Academic literature on the topic 'Biological mathematic'
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Journal articles on the topic "Biological mathematic"
Šorgo, Andrej. "Connecting Biology and Mathematics: First Prepare the Teachers." CBE—Life Sciences Education 9, no. 3 (September 2010): 196–200. http://dx.doi.org/10.1187/cbe.10-03-0014.
Full textSari, Putri Permata, and Zubaidah Amir MZ. "Pengembangan Lembar Kerja Siswa (LKS) Berbasis Model Pembelajaran Realistic Mathematic Education (RME) Pada Materi Bangun Ruang Sisi Datar." JURING (Journal for Research in Mathematics Learning) 4, no. 3 (November 2, 2021): 269. http://dx.doi.org/10.24014/juring.v4i3.14024.
Full textXIE, FEI, PHILLIP C. Y. SHEU, ARTHUR LANDER, and VITTORIO CRISTINI. "SEMANTIC ANALYSIS AND SYNTHESIS OF COMPLEX BIOLOGICAL SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 15, no. 03 (June 2005): 547–69. http://dx.doi.org/10.1142/s0218194005002415.
Full textLi, Xin Yu, Bing Liu, Si Ze Li, and Shu Sen Liu. "Numerical Simulations of Biological Droplet Transport in an Indoor Environment." Advanced Materials Research 356-360 (October 2011): 862–66. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.862.
Full textZhang, Cheng, Ning Wang, Yu Xu, Hor‐Yue Tan, and Yibin Feng. "Identification of Key Contributive Compounds in a Herbal Medicine: A Novel Mathematic—Biological Evaluation Approach." Advanced Theory and Simulations 4, no. 6 (May 4, 2021): 2000279. http://dx.doi.org/10.1002/adts.202000279.
Full textWahyuni, Hanny Indrat, Retno Iswarin Pujaningsih, and Padwi Anwar Sayekti. "Kajian Nilai Energi Metabolis Biji Sorghum Melalui Teknologi Sangrai Pada Ayam Petelur Periode Afkir." Jurnal Agripet 8, no. 1 (April 1, 2008): 25–30. http://dx.doi.org/10.17969/agripet.v8i1.605.
Full textYu, Zhong Hai, Tian Chen, Di Shi Liu, and Jing Wang. "Study on the Optimized Cutting Parameters for the Nuclear Channel Head." Applied Mechanics and Materials 121-126 (October 2011): 3534–40. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3534.
Full textHayya, Adieba Warda. "The Creative Thinking Skill of Biological Learning Students at Candi Baru High School." Journal Of Biology Education 4, no. 2 (November 18, 2021): 164. http://dx.doi.org/10.21043/jobe.v4i2.10187.
Full textLiang, Xin Li, Dan Lv, Juan Luo, Xue Jing Guan, and Zheng Gen Liao. "Application Research of Lipid Microsphere Biological Carrier Material on Prescription of Tanshinone II a Lipid Microsphere by Central Composite Design-Response Surface Method." Advanced Materials Research 1120-1121 (July 2015): 834–41. http://dx.doi.org/10.4028/www.scientific.net/amr.1120-1121.834.
Full textFilipchuk, O. V., and O. M. Gurov. "PECULIARITIES OF APPLYING BALLISTIC GEL AS A SIMULATOR OF HUMAN BIOLOGICAL TISSUES." Theory and Practice of Forensic Science and Criminalistics 15 (November 30, 2016): 367–73. http://dx.doi.org/10.32353/khrife.2015.46.
Full textDissertations / Theses on the topic "Biological mathematic"
AFFILI, ELISA. "EVOLUTION EQUATIONS WITH APPLICATIONS TO POPULATION DYNAMICS." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/820854.
Full textEl-Hachem, Maud. "Mathematical models of biological invasion." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/232864/1/Maud_El-Hachem_Thesis.pdf.
Full textHodgkinson, Arran. "Mathematical Methods for Modelling Biological Heterogeneity." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS119.
Full textBiological 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
Ohlsson, Henrik. "Mathematical Analysis of a Biological Clock Model." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6750.
Full textHave you thought of why you get tired or why you get hungry? Something in your body keeps track of time. It is almost like you have a clock that tells you all those things.
And indeed, in the suparachiasmatic region of our hypothalamus reside cells which each act like an oscillator, and together form a coherent circadian rhythm to help our body keep track of time. In fact, such circadian clocks are not limited to mammals but can be found in many organisms including single-cell, reptiles and birds. The study of such rhythms constitutes a field of biology, chronobiology, and forms the background for my research and this thesis.
Pioneers of chronobiology, Pittendrigh and Aschoff, studied biological clocks from an input-output view, across a range of organisms by observing and analyzing their overt activity in response to stimulus such as light. Their study was made without recourse to knowledge of the biological underpinnings of the circadian pacemaker. The advent of the new biology has now made it possible to "break open the box" and identify biological feedback systems comprised of gene transcription and protein translation as the core mechanism of a biological clock.
My research has focused on a simple transcription-translation clock model which nevertheless possesses many of the features of a circadian pacemaker including its entrainability by light. This model consists of two nonlinear coupled and delayed differential equations. Light pulses can reset the phase of this clock, whereas constant light of different intensity can speed it up or slow it down. This latter property is a signature property of circadian clocks and is referred to in chronobiology as "Aschoff's rule". The discussion in this thesis focus on develop a connection and also a understanding of how constant light effect this clock model.
Magi, Ross. "Dynamic behavior of biological membranes." Thesis, The University of Utah, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680576.
Full textBiological membranes are important structural units in the cell. Composed of a lipid bilayer with embedded proteins, most exploration of membranes has focused on the proteins. While proteins play a vital role in membrane function, the lipids themselves can behave in dynamic ways which affect membrane structure and function. Furthermore, the dynamic behavior of the lipids can affect and be affected by membrane geometry. A novel fluid membrane model is developed in which two different types of lipids flow in a deforming membrane, modelled as a two-dimensional Riemannian manifold that resists bending. The two lipids behave like viscous Newtonian fluids whose motion is determined by realistic physical forces. By examining the stability of various shapes, it is shown that instability may result if the two lipids forming the membrane possess biophysical qualities, which cause them to respond differently to membrane curvature. By means of numerical simulation of a simplified model, it is shown that this instability results in curvature induced phase separation. Applying the simplified model to the Golgi apparatus, it is hypothesized that curvature induced phase separation may occur in a Golgi cisterna, aiding in the process of protein sorting.
In addition to flowing tangentially in the membrane, lipids also flip back and forth between the two leaflets in the bilayer. While traditionally assumed to occur very slowly, recent experiments have indicated that lipid flip-flop may occur rapidly. Two models are developed that explore the effect of rapid flip-flop on membrane geometry and the effect of a pH gradient on the distribution of charged lipids in the leaflets of the bilayer. By means of a stochastic model, it is shown that even the rapid flip-flop rates observed are unlikely to be significant inducers of membrane curvature. By means of a nonlinear Poisson- Boltzmann model, it is shown that pH gradients are unlikely to be significant inducers of bilayer asymmetry under physiological conditions.
Chindelevitch, Leonid Alexandrovich. "Extracting information from biological networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/64607.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 175-194).
Systems biology, the study of biological systems in a holistic manner, has been catalyzed by a dramatic improvement in experimental techniques, coupled with a constantly increasing availability of biological data. The representation and analysis of this heterogeneous data is facilitated by the powerful abstraction of biological networks. This thesis examines several types of these networks and looks in detail at the kind of information their analysis can yield. The first part discusses protein interaction networks. We introduce a new algorithm for the pairwise alignment of these networks. We show that these alignments can provide important clues to the function of proteins as well as insights into the evolutionary history of the species under examination. The second part discusses regulatory networks. We present an approach for validating putative drug targets based on the information contained in these networks. We show how this approach can also be used to discover drug targets. The third part discusses metabolic networks. We provide new insights into the structure of constraint-based models of cell metabolism and describe a methodology for performing a complete analysis of a metabolic network. We also present an implementation of this methodology and discuss its application to a variety of problems related to the metabolism of bacteria. The final part describes an application of our methodology to Mycobacterium tuberculosis, the pathogen responsible for almost 2 million deaths around the world every year. We introduce a method for reconciling metabolic network reconstructions and apply it to merge the two published networks for tuberculosis. We analyze the merged network and show how it can be refined based on available experimental data to improve its predictive power. We conclude with a list of potential drug targets.
by Leonid Alexandrovich Chindelevitch.
Ph.D.
Altschul, Stephen Frank. "Aspects of biological sequence comparison." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/102708.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Bibliography: leaves 165-168.
by Stephen Frank Altschul.
Ph.D
Basse, Britta. "Case studies in mathematical modelling for biological conservation." Thesis, University of Canterbury. Mathematics & Statistics, 1999. http://hdl.handle.net/10092/4804.
Full textGrau, Ribes Alexis. "Mathematical models of transport phenomena in biological tissues." Doctoral thesis, Universite Libre de Bruxelles, 2020. https://dipot.ulb.ac.be/dspace/bitstream/2013/303032/4/contents.pdf.
Full textDoctorat en Sciences
info:eu-repo/semantics/nonPublished
Orme, Belinda Abigail Amanda. "Biological mixing and chaos." Thesis, University of Birmingham, 2002. http://etheses.bham.ac.uk//id/eprint/7637/.
Full textBooks on the topic "Biological mathematic"
K, Maini Philip, and Othmer H. G. 1943-, eds. Mathematical models for biological pattern formulation: Frontiers in biological mathematics. New York: Springer, 2001.
Find full textBill, Broadhurst, and Hladky S. B, eds. Mathematics for biological scientists. New York, NY: Garland Science, 2009.
Find full text1960-, Deutsch Andreas, ed. Mathematical modeling of biological systems. Boston: Birkhauser, 2007.
Find full textLewis, Mark A., Sergei V. Petrovskii, and Jonathan R. Potts. The Mathematics Behind Biological Invasions. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32043-4.
Full textBanks, H. Thomas. Mathematical and experimental modeling of physical and biological processes. Boca Raton: Chapman & Hall/CRC, 2009.
Find full textFriedman, Avner, and Chiu-Yen Kao. Mathematical Modeling of Biological Processes. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08314-8.
Full textOhira, Toru, and Tohru Uzawa, eds. Mathematical Approaches to Biological Systems. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55444-8.
Full textDeutsch, Andreas, Lutz Brusch, Helen Byrne, Gerda de Vries, and Hanspeter Herzel, eds. Mathematical Modeling of Biological Systems. Boston, MA: Birkhäuser Boston, 2007. http://dx.doi.org/10.1007/978-0-8176-4558-8.
Full textBook chapters on the topic "Biological mathematic"
Khyar, Omar, Adil Meskaf, and Karam Allali. "Mathematic Analysis of a SIHV COVID-19 Pandemic Model Taking Into Account a Vaccination Strategy." In Trends in Biomathematics: Stability and Oscillations in Environmental, Social, and Biological Models, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12515-7_11.
Full textKimmel, Marek, and David E. Axelrod. "Biological Background." In Interdisciplinary Applied Mathematics, 19–36. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1559-0_2.
Full textKimmel, Marek, and David E. Axelrod. "Biological Background." In Interdisciplinary Applied Mathematics, 19–31. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21639-1_2.
Full textBritton, Nicholas Ferris. "Biological Motion." In Springer Undergraduate Mathematics Series, 147–73. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0049-2_5.
Full textVoit, Eberhard O. "The Mathematics of Biological Systems." In A First Course in Systems Biology, 83–134. Second edition. | New York : Garland Science, 2017.: Garland Science, 2017. http://dx.doi.org/10.4324/9780203702260-4.
Full textStamova, Ivanka, and Gani Stamov. "Impulsive Biological Models." In CMS Books in Mathematics, 41–112. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28061-5_3.
Full textLancaster, H. O. "Mathematics and Epidemiology." In Quantitative Methods in Biological and Medical Sciences, 139–51. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2658-1_12.
Full textMurray, J. D. "Biological Oscillators and Switches." In Interdisciplinary Applied Mathematics, 218–56. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-0-387-22437-4_7.
Full textLewis, Mark A., Sergei V. Petrovskii, and Jonathan R. Potts. "Dynamics of Biological Invasions." In Interdisciplinary Applied Mathematics, 19–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32043-4_2.
Full textEfendiev, Messoud. "Biological Background." In Mathematical Modeling of Mitochondrial Swelling, 27–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99100-9_2.
Full textConference papers on the topic "Biological mathematic"
Zakiyah, R. Nur, Ibrohim, and Hadi Suwono. "The influence of science, technology, engineering, mathematic (STEM) based biology learning through inquiry learning models towards students’ critical thinking skills and mastery of biological concepts." In THE 4TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCE EDUCATION (ICoMSE) 2020: Innovative Research in Science and Mathematics Education in The Disruptive Era. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0043361.
Full textSeipel, Justin. "Mechanistic Model-Based Method for Bio-Inspired Design and Education." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-64595.
Full textGaver, Donald P. "The Use of Collaborative Learning in Biomedical Engineering Education." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/bed-23018.
Full textTregubov, 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.
Full textTregubov, 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.
Full textJheng, Yu-Chen, and Chi-Lun Lin. "Fabrication and Testing of Breast Tissue-Mimicking Phantom for Needle Biopsy Cutting: A Pilot Study." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3505.
Full text"Model Composition for Biological Mathematical Systems." In International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004699202170224.
Full textTregubov, Vladimir. "Mathematical modeling of biological fluid flows." In 2014 2nd International Conference on Emission Electronics (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/emission.2014.6893982.
Full textEtheridge, Alison M. "Drift, draft and structure: some mathematical models of evolution." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-7.
Full textSoetaert, 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.
Full textReports on the topic "Biological mathematic"
Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, October 2020. http://dx.doi.org/10.47496/nl.blog.09.
Full textHeinz, Kevin, Itamar Glazer, Moshe Coll, Amanda Chau, and Andrew Chow. Use of multiple biological control agents for control of western flower thrips. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7613875.bard.
Full textComputational Biology: Development in the Field of Medicine. Science Repository, April 2021. http://dx.doi.org/10.31487/sr.blog.31.
Full textIncongruity between biological and chronologic age among the pupils of sports schools and the problem of group lessons effectiveness at the initial stage of training in Greco-Roman wrestling. Aleksandr S. Kuznetsov, March 2021. http://dx.doi.org/10.14526/2070-4798-2021-16-1-19-23.
Full textMethodology of sports working capacity level increase in basketball players on the basis of stimulation and rehabilitation means. Viktor V. Andreev, Igor E. Konovalov, Dmitriy S. Andreev, Aleksandr I. Morozov, March 2021. http://dx.doi.org/10.14526/2070-4798-2021-16-1-5-11.
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