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Journal articles on the topic 'Biological modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Roenneberg, Till, Elaine Jane Chua, Ric Bernardo, and Eduardo Mendoza. "Modelling Biological Rhythms." Current Biology 18, no. 17 (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 (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 (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

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4

Keane, John. "Tools for modelling biological processes." Nature 421, no. 6923 (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 (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 (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 (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’

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8

Purmal', A. P., and L. A. Nikolaev. "The Modelling of Biological Catalysts." Russian Chemical Reviews 54, no. 5 (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 (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

Tarasich, Yu H., and H. O. Soloshenko. "Neurosymbolic Approach in Biological Research." Control Systems and Computers, no. 4 (304) (2023): 39–51. http://dx.doi.org/10.15407/csc.2023.04.039.

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Modelling and studying the processes and methods of intercellular and intracellular signalling cascades regulation involved in the process of programmed cell death and searching for substances capable of influencing the activation or inhibition of the process of cell apoptosis and the methods of their transportation to a given cell, is one of the numerous actual and open issues in biological research. A safe and fast method for this that does not require research on living organisms is computer molecular modelling. Many approaches and tools have been proposed and developed in the last decade.

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12

Vincent, Julian F. V. "Biomimetic modelling." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1437 (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

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13

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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14

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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15

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

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16

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

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17

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

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18

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

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19

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

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20

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

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21

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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22

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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23

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

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24

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

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25

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

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26

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

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27

Mrs., Manju Devi*1 &. Mr. Sanjay Chopra2. "MATHEMATICAL MODELLING THROUGH DIFFERENCE EQUATIONS IN GENETICS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 83–85. https://doi.org/10.5281/zenodo.822996.

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The real world is infinitely complicated. To penetrate that complexity using model building. We must learn to make reasonable, simplifying assumptions about complex process. Suppose the age of a father is four times the age of the son and after five years, the age of the father will be only three times the age of the son. We have to find their ages. So, let x be the age of the father and y be the age of son. Then the data of problem gives – These two equations give a mathematical model of the biological situation, so that the biological problem of ages is reduced to the mathematical problem of

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28

Lesk, Victor, Jan Taubert, Chris Rawlings, Stuart Dunbar, and Stephen Muggleton. "WIBL: Workbench for Integrative Biological Learning." Journal of Integrative Bioinformatics 8, no. 2 (2011): 14–22. http://dx.doi.org/10.1515/jib-2011-156.

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Summary The construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. We present WIBL, a visualization and model development environment initially geared towards logic-based modelling of biological systems using integrated datasets. WIBL combines data integration, visualisation and modelling in a single portalbased workbench providing a comprehensive solution for interdisciplinary systems biology projects.

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29

Sobhy, Zineb, Mustapha Krim, Jamal Inchaouh, et al. "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 (2019): 1060–66. http://dx.doi.org/10.5373/jardcs/v11sp11/20193137.

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30

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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31

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 (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

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32

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

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33

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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34

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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35

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

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36

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

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37

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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38

Coveney, Peter V., and Philip W. Fowler. "Modelling biological complexity: a physical scientist's perspective." Journal of The Royal Society Interface 2, no. 4 (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

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39

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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40

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

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41

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

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42

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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43

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

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44

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 (2010): 431–40. http://dx.doi.org/10.1007/s12524-010-0045-3.

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45

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

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46

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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47

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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48

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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49

Kennaway, Richard, and Enrico Coen. "Volumetric finite-element modelling of biological growth." Open Biology 9, no. 5 (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 examp

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50

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

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