Relevant bibliographies by topics / Mathematical modelling/biology

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Mathematical modelling/biology'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Mathematical modelling/biology"

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Vasieva, Olga, Manan'Iarivo Rasolonjanahary, and Bakhtier Vasiev. "Mathematical modelling in developmental biology." REPRODUCTION 145, no. 6 (2013): R175—R184. http://dx.doi.org/10.1530/rep-12-0081.

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In recent decades, molecular and cellular biology has benefited from numerous fascinating developments in experimental technique, generating an overwhelming amount of data on various biological objects and processes. This, in turn, has led biologists to look for appropriate tools to facilitate systematic analysis of data. Thus, the need for mathematical techniques, which can be used to aid the classification and understanding of this ever-growing body of experimental data, is more profound now than ever before. Mathematical modelling is becoming increasingly integrated into biological studies
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Tomlin, Claire J., and Jeffrey D. Axelrod. "Biology by numbers: mathematical modelling in developmental biology." Nature Reviews Genetics 8, no. 5 (2007): 331–40. http://dx.doi.org/10.1038/nrg2098.

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Jäger, Willi. "Mathematical Modelling in Chemistry and Biology." Interdisciplinary Science Reviews 11, no. 2 (1986): 181–88. http://dx.doi.org/10.1179/isr.1986.11.2.181.

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Chaplain, M. A. J. "Multiscale mathematical modelling in biology and medicine." IMA Journal of Applied Mathematics 76, no. 3 (2011): 371–88. http://dx.doi.org/10.1093/imamat/hxr025.

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Lei, Jinzhi. "Viewpoints on modelling: Comments on "Achilles and the tortoise: Some caveats to mathematical modelling in biology"." Mathematics in Applied Sciences and Engineering 1, no. 1 (2020): 85–90. http://dx.doi.org/10.5206/mase/10267.

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Mathematical modelling has been proven to be useful in understanding some problems from biological science, provided that it is used properly. However, it has also attracted some criticisms as partially presented in a recent opinion article \cite{Gilbert2018} from biological community. This note intends to clarify some confusion and misunderstanding in regard to mathematically modelling by commenting on those critiques raised in \cite{Gilbert2018}, with a hope of initiating some further discussion so that both applied mathematicians and biologist can better use mathematical modelling and bette
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Middleton, A., M. Owen, M. Bennett, and J. King. "Mathematical modelling of gibberellinsignalling." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 150, no. 3 (2008): S46. http://dx.doi.org/10.1016/j.cbpa.2008.04.023.

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Butler, George, Jonathan Rudge, and Philip R. Dash. "Mathematical modelling of cell migration." Essays in Biochemistry 63, no. 5 (2019): 631–37. http://dx.doi.org/10.1042/ebc20190020.

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Abstract The complexity of biological systems creates challenges for fully understanding their behaviour. This is particularly true for cell migration which requires the co-ordinated activity of hundreds of individual components within cells. Mathematical modelling can help understand these complex systems by breaking the system into discrete steps which can then be interrogated in silico. In this review, we highlight scenarios in cell migration where mathematical modelling can be applied and discuss what types of modelling are most suited. Almost any aspect of cell migration is amenable to ma
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Divya, B., and K. Kavitha. "A REVIEW ON MATHEMATICAL MODELLING IN BIOLOGY AND MEDICINE." Advances in Mathematics: Scientific Journal 9, no. 8 (2020): 5869–79. http://dx.doi.org/10.37418/amsj.9.8.54.

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MacArthur, B. D., C. P. Please, M. Taylor, and R. O. C. Oreffo. "Mathematical modelling of skeletal repair." Biochemical and Biophysical Research Communications 313, no. 4 (2004): 825–33. http://dx.doi.org/10.1016/j.bbrc.2003.11.171.

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Shone, John. "Working at the biology/mathematics interface: mathematical modelling and sixth form biology." International Journal of Mathematical Education in Science and Technology 19, no. 4 (1988): 501–9. http://dx.doi.org/10.1080/0020739880190402.

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Dissertations / Theses on the topic "Mathematical modelling/biology"

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Hunt, Gordon S. "Mathematical modelling of pattern formation in developmental biology." Thesis, Heriot-Watt University, 2013. http://hdl.handle.net/10399/2706.

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The transformation from a single cell to the adult form is one of the remarkable wonders of nature. However, the fundamental mechanisms and interactions involved in this metamorphic change still remain elusive. Due to the complexity of the process, researchers have attempted to exploit simpler systems and, in particular, have focussed on the emergence of varied and spectacular patterns in nature. A number of mathematical models have been proposed to study this problem with one of the most well studied and prominent being the novel concept provided by A.M. Turing in 1952. Turing's simple yet el
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Nurtay, Anel. "Mathematical modelling of pathogen specialisation." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667178.

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L’aparició de nous virus causants de malalties està estretament lligada a l’especialització de subpoblacions virals cap a nous tipus d’amfitrions. La modelització matemàtica proporciona un marc quantitatiu que pot ajudar amb la predicció de processos a llarg termini com pot ser l’especialització. A causa de la naturalesa complexa que presenten les interaccions intra i interespecífiques en els processos evolutius, cal aplicar eines matemàtiques complexes, com ara l’anàlisi de bifurcacions, al estudiar dinàmiques de població. Aquesta tesi desenvolupa una jerarquia de models de població per poder
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Rata, Scott. "Mathematical modelling of mitotic controls." Thesis, University of Oxford, 2018. https://ora.ox.ac.uk/objects/uuid:7bef862c-2025-4494-a2bb-4fe93584d92a.

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The mitotic cell cycle is fundamental to eukaryotic life. In mitosis, replicated chromosomes are segregated to form two new nuclei. This is essential to ensure the maintenance of chromosome number between parent and daughter cells. In higher eukaryotes, numerous cytological changes occur to facilitate the separation of the genetic material: the nuclear envelope breaks down, the mitotic spindle assembles, and the cell rounds-up. There is a well-conserved control network that regulates these processes to bring about the entry into mitosis, the separation of the genetic material, and the reversal
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Catt, Christopher Joseph. "Mathematical modelling of tissue metabolism and growth." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/176447/.

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The work presented in this thesis is concerned with modelling the growth of tissue constructs, with particular focus on the effects the local micro environment has on the cell cycle and metabolism. We consider two cases; multicellular tumour spheroids and orthopaedic tissue constructs. This thesis is divided into two parts. In the first part we will present a multispecies model of an avascular tumour that studies how a cell’s metabolism affects the cell cycle, spheroid growth and the mechanical forces that arise during growth. The second part consists of a study of the growth of an engineered
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Modhara, Sunny. "Mathematical modelling of vascular development in zebrafish." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29125/.

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The Notch signalling pathway is pivotal in ensuring that the processes of arterial specification, angiogenic sprouting and haematopoietic stem cell (HSC) specification are correctly carried out in the dorsal aorta (DA), a primary arterial blood vessel in developing vertebrate embryos. Using the zebrafish as a model organism, and additional experimental observations from mouse and cell line models to guide mathematical modelling, this thesis aims to better understand the mechanisms involved in the establishment of a healthy vasculature in the growing embryo. We begin by studying arterial and HS
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Durney, Clinton H. "A Two-Component Model For Bacterial Chemotaxis." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366312981.

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Moi, Adriano. "Mathematical modelling of integrin-like receptors systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11255/.

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Nel presente lavoro, ho studiato e trovato le soluzioni esatte di un modello matematico applicato ai recettori cellulari della famiglia delle integrine. Nel modello le integrine sono considerate come un sistema a due livelli, attivo e non attivo. Quando le integrine si trovano nello stato inattivo possono diffondere nella membrana, mentre quando si trovano nello stato attivo risultano cristallizzate nella membrana, incapaci di diffondere. La variazione di concentrazione nella superficie cellulare di una sostanza chiamata attivatore dà luogo all’attivazione delle integrine. Inoltre, questi ete
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Bakshi, Suruchi D. "Mathematical modelling of Centrosomin incorporation in Drosophila centrosomes." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:baefde65-bc38-4a11-bd92-e2e4cccad784.

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Centrosomin (Cnn) is an integral centrosomal protein in Drosophila with orthologues in several species, including humans. The human orthologue of Cnn is required for brain development with Cnn hypothesised to play a similar role in Drosophila. Control of Cnn incorporation into centrosomes is crucial for controlling asymmetric division in certain types of Drosophila stem cells. FRAP experiments on Cnn show that Cnn recovers in a pe- culiar fashion, which suggest that Cnn may be incorporated closest to the centrioles and then spread radially outward, either diffusively or ad- vectively. The aim
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Chapman, Lloyd A. C. "Mathematical modelling of cell growth in tissue engineering bioreactors." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:7c9ee131-7d9b-4e5d-8534-04a059fbd039.

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Expanding cell populations extracted from patients or animals is essential to the process of tissue engineering and is commonly performed in laboratory incubation devices known as bioreactors. Bioreactors provide a means of controlling the chemical and mechanical environment experienced by cells to ensure growth of a functional population. However, maximising this growth requires detailed knowledge of how cell proliferation is affected by bioreactor operating conditions, such as the flow rate of culture medium into the bioreactor, and by the initial cell seeding distribution in the bioreactor.
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Osman, Mohamad Hussein. "Mathematical modelling and simulation of biofuel cells." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/363762/.

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Bio-fuel cells are driven by diverse and abundant bio-fuels and biological catalysts. The production/consumption cycle of bio-fuels is considered to be carbon neutral and, in principle, more sustainable than that of conventional fuel cells. The cost benefits over traditional precious-metal catalysts, and the mild operating conditions represent further advantages. It is important that mathematical models are developed to reduce the burden on laboratory based testing and accelerate the development of practical systems. In this study, recent key developments in bio-fuel cell technology are review
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Books on the topic "Mathematical modelling/biology"

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Morris, Richard J., ed. Mathematical Modelling in Plant Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99070-5.

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Brebbia, C. A. Modelling in medicine and biology. WIT Press, 2011.

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

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

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Demin, Oleg. Kinetic modelling in systems biology. Chapman & Hall/CRC, 2009.

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Nisbet, R. M. Modelling fluctuating populations. Blackburn Press, 2003.

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Nisbet, R. M. Modelling fluctuating populations. University Microfilms International, 1992.

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Daley, Daryl J. Epidemic modelling: An introduction. Cambridge University Press, 1999.

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Wilkinson, Darren James. Stochastic modelling for systems biology. Chapman & Hall/CRC Press, 2007.

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Stochastic modelling for systems biology. Taylor & Francis, 2006.

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Book chapters on the topic "Mathematical modelling/biology"

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Pérez-Escobar, José Antonio. "Mathematical Modelling and Teleology in Biology." In Proceedings of the Canadian Society for History and Philosophy of Mathematics/ Société canadienne d’histoire et de philosophie des mathématiques. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31298-5_4.

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Wedgwood, K. C. A., J. Tabak, and K. Tsaneva-Atanasova. "Modelling Ion Channels." In Mathematical Modelling in Plant Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99070-5_3.

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Ahmed, Danish A., Joseph D. Bailey, Sergei V. Petrovskii, and Michael B. Bonsall. "Mathematical Bases for 2D Insect Trap Counts Modelling." In Computational Biology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69951-2_6.

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Slavova, Angela. "CNN modelling in biology, physics and ecology." In Mathematical Modelling: Theory and Applications. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0261-4_4.

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Greulich, Philip. "Mathematical Modelling of Clonal Stem Cell Dynamics." In Computational Stem Cell Biology. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9224-9_5.

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Deinum, Eva E., and Bela M. Mulder. "Modelling the Plant Microtubule Cytoskeleton." In Mathematical Modelling in Plant Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99070-5_4.

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D’Agostino, Daniele, Andrea Clematis, Emanuele Danovaro, and Ivan Merelli. "Modelling of Protein Surface Using Parallel Heterogeneous Architectures." In Mathematical Models in Biology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23497-7_14.

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Dumond, Mathilde, and Arezki Boudaoud. "Physical Models of Plant Morphogenesis." In Mathematical Modelling in Plant Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99070-5_1.

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Živković, Daniel, and Aurélien Tellier. "All But Sleeping? Consequences of Soil Seed Banks on Neutral and Selective Diversity in Plant Species." In Mathematical Modelling in Plant Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99070-5_10.

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Blyth, M. G., and R. J. Morris. "Fluid Transport in Plants." In Mathematical Modelling in Plant Biology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99070-5_2.

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Conference papers on the topic "Mathematical modelling/biology"

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Sabrekov, A. F., M. V. Glagolev, I. E. Terentieva, and S. Y. Mochenov. "Identification of soil methane oxidation activity by inverse modelling." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.77.

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Iaparov, B. I., and V. V. Ivchenko. "Mathematical modelling of the ryanodine receptor’s activation time dependence on magnesium." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.69.

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Sabrekov, A. F., M. V. Glagolev, and I. E. Terentieva. "Measuring methane flux from the soil by inverse modelling using adjoint equations." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.85.

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Romanov, M. S., and V. B. Masterov. "Modelling of the Steller’s Sea Eagle Population: Stable Demographic Structure vs. Transient Dynamics." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.99.

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Okenov, A. O., B. Ya Iaparov, and A. S. Moskvin. "Kramers rate theory as a starting point for modelling temperature effects in TRP channels." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.38.

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Polyakov, M. V., and A. S. Astakhov. "Mathematical Processing and Computer Analysis of Data from Numerical Modelling of Radiothermometric Medical Examinations." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.23.

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Nikolaev, G. I., N. A. Shuldov, I. P. Bosko, A. I. Anischenko, A. V. Tuzikov, and A. M. Andrianov. "Application of Deep Learning and Molecular Modelling Methods to Identify Potential HIV-1 Entry Inhibitors." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.6.

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van Leeuwen, Ingeborg M. M., Helen M. Byrne, Matthew D. Johnston, et al. "Modelling multiscale aspects of colorectal cancer." In INTERNATIONAL CONFERENCE ON MATHEMATICAL BIOLOGY 2007: ICMB07. AIP, 2008. http://dx.doi.org/10.1063/1.2883865.

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BANERJEE, M., M. BENMIR, and V. VOLPERT. "MULTI-SCALE MODELLING IN CELL DYNAMICS." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814667944_0020.

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MONDAINI, RUBEM P., and ROBERTO A. C. PRATA. "GEODESIC CURVES FOR BIOMOLECULAR STRUCTURE MODELLING." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812812339_0018.

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Journal articles Dissertations / Theses Books
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