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Artykuły w czasopismach na temat "Biological Mathematics"
Doube, Michael. "Mathematics for Biological Scientists". Journal of Anatomy 216, nr 4 (kwiecień 2010): 542. http://dx.doi.org/10.1111/j.1469-7580.2009.01195.x.
Pełny tekst źródłaB. Khyade, Vitthalrao, i Hanumant V. Wanve. "Review on Use of Mathematics for Progression of Biological Sciences". International Academic Journal of Innovative Research 05, nr 01 (12.06.2018): 301–38. http://dx.doi.org/10.9756/iajir/v5i1/1810004.
Pełny tekst źródłaThompson, Paul. "Mathematics in the biological sciences". International Studies in the Philosophy of Science 6, nr 3 (styczeń 1992): 241–48. http://dx.doi.org/10.1080/02698599208573434.
Pełny tekst źródłaBELLOMO, N., i F. BREZZI. "MATHEMATICS AND COMPLEXITY IN BIOLOGICAL SCIENCES". Mathematical Models and Methods in Applied Sciences 21, supp01 (kwiecień 2011): 819–24. http://dx.doi.org/10.1142/s0218202511005374.
Pełny tekst źródłaKarl, Lila. "DNA computing: Arrival of biological mathematics". Mathematical Intelligencer 19, nr 2 (marzec 1997): 9–22. http://dx.doi.org/10.1007/bf03024425.
Pełny tekst źródłaPenny, D. "Doom01: biological mathematics in evolutionary processes". Trends in Ecology & Evolution 16, nr 6 (1.06.2001): 275–76. http://dx.doi.org/10.1016/s0169-5347(01)02166-8.
Pełny tekst źródłaMarland, Eric, Katrina M. Palmer i René A. Salinas. "Biological Applications in the Mathematics Curriculum". PRIMUS 18, nr 1 (17.01.2008): 85–100. http://dx.doi.org/10.1080/10511970701744984.
Pełny tekst źródłaŠorgo, Andrej. "Connecting Biology and Mathematics: First Prepare the Teachers". CBE—Life Sciences Education 9, nr 3 (wrzesień 2010): 196–200. http://dx.doi.org/10.1187/cbe.10-03-0014.
Pełny tekst źródłaDurán, Pablo A., i Jill A. Marshall. "Mathematics for biological sciences undergraduates: a needs assessment". International Journal of Mathematical Education in Science and Technology 50, nr 6 (26.10.2018): 807–24. http://dx.doi.org/10.1080/0020739x.2018.1537451.
Pełny tekst źródłaNaidoo, Jayaluxmi. "Integrating Indigenous Knowledge and Culturally based Activities in South African Mathematics Classrooms". African Journal of Teacher Education 10, nr 2 (11.12.2021): 17–36. http://dx.doi.org/10.21083/ajote.v10i2.6686.
Pełny tekst źródłaRozprawy doktorskie na temat "Biological Mathematics"
Magi, Ross. "Dynamic behavior of biological membranes". Thesis, The University of Utah, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3680576.
Pełny tekst źródłaBiological 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.
Pełny tekst źródłaCataloged 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.
Pełny tekst źródłaThis 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
Orme, Belinda Abigail Amanda. "Biological mixing and chaos". Thesis, University of Birmingham, 2002. http://etheses.bham.ac.uk//id/eprint/7637/.
Pełny tekst źródłaTucker, George Jay. "Statistical methods to infer biological interactions". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89874.
Pełny tekst źródłaThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
169
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 153-170).
Biological systems are extremely complex, and our ability to experimentally measure interactions in these systems is limited by inherent noise. Technological advances have allowed us to collect unprecedented amounts of raw data, increasing the need for computational methods to disentangle true interactions from noise. In this thesis, we focus on statistical methods to infer two classes of important biological interactions: protein-protein interactions and the link between genotypes and phenotypes. In the first part of the thesis, we introduce methods to infer protein-protein interactions from affinity purification mass spectrometry (AP-MS) and from luminescence-based mammalian interactome mapping (LUMIER). Our work reveals novel context dependent interactions in the MAPK signaling pathway and insights into the protein homeostasis machinery. In the second part, we focus on methods to understand the link between genotypes and phenotypes. First, we characterize the effects of related individuals on standard association statistics for genome-wide association studies (GWAS) and introduce a new statistic that corrects for relatedness. Then, we introduce a statistically powerful association testing framework that corrects for confounding from population structure in large scale GWAS. Lastly, we investigate regularized regression for phenotype prediction from genetic data.
by George Jay Tucker.
Ph. D.
Breitsch, Nathan W. "Techniques for the Study of Biological Coupled Oscillator Systems". Ohio University Honors Tutorial College / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1399892563.
Pełny tekst źródłaMontenegro-Johnson, Thomas D. "Microscopic swimming in biological fluids". Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4220/.
Pełny tekst źródłaSeier, Edith, i Karl H. Joplin. "Introduction to STATISTICS in a Biological Context". Digital Commons @ East Tennessee State University, 2011. http://amzn.com/1463613377.
Pełny tekst źródłaCaberlin, Martin D. "Stiff ordinary and delay differential equations in biological systems". Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29416.
Pełny tekst źródłaYu, Yun William. "Compressive algorithms for search and storage in biological data". Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112879.
Pełny tekst źródłaCataloged from PDF version of thesis.
Includes bibliographical references (pages 187-197).
Disparate biological datasets often exhibit similar well-defined structure; efficient algorithms can be designed to exploit this structure. In this doctoral thesis, we present a framework for similarity search based on entropy and fractal dimension; here, we prove that a clustered search algorithm scales in time with metric entropy number of covering hyperspheres-if the fractal dimension is low. Using these ideas, entropy-scaling versions of standard bioinformatics search tools can be designed, including for small-molecule, metagenomics, and protein structure search. This 'compressive acceleration' approach taking advantage of redundancy and sparsity in biological data can be leveraged also for next-generation sequencing (NGS) read mapping. By pairing together a clustered grouping over similar reads and a homology table for similarities in the human genome, our CORA framework can accelerate all-mapping by several orders of magnitude. Additionally, we also present work on filtering empirical base-calling quality scores from Next Generation Sequencing data. By using the sparsity of k-mers of sufficient length in the human genome and imposing a human prior through the use of frequent k-mers in a large corpus of human DNA reads, we are able to quickly discard over 90% of the information found in those quality scores while retaining or even improving downstream variant-calling accuracy. This filtering step allows for fast lossy compression of quality scores.
by Yun William Yu.
Ph. D.
Książki na temat "Biological Mathematics"
K, Maini Philip, i Othmer H. G. 1943-, red. Mathematical models for biological pattern formulation: Frontiers in biological mathematics. New York: Springer, 2001.
Znajdź pełny tekst źródłaBill, Broadhurst, i Hladky S. B, red. Mathematics for biological scientists. New York, NY: Garland Science, 2009.
Znajdź pełny tekst źródłaLewis, Mark A., Sergei V. Petrovskii i Jonathan R. Potts. The Mathematics Behind Biological Invasions. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32043-4.
Pełny tekst źródłaCampbell, June Mundy. Laboratory mathematics: Medical and biological applications. Wyd. 4. St. Louis: Mosby, 1990.
Znajdź pełny tekst źródła1933-, Campbell June Mundy, red. Laboratory mathematics: Medical and biological applications. Wyd. 5. St. Louis: Mosby, 1997.
Znajdź pełny tekst źródłaSegal, Rebecca, Blerta Shtylla i Suzanne Sindi, red. Using Mathematics to Understand Biological Complexity. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57129-0.
Pełny tekst źródłaRadunskaya, Ami, Rebecca Segal i Blerta Shtylla, red. Understanding Complex Biological Systems with Mathematics. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98083-6.
Pełny tekst źródłaBanks, H. Thomas. Mathematical and experimental modeling of physical and biological processes. Boca Raton: Chapman & Hall/CRC, 2009.
Znajdź pełny tekst źródła1960-, Deutsch Andreas, red. Mathematical modeling of biological systems. Boston: Birkhauser, 2007.
Znajdź pełny tekst źródłaGoriely, Alain. The Mathematics and Mechanics of Biological Growth. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-0-387-87710-5.
Pełny tekst źródłaCzęści książek na temat "Biological Mathematics"
Kimmel, Marek, i David E. Axelrod. "Biological Background". W Interdisciplinary Applied Mathematics, 19–36. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-1559-0_2.
Pełny tekst źródłaKimmel, Marek, i David E. Axelrod. "Biological Background". W Interdisciplinary Applied Mathematics, 19–31. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21639-1_2.
Pełny tekst źródłaBritton, Nicholas Ferris. "Biological Motion". W Springer Undergraduate Mathematics Series, 147–73. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0049-2_5.
Pełny tekst źródłaStamova, Ivanka, i Gani Stamov. "Impulsive Biological Models". W CMS Books in Mathematics, 41–112. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28061-5_3.
Pełny tekst źródłaMurray, J. D. "Biological Oscillators and Switches". W Interdisciplinary Applied Mathematics, 218–56. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-0-387-22437-4_7.
Pełny tekst źródłaLewis, Mark A., Sergei V. Petrovskii i Jonathan R. Potts. "Dynamics of Biological Invasions". W Interdisciplinary Applied Mathematics, 19–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32043-4_2.
Pełny tekst źródłaVoit, Eberhard O. "The Mathematics of Biological Systems". W 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.
Pełny tekst źródłaMurray, J. D. "Biological Waves: Single-Species Models". W Interdisciplinary Applied Mathematics, 437–83. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-0-387-22437-4_13.
Pełny tekst źródłaOkubo, Akira, i Daniel Grünbaum. "Mathematical Treatment of Biological Diffusion". W Interdisciplinary Applied Mathematics, 127–69. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-4978-6_5.
Pełny tekst źródłaGlass, Leon. "Resetting and Entraining Biological Rhythms". W Interdisciplinary Applied Mathematics, 123–48. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21640-9_5.
Pełny tekst źródłaStreszczenia konferencji na temat "Biological Mathematics"
Guo, Syuan-Ming, Anitha Krishnan, Jenny Folkesson, Jim Karkanias i Shalin B. Mehta. "Learning Biological Structures from Birefringence images with Deep Neural Networks". W Mathematics in Imaging. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/math.2019.mtu3d.3.
Pełny tekst źródłaSoetaert, Karline, Dick van Oevelen, Theodore E. Simos, George Psihoyios, Ch Tsitouras i Zacharias Anastassi. "Modelling Marine Biological and Biogeochemical Data". W 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.
Pełny tekst źródłaIrishina, Natalia, Diego Alvarez, Theodore E. Simos, George Psihoyios, Ch Tsitouras i Zacharias Anastassi. "Characterization of Biological Tissues Using Microwaves". W NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637890.
Pełny tekst źródłaMustafa, Sriyanti, Baharullah i Vernita Sari. "Gesture learning mathematics, spontaneous?" W 4TH INTERNATIONAL CONFERENCE ON FRONTIERS OF BIOLOGICAL SCIENCES AND ENGINEERING (FBSE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0099560.
Pełny tekst źródłaBorisovich, Andrei, Hanna Treder, Theodore E. Simos, George Psihoyios i Ch Tsitouras. "Symmetry-Breaking Bifurcations for Free Elastic Shell of Biological Cluster". W Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790274.
Pełny tekst źródła"Preface: International Conference on Biological Engineering and Medical Science (ICBioMed)". W 7TH INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Mathematics of Quantum Computing. AIP Publishing, 2022. http://dx.doi.org/10.1063/12.0012966.
Pełny tekst źródłaYang, Shuran, i Qifeng Yang. "Development of GelMA ink for multi-scale biological 3D printing". W 7TH INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Mathematics of Quantum Computing. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0123152.
Pełny tekst źródłaDíaz, Elena, i Rafael Gutierrez. "Spin transport in helical biological systems". W INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4893525.
Pełny tekst źródłaAndreucci, Daniele, Dario Bellaveglia, Emilio Maria, Nicola Cirillo, Silvia Marconi, Theodore E. Simos, George Psihoyios, Ch Tsitouras i Zacharias Anastassi. "A Mathematical Model for Alternating Pores in Biological Membranes". W NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637835.
Pełny tekst źródłaHAO, BAILIN. "A FEW PIECES OF MATHEMATICS INSPIRED BY REAL BIOLOGICAL DATA". W Statistical Physics, High Energy, Condensed Matter and Mathematical Physics - The Conference in Honor of C. N. Yang'S 85th Birthday. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812794185_0029.
Pełny tekst źródłaRaporty organizacyjne na temat "Biological Mathematics"
Chakraborty, Srijani. Promises and Challenges of Systems Biology. Nature Library, październik 2020. http://dx.doi.org/10.47496/nl.blog.09.
Pełny tekst źródłaHeinz, Kevin, Itamar Glazer, Moshe Coll, Amanda Chau i 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.
Pełny tekst źródłaComputational Biology: Development in the Field of Medicine. Science Repository, kwiecień 2021. http://dx.doi.org/10.31487/sr.blog.31.
Pełny tekst źródłaIncongruity 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, marzec 2021. http://dx.doi.org/10.14526/2070-4798-2021-16-1-19-23.
Pełny tekst źródłaMethodology 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, marzec 2021. http://dx.doi.org/10.14526/2070-4798-2021-16-1-5-11.
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