasible and desirable. In this way, a number of open questions are formulated whose study seems to require interaction among mathematical analysis and biological experimentation.

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46

Kennaway, Richard, and Enrico Coen. "Volumetric finite-element modelling of biological growth." Open Biology 9, no. 5 (May 2019): 190057. http://dx.doi.org/10.1098/rsob.190057.

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Differential growth is the driver of tissue morphogenesis in plants, and also plays a fundamental role in animal development. Although the contributions of growth to shape change have been captured through modelling tissue sheets or isotropic volumes, a framework for modelling both isotropic and anisotropic volumetric growth in three dimensions over large changes in size and shape has been lacking. Here, we describe an approach based on finite-element modelling of continuous volumetric structures, and apply it to a range of forms and growth patterns, providing mathematical validation for examples that admit analytic solution. We show that a major difference between sheet and bulk tissues is that the growth of bulk tissue is more constrained, reducing the possibility of tissue conflict resolution through deformations such as buckling. Tissue sheets or cylinders may be generated from bulk shapes through anisotropic specified growth, oriented by a polarity field. A second polarity field, orthogonal to the first, allows sheets with varying lengths and widths to be generated, as illustrated by the wide range of leaf shapes observed in nature. The framework we describe thus provides a key tool for developing hypotheses for plant morphogenesis and is also applicable to other tissues that deform through differential growth or contraction.

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47

Rickaby, S. R., and N. H. Scott. "Multicyclic modelling of softening in biological tissue." IMA Journal of Applied Mathematics 79, no. 6 (February 12, 2013): 1107–25. http://dx.doi.org/10.1093/imamat/hxt008.

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48

Zhang, Shujun, Kevin Hapeshi, and Ashok K. Bhattacharya. "3D Modelling of Biological Systems for Biomimetics." Journal of Bionic Engineering 1, no. 1 (March 2004): 20–40. http://dx.doi.org/10.1007/bf03399451.

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49

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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50

Brodziak, Jon. "Modelling biological populations in space and time." Mathematical Biosciences 114, no. 2 (April 1993): 249–50. http://dx.doi.org/10.1016/0025-5564(93)90079-p.

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