Academic literature on the topic 'Biological Mathematics'
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Journal articles on the topic "Biological Mathematics"
Doube, Michael. "Mathematics for Biological Scientists." Journal of Anatomy 216, no. 4 (April 2010): 542. http://dx.doi.org/10.1111/j.1469-7580.2009.01195.x.
Full textB. Khyade, Vitthalrao, and Hanumant V. Wanve. "Review on Use of Mathematics for Progression of Biological Sciences." International Academic Journal of Innovative Research 05, no. 01 (June 12, 2018): 301–38. http://dx.doi.org/10.9756/iajir/v5i1/1810004.
Full textThompson, Paul. "Mathematics in the biological sciences." International Studies in the Philosophy of Science 6, no. 3 (January 1992): 241–48. http://dx.doi.org/10.1080/02698599208573434.
Full textBELLOMO, N., and F. BREZZI. "MATHEMATICS AND COMPLEXITY IN BIOLOGICAL SCIENCES." Mathematical Models and Methods in Applied Sciences 21, supp01 (April 2011): 819–24. http://dx.doi.org/10.1142/s0218202511005374.
Full textKarl, Lila. "DNA computing: Arrival of biological mathematics." Mathematical Intelligencer 19, no. 2 (March 1997): 9–22. http://dx.doi.org/10.1007/bf03024425.
Full textPenny, D. "Doom01: biological mathematics in evolutionary processes." Trends in Ecology & Evolution 16, no. 6 (June 1, 2001): 275–76. http://dx.doi.org/10.1016/s0169-5347(01)02166-8.
Full textMarland, Eric, Katrina M. Palmer, and René A. Salinas. "Biological Applications in the Mathematics Curriculum." PRIMUS 18, no. 1 (January 17, 2008): 85–100. http://dx.doi.org/10.1080/10511970701744984.
Full textŠorgo, 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 textDurán, Pablo A., and Jill A. Marshall. "Mathematics for biological sciences undergraduates: a needs assessment." International Journal of Mathematical Education in Science and Technology 50, no. 6 (October 26, 2018): 807–24. http://dx.doi.org/10.1080/0020739x.2018.1537451.
Full textNaidoo, Jayaluxmi. "Integrating Indigenous Knowledge and Culturally based Activities in South African Mathematics Classrooms." African Journal of Teacher Education 10, no. 2 (December 11, 2021): 17–36. http://dx.doi.org/10.21083/ajote.v10i2.6686.
Full textDissertations / Theses on the topic "Biological Mathematics"
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
Orme, Belinda Abigail Amanda. "Biological mixing and chaos." Thesis, University of Birmingham, 2002. http://etheses.bham.ac.uk//id/eprint/7637/.
Full textTucker, George Jay. "Statistical methods to infer biological interactions." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89874.
Full textThis 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.
Full textMontenegro-Johnson, Thomas D. "Microscopic swimming in biological fluids." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4220/.
Full textSeier, Edith, and Karl H. Joplin. "Introduction to STATISTICS in a Biological Context." Digital Commons @ East Tennessee State University, 2011. http://amzn.com/1463613377.
Full textCaberlin, 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.
Full textYu, Yun William. "Compressive algorithms for search and storage in biological data." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112879.
Full textCataloged 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.
Books on the topic "Biological Mathematics"
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 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 textCampbell, June Mundy. Laboratory mathematics: Medical and biological applications. 4th ed. St. Louis: Mosby, 1990.
Find full text1933-, Campbell June Mundy, ed. Laboratory mathematics: Medical and biological applications. 5th ed. St. Louis: Mosby, 1997.
Find full textSegal, Rebecca, Blerta Shtylla, and Suzanne Sindi, eds. Using Mathematics to Understand Biological Complexity. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57129-0.
Full textRadunskaya, Ami, Rebecca Segal, and Blerta Shtylla, eds. Understanding Complex Biological Systems with Mathematics. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98083-6.
Full textBanks, H. Thomas. Mathematical and experimental modeling of physical and biological processes. Boca Raton: Chapman & Hall/CRC, 2009.
Find full text1960-, Deutsch Andreas, ed. Mathematical modeling of biological systems. Boston: Birkhauser, 2007.
Find full textGoriely, 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.
Full textBook chapters on the topic "Biological Mathematics"
Kimmel, 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 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 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 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 textMurray, J. D. "Biological Waves: Single-Species Models." In Interdisciplinary Applied Mathematics, 437–83. New York, NY: Springer New York, 1993. http://dx.doi.org/10.1007/978-0-387-22437-4_13.
Full textOkubo, Akira, and Daniel Grünbaum. "Mathematical Treatment of Biological Diffusion." In Interdisciplinary Applied Mathematics, 127–69. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-4978-6_5.
Full textGlass, Leon. "Resetting and Entraining Biological Rhythms." In Interdisciplinary Applied Mathematics, 123–48. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21640-9_5.
Full textConference papers on the topic "Biological Mathematics"
Guo, Syuan-Ming, Anitha Krishnan, Jenny Folkesson, Jim Karkanias, and Shalin B. Mehta. "Learning Biological Structures from Birefringence images with Deep Neural Networks." In Mathematics in Imaging. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/math.2019.mtu3d.3.
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 textIrishina, Natalia, Diego Alvarez, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Characterization of Biological Tissues Using Microwaves." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637890.
Full textMustafa, Sriyanti, Baharullah, and Vernita Sari. "Gesture learning mathematics, spontaneous?" In 4TH INTERNATIONAL CONFERENCE ON FRONTIERS OF BIOLOGICAL SCIENCES AND ENGINEERING (FBSE 2021). AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0099560.
Full textBorisovich, Andrei, Hanna Treder, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Symmetry-Breaking Bifurcations for Free Elastic Shell of Biological Cluster." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790274.
Full text"Preface: International Conference on Biological Engineering and Medical Science (ICBioMed)." In 7TH INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Mathematics of Quantum Computing. AIP Publishing, 2022. http://dx.doi.org/10.1063/12.0012966.
Full textYang, Shuran, and Qifeng Yang. "Development of GelMA ink for multi-scale biological 3D printing." In 7TH INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Mathematics of Quantum Computing. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0123152.
Full textDíaz, Elena, and Rafael Gutierrez. "Spin transport in helical biological systems." In INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4893525.
Full textAndreucci, Daniele, Dario Bellaveglia, Emilio Maria, Nicola Cirillo, Silvia Marconi, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "A Mathematical Model for Alternating Pores in Biological Membranes." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637835.
Full textHAO, BAILIN. "A FEW PIECES OF MATHEMATICS INSPIRED BY REAL BIOLOGICAL DATA." In 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.
Full textReports on the topic "Biological Mathematics"
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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