Relevant bibliographies by topics / Biological modelling

Academic literature on the topic 'Biological modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Biological modelling"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Biological modelling"

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Lemon, A. P. "Modelling the biological membrane." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760688.

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Lumley, James Andrew. "Molecular modelling of biological activity." Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393752.

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Luo, Yang. "Stochastic modelling in biological systems." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610145.

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Billing, Alison Emslie. "Modelling techniques for biological systems." Master's thesis, University of Cape Town, 1987. http://hdl.handle.net/11427/21917.

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The objective of this investigation has been to develop and evaluate techniques which are appropriate to the modelling and simulation of biological reaction system behaviour. The model used as the basis for analysis of modelling and simulation techniques is a reduced version of the biological model proposed by the IAWPRC Task Group for mathematical modell ing in wastewater treatment design. This limited model has the advantage of being easily manageable in terms of analysis and presentation of the simulation techniQues whilst at the same time incorporating a range of features encountered with biological growth applications in general. Because a model may incorporate a number of different components and large number of biological conversion processes, a convenient method of presentation was found to be a matrix format. The matrix representation ensures clarity as to what compounds, processes and react ion terms are to be incorporated and allows easy comparison of different models. In addition, it facilitates transforming the model into a computer program. Simulation of the system response first involves specifying the reactor configuration and flow patterns. With this information fixed, mass balances for each compound in each reactor can be completed. These mass balances constitute a set of simultaneous non-linear differential and algebraic eQuations which, when solved, characterise the system behaviour.
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Cotton-Barratt, Rebecca. "Modelling biological form in evolution." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/70973/.

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How are processes working at the individual level, the species level and the macro-ecological level connected? This thesis explores the theoretical and structural constraints on biological evolution. It does this by developing an evolutionary program to model biological form. This development was necessary as the existing models of evolution are poorly suited to modelling morphological constraint. The model of biological form developed in this thesis uses graphs to abstractly represent organisms and the relationships of their internal structure. We show that by increasing the number of degrees of freedom, or by increasing the ruggedness of the fitness landscape, higher levels of diversity are supported - particularly when there is strong directional selection. We explore whether meta-regulation is bounded in the model by using an analytical framework. We show that there is no analytical steady state, but that one can be induced in the model by selection effects. We find that a mixed strategy between increasing object complexity and increasing hierarchical complexity maximises the average degree of a vertex. This agrees with the evolutionary history of meta-regulation. We claim that the macro-ecological response to environmental perturbation is determined by both the characteristic time scale of mutation and the time scale of the environmental change. We show that for high amplitude changes the system can adapt provide the mutation time scale is smaller than the environmental change. We also show that low amplitude environmental changes cause rapid turnovers in species' diversity. Finally, we show that mass extinctions can be the result of species' interactions and background rates of extinction, and do not need large external perturbations to occur. This, combined with the results above, suggests that many of the trends seen over geologically long time periods can be explained as a result of the interacting processes at the individual and species level.
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Djordjilovic, Vera. "Graphical modelling of biological pathways." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424702.

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Biological pathways underlie the basic functions of a living cell. They are complex diagrams featuring genes, proteins and other small molecules, showing how they work together to achieve a particular biological effect. From a technical point of view, they are networks represented through a graph where genes and their connections are, respectively, nodes and edges of a graph. The main research objective of this thesis is to develop a framework for simulating effects of gene silencing. To this end, we propose a three step approach. First, we refine the structure of a pathway via our CK2 algorithm. Next, we assess the uncertainty in the refined structure. Finally, we simulate gene silencing through intervention analysis in causal graphical models. The proposed approach showed promising results when applied to the problem of predicting the effect of the knockdown of the nkd gene in Drosophila Melanogaster.
I pathway biologici sono alla base del funzionamento delle cellule viventi. Tali pathway sono diagrammi complessi che coinvolgono geni, proteine e altre piccole molecole, mostrando come essi svolgano un ruolo congiunto nel raggiungimento di uno specifico effetto biologico. Da un punto di vista tecnico, questi network sono rappresentati mediante diagrammi dove i geni e le loro connessioni sono, rispettivamente, nodi e archi. Il principale obiettivo di questa ricerca è sviluppare una tecnica per simulare gli effetti del silenziamento genico. A tal fine, proponiamo un approccio basato su tre passi. Nel primo passo, raffiniamo la struttura di un pathway attraverso il nostro algoritmo CK2. In seguito, nel secondo passo, valutiamo l'incertezza nella struttura raffinata. Infine, nel terzo passo, simuliamo il silenziamento genico tramite intervention analysis nei modelli grafici causali. L'approccio proposto mostra risultati promettenti se applicato al problema della previsione dell'effetto del silenziamento del gene nkd della Drosophila Melanogaster.
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Hodgkinson, Arran. "Mathematical Methods for Modelling Biological Heterogeneity." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS119.

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Les processus biologiques sont des phénomènes complexes, multi-échelles, présentant une hétérogénéité importante à travers l’espace, la structure et la fonction. De plus, ils impliquent des événements fortement corrélés et présentent des boucles de rétroaction à travers les échelles. Dans cette thèse, nous utilisons des représentations spatio-structuro-temporelles en grande dimension pour étudier l'hétérogénéité biologique à travers l'espace, la fonction biologique et le temps, et appliquons cette méthode à divers problèmes importants en biologie et en clinique.Nous commençons par introduire un nouveau cadre spatio-structuro-temporel, basé sur équations aux dérivées partielles, pour le cas d’un système biologique dont la fonction dépend de la dynamique dans le temps et l’espace des récepteurs membranaires, des ligands et du métabolisme. Afin d’étudier les solutions de ces équations, nous utilisons un schéma numérique de différences finies ainsi que divers résultats analytiques. Pour tester la validité de nos approches numériques nous prouvons un théorème sur la stabilité de notre schéma.Le cancer est un problème croissant pour la population mondiale, car ses taux d'incidence et sa résistance aux médicaments augmentent. D’abord nous modélisons l’invasion du cancer du sein agressif via sa capacité à produire des enzymes dégradant la matrice extracellulaire, et nous montrons la génération de structures spatiales anatomo-pathologiques difficiles à enlever par la chirurgie. Ensuite, nous développons des modèles mathématiques de tumeurs résistantes au traitement et appliquons ces modèles à la résistance aux thérapies ciblées (inhibiteurs de BRAF et de MEK) du mélanome cutané. Nous constatons que les tumeurs développent une résistance à la fois à travers des processus d'adaptations génétiques ou par le remodelage de leur métabolisme, mais montrons que seules les tumeurs métaboliquement plastiques manifestent une re-sensibilisation à ces thérapies. Enfin, via une approche basée sur des données d’expression en cellule unique (RNA-seq), nous montrons que la dynamique spatiale contribue à l'hétérogénéité tumorale et à la résistante aux traitements de façon liée au statut prolifératif des cellules cancéreuses.Nous appliquons nos méthodes à deux autres systèmes. Dans le contexte de la réponse immunitaire à l’infection virale, nous étudions la production et la dynamique spatiale de l’interféron (IFN) et l’apparent paradoxe de la conservation de molécules d’IFN avec affinités faibles et fortes. Nous constatons que les molécules IFN de faible affinité sont plus capables de se propager dans l'espace, alors que les molécules de haute affinité sont capables de maintenir le signal localement. L’addition de ligands de faible affinité à un système ne comprenant que des ligands de moyenne ou grande affinité peut entraîner une diminution de la charge virale d’environ 23%. Ensuite, nous explorons le contexte de la sélection sexuelle de l'apparence masculine dans l'évolution darwinienne. Nous constatons que les systèmes biologiques conservent les traits sélectionnés sexuellement, même si cela entraîne une diminution générale de la population.Enfin, nous introduisons deux autres techniques de modélisation: pour augmenter la dimensionnalité de notre approche, nous développons une approche pseudo-spectrale basée sur les polynômes de Chebyshev et l’appliquons au même scénario de résistance aux médicaments phénotypiques que ci-dessus. Ensuite, pour étudier un scénario coopératif dans lequel des cellules cancéreuses prolifératives et invasives sont co-injectées, induisant des comportements invasifs dans les cellules prolifératives, nous développons une nouvelle méthode de simulation combinant des automates cellulaires et systèmes d’agents. Nous trouvons que cette méthode est capable de reproduire les résultats de l'expérience de coinjection et d'autres expériences dans lesquelles des cellules ont été placées dans des micropistes de collagène
Biological processes are complex, multi-scale phenomena displaying extensive heterogeneity across space, structure, and function. Moreover, these events are highly correlated and involve feedback loops across scales, with nuclear transcription being effected by protein concentrations and vice versa, presenting a difficulty in representing these through existing mathematical approaches. In this thesis we use higher-dimensional spatio-structuro-temporal representations to study biological heterogeneity through space, biological function, and time and apply this method to various scenarios of significance to the biological and clinical communities.We begin by deriving a novel spatio-structuro-temporal, partial differential equation framework for the general case of a biological system whose function depends upon dynamics in time, space, surface receptors, binding ligands, and metabolism. In order to simulate solutions for this system, we present a numerical finite difference scheme capable of this and various analytic results connected with this system, in order to clarify the validity of our predictions. In addition to this, we introduce a new theorem establishing the stability of the central differences scheme.Despite major recent clinical advances, cancer incidence continues to rise and resistance to newly synthesised drugs represents a major health issue. To tackle this problem, we begin by investigating the invasion of aggressive breast cancer on the basis of its ability to produce extracellular matrix degrading enzymes, finding that the cancer produced a surgically challenging morphology. Next, we produce a novel structure in which models of cancer resistance can be established and apply this computational model to study genetic and phenotypic modes of resistance and re-sensitisation to targeted therapies (BRAF and MEK inhibitors). We find that both genetic and phenotypic heterogeneity drives resistance but that only the metabolically plastic, phenotypically resistant, tumour cells are capable of manifesting re-sensitisation to these therapies. We finally use a data-driven approach for single-cell RNA-seq analysis and show that spatial dynamics fuel tumour heterogeneity, contributing to resistance to treatment accordingly with the proliferative status of cancer cells.In order to expound this method, we look at two further systems: To investigate a case where cell-ligand interaction is particularly important, we take the scenario in which interferon (IFN) is produced upon infection of the cell by a virus and ask why biological systems evolve and retain multiple different affinities of IFN. We find that low affinity IFN molecules are more capable of propagating through space; high affinity molecules are capable of sustaining the signal locally; and that the addition of low affinity ligands to a system with only medium or high affinity ligands can lead to a ~23% decrease in viral load. Next, we explore the non-spatial, structuro-temporal context of male elaboration sexual and natural selection in Darwinian evolution. We find that biological systems will conserve sexually selected traits even in the event where this leads to an overall population decrease, contrary to natural selection.Finally, we introduce two further modelling techniques: To increase the dimensionality of our approach, we develop a pseudo-spectral Chebyshev polynomial-based approach and apply this to the same scenario of phenotypic drug resistance as above. Next, to deal with one scenario in which proliferative and invasive cancer cells are co-injected, inducing invasive behaviours in the proliferative cells, we develop a novel agent-based, cellular automaton method and associated analytic theorems for generating numerical solutions. We find that this method is capable of reproducing the results of the co-injection experiment and further experiments, wherein cells migrate through artificially produced collagen microtracks
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Fear, Elise Carolyn. "Modelling biological cells exposed to electric fields." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ32685.pdf.

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Liu, Dianbo. "Modelling biological networks : topology, dynamics and generation." Thesis, University of Dundee, 2017. https://discovery.dundee.ac.uk/en/studentTheses/8ab98533-d17f-4ea5-adb7-62b23d1e42bc.

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Lumbers, Jeremy. "Rotating biological contactors : mechanisms, modelling and design." Thesis, Imperial College London, 1988. http://hdl.handle.net/10044/1/47161.

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Books on the topic "Biological modelling"

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Mosekilde, Erik, and Ole G. Mouritsen, eds. Modelling the Dynamics of Biological Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79290-8.

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Mastebroek, Henk A. K., and Johan E. Vos, eds. Plausible Neural Networks for Biological Modelling. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0674-3.

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Sukumaran, Muralidharan. Modelling the biological activity of s.coelicolor. Manchester: UMIST, 1995.

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K, Mastebroek Henk A., and Vos Johan E, eds. Plausible neural networks for biological modelling. Dordrecht: Kluwer Academic Publishers, 2001.

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R, Carson Ewart, ed. Mathematical modelling of dynamic biological systems. 2nd ed. Letchworth, Hertfordshire, England: Research Studies Press, 1985.

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Mastebroek, Henk A. K. Plausible Neural Networks for Biological Modelling. Dordrecht: Springer Netherlands, 2001.

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Wilkinson, Darren James. Stochastic modelling for systems biology. 2nd ed. Boca Raton: Taylor & Francis, 2012.

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R, Mondaini, and Pardalos P. M. 1954-, eds. Mathematical modelling of biosystems. New York: Springer, 2008.

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Baláž, Štefan. Modelling kinetics of biological activity in xenobiotics. Bratislava: Veda, 1990.

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M, Henze, ed. Biological wastewater treatment: Principles, modelling and design. London: IWA Pub., 2008.

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Book chapters on the topic "Biological modelling"

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Tjelmeland, Sigurd, and Bjarte Bogstad. "Biological Modelling." In Contributions to Economics, 69–91. Heidelberg: Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-99793-8_3.

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Maurer, Richard I., and Christopher A. Reynolds. "Modelling biological systems." In Chemical Modelling, 199–238. Cambridge: Royal Society of Chemistry, 2007. http://dx.doi.org/10.1039/9781847553317-00199.

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Deakin, Michael A. B. "Modelling Biological Systems." In Dynamics of Complex Interconnected Biological Systems, 2–16. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4684-6784-0_1.

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Cotton-Barratt, Rebecca, and Markus Kirkilionis. "Modelling Biological Form." In Proceedings of the European Conference on Complex Systems 2012, 511–22. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00395-5_64.

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Kaandorp, Jaap A. "Methods for Modelling Biological Objects." In Fractal Modelling, 7–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-57922-6_2.

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Dean, Jeffrey, and Holk Cruse. "Modelling the Control of Walking in Insects." In Biological Motion, 200–219. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51664-1_14.

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Mees, Alistair I. "Modelling Complex Systems." In Dynamics of Complex Interconnected Biological Systems, 104–24. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4684-6784-0_6.

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Clarke, Dave, David Costa, and Farhad Arbab. "Modelling Coordination in Biological Systems." In Leveraging Applications of Formal Methods, 9–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11925040_2.

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Furze, James N., Q. Zhu, J. Hill, and F. Qiao. "Biological Modelling for Sustainable Ecosystems." In Mathematical Advances Towards Sustainable Environmental Systems, 9–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43901-3_2.

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Boulier, François. "Differential Elimination and Biological Modelling." In Gröbner Bases in Symbolic Analysis, edited by Markus Rosenkranz and Dongming Wang, 109–38. Berlin, Boston: DE GRUYTER, 2007. http://dx.doi.org/10.1515/9783110922752.109.

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Conference papers on the topic "Biological modelling"

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STOOP, RUEDI, and STEFANO LECCHINI. "BIOLOGICAL NEURAL NETWORKS: MODELING AND MEASUREMENTS." In Modelling Biomedical Signals. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778055_0009.

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Abdullah, Shaikh. "Dispersion modelling for biological threat." In 2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2015. http://dx.doi.org/10.1109/ibcast.2015.7058488.

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Tregubov, Vladimir, and Gerasim Krivovichev. "Mathematical modelling of biological mobility." In 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA). IEEE, 2014. http://dx.doi.org/10.1109/icctpea.2014.6893353.

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Tregubov, Vladimir. "Mathematical modelling of biological liquids." In 2014 International Conference on Computer Technologies in Physical and Engineering Applications (ICCTPEA). IEEE, 2014. http://dx.doi.org/10.1109/icctpea.2014.6893354.

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Schnase, J. L., and J. J. Leggett. "Computational hypertext in biological modelling." In the second annual ACM conference. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74224.74240.

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Shiny, S., K. V. Shihabudheen, and Anish Gopinath. "Modelling of Biological Lower Extremity." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993402.

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Fernandez, D. R., J. M. G. Chamizo, F. M. Perez, A. S. Paya, F. M. Perez, and A. S. Paya. "Robust Modelling of Biological Neuroregulators." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617100.

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Soetaert, Karline, Dick van Oevelen, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Modelling Marine Biological and Biogeochemical Data." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636664.

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Paltanea, Marius, Sabin Tabirca, Ernesc Scheiber, and Mark Tangney. "Logarithmic Growth in Biological Processes." In 2010 12th International Conference on Computer Modelling and Simulation. IEEE, 2010. http://dx.doi.org/10.1109/uksim.2010.29.

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Wilczyński, Bartek. "A stochastic extension of R. Thomas regulatory network modelling." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-19.

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Reports on the topic "Biological modelling"

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Комарова, Олена Володимирівна, and Альберт Армаїсович Азарян. Computer Simulation of Biological Processes at the High School. CEUR Workshop Proceedings (CEUR-WS.org), 2018. http://dx.doi.org/10.31812/123456789/2695.

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Abstract. Research goals: the necessity of study in high school of the law of Hardy – Weinberg as one of the fundamental genetic laws was justified. The peculiarities of using the method of model experiment in the study of the genetic and evolutionary processes in populations with the use of computer technology. Object of research: computer simulation of population genetic structure. Subject of research: computer simulation of genetic and evolutionary processes in ideal and real populations. Research methods: pedagogical experiment (survey), analysis of scientific publications on the use of the high school method of modelling genetic and evolutionary processes in populations, computer simulation. Results of the research: a web page for processing by the pupils of the modelling results of genetic and evolutionary processes in populations was created.
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Комарова, Олена Володимирівна, and Альберт Арамаїсович Азарян. Computer Simulation of Biological Processes at the High School. CEUR-WS.org, 2018. http://dx.doi.org/10.31812/123456789/2656.

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Research goals: the necessity of study in high school of the law of Hardy – Weinberg as one of the fundamental genetic laws was justified. The peculiarities of using the method of model experiment in the study of the genetic and evolutionary processes in populations with the use of computer technology. Object of research: computer simulation of population genetic structure. Subject of research: computer simulation of genetic and evolutionary processes in ideal and real populations. Research methods: pedagogical experiment (survey), analysis of scientific publications on the use of the high school method of modelling genetic and evolutionary processes in populations, computer simulation. Results of the research: a web page for processing by the pupils of the modelling results of genetic and evolutionary processes in populations was created.
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3

Burkimsher, Marion. Modelling biological birth order and comparison with census parity data in Switzerland: a report to complement the Swiss data in the Human Fertility Collection (HFC). Rostock: Max Planck Institute for Demographic Research, October 2011. http://dx.doi.org/10.4054/mpidr-tr-2011-005.

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4

Savosko, V., I. Komarova, Yu Lykholat, E. Yevtushenko, and T. Lykholat. Predictive model of heavy metals inputs to soil at Kryvyi Rih District and its use in the training for specialists in the field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4511.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values (mg ⋅ m2/year): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline "Computer modelling in biology").
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5

Савосько, Василь Миколайович, Ірина Олександрівна Комарова, Юрій Васильович Лихолат, Едуард Олексійович Євтушенко,, and Тетяна Юріївна Лихолат. Predictive Model of Heavy Metals Inputs to Soil at Kryvyi Rih District and its Use in the Training for Specialists in the Field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4266.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values ( mg ∙ m ଶ year ⁄ ): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline “Computer modelling in biology”).
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6

Taucher, Jan, and Markus Schartau. Report on parameterizing seasonal response patterns in primary- and net community production to ocean alkalinization. OceanNETs, November 2021. http://dx.doi.org/10.3289/oceannets_d5.2.

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We applied a 1-D plankton ecosystem-biogeochemical model to assess the impacts of ocean alkalinity enhancement (OAE) on seasonal changes in biogeochemistry and plankton dynamics. Depending on deployment scenarios, OAE should theoretically have variable effects on pH and seawater pCO2, which might in turn affect (a) plankton growth conditions and (b) the efficiency of carbon dioxide removal (CDR) via OAE. Thus, a major focus of our work is how different magnitudes and temporal frequencies of OAE might affect seasonal response patterns of net primary productivity (NPP), ecosystem functioning and biogeochemical cycling. With our study we aimed at identifying a parameterization of how magnitude and frequency of OAE affect net growth rates, so that these effects could be employed for Earth System Modell applications. So far we learned that a meaningful response parameterization has to resolve positive and negative anomalies that covary with temporal shifts. As to the intricacy of the response patterns, the derivation of such parameterization is work in progress. However, our study readily provides valuable insights to how OAE can alter plankton dynamics and biogeochemistry. Our modelling study first focuses at a local site where time series data are available (European Station for Time series in the Ocean Canary Islands ESTOC), including measurements of pH, concentrations of total alkalinity, dissolved inorganic carbon (DIC), chlorophyll-a and dissolved inorganic nitrogen (DIN). These observational data were made available by Andres Cianca (personal communication, PLOCAN, Spain), Melchor Gonzalez and Magdalena Santana Casiano (personal communication, Universidad de Las Palmas de Gran Canaria). The choice of this location was underpinned by the fact that the first OAE mesocosm experiment was conducted on the Canary Island Gran Canaria, which will facilitate synthesizing our modelling approach with experimental findings. For our simulations at the ESTOC site in the Subtropical North Atlantic we found distinct, non-linear responses of NPP to different temporal modes of alkalinity deployment. In particular, phytoplankton bloom patterns displayed pronounced temporal phase shifts and changes in their amplitude. Notably, our simulations suggest that OAE can have a slightly stimulating effect on NPP, which is however variable, depending on the magnitude of OAE and the temporal mode of alkalinity addition. Furthermore, we find that increasing alkalinity perturbations can lead to a shift in phytoplankton community composition (towards coccolithophores), which even persists after OAE has stopped. In terms of CDR, we found that a decrease in efficiency with increasing magnitude of alkalinity addition, as well as substantial differences related to the timing of addition. Altogether, our results suggest that annual OAE during the right season (i.e. physical and biological conditions), could be a reasonable compromise in terms of logistical feasibility, efficiency of CDR and side-effects on marine biota. With respect to transferability to global models, the complex, non-linear responses of biological processes to OAE identified in our simulations do not allow for simple parameterizations that can easily adapted. Dedicated future work is required to transfer the observed responses at small spatiotemporal scales to the coarser resolution of global models.
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