Free Access
Foreword
Issue
Math. Model. Nat. Phenom.
Volume 6, Number 6, 2011
Biomathematics Education
Page(s) 1 - 21
Section Introduction
DOI https://doi.org/10.1051/mmnp/20116601
Published online 05 October 2011
  1. J. Cohen. Mathematics is biology’s next microscope, only better; biology is mathematics’ next physics, only better. PLOS Biology, 2, (2004), 439. [CrossRef] [Google Scholar]
  2. J. Jungck, P. Marsteller, editors. Bio 2010: Mutualism of biology and mathematics. A special issue of CBE Life Science Education, 9, (2010) No. 3. Available from: (http://www.lifescied.org/content/vol9/issue3/index.dtl). [Google Scholar]
  3. J. Jungck. Ten equations that changed biology. Bioscene, 23 (1997), No. 1, 11–36. [Google Scholar]
  4. Board on Life Sciences. National Research Council. BIO2010: Transforming undergraduate education for future research Bbologists. National Academies Press: Washington, D.C., 2003. [Google Scholar]
  5. A. Weisstein. Building mathematical models and biological insight in an introductory biology course. Math. Model. Nat. Phenom., 6 (2011), No. 6, 198–214. [CrossRef] [EDP Sciences] [Google Scholar]
  6. H. Gaff, M. Lyons, G. Watson. Classroom manipulative to engage students in mathematical modeling of disease spread: 1+1 = Achoo!. Math. Model. Nat. Phenom., 6 (2011), No. 6, 215–226. [CrossRef] [EDP Sciences] [Google Scholar]
  7. C. Neuhauser, E. Stanley. The p and the peas: An intuitive modeling approach to hypothesis testing. Math. Model. Nat. Phenom., 6 (2011), No. 6, 76–95. [CrossRef] [EDP Sciences] [Google Scholar]
  8. G. Koch. Drugs in the classroom: Using pharmacokinetics to introduce biomathematical modeling. Math. Model. Nat. Phenom., 6 (2011), No. 6, 227–244. [CrossRef] [EDP Sciences] [Google Scholar]
  9. AAAS Vision and Change in Undergraduate biology education: A call To action. American Association for the Advancement of Science, Washington, D.C., 2011. [Google Scholar]
  10. National Research Council. A New Biology for the 21st Century: Ensuring that the United States Leads the Coming Biology Revolution. National Academies Press, Washington, D.C., 2009. [Google Scholar]
  11. S. Emmott, S. Rison, Editors. Towards 2020 science. Microsoft Corporation, Cambridge, 2006, http://research.microsoft.com/en-us/um/cambridge/projects/towards2020science/\downloads/t2020s_report.pdf [Google Scholar]
  12. L. Steen, Editor. Math and Bio 2010: Linking Undergraduate Disciplines. Mathematics Association of America, Washington, D.C., 2005. [Google Scholar]
  13. T. Hey, St. Tansley, K. Tolle, Editors. The fourth paradigm: Data-intensive scientific discovery. Microsoft: Redmond, Washington, 2009. (http://research.microsoft.com/en-us/collaboration/fourthparadigm/4th_paradigm_book_complete_lr.pdf). [Google Scholar]
  14. Scientific Foundations for Future Physicians: Report of the AAMC-HHMI Committee. Association of American Medical Colleges, Washington, D.C., 2009. (http://www.hhmi.org/grants/pdf/08–209_AAMC-HHMI_report.pdf). [Google Scholar]
  15. J. Woodger. Biological principles : a critical study. Harcourt, Brace, London, 1929. [Google Scholar]
  16. C. Anderson. The end of theory: The data deluge makes the scientific method obsolete. Wired, 16 (2008) 7. [Google Scholar]
  17. M. Pigliucci. The end of theory in science? EMBO Reports, 10 (2009), 534. [CrossRef] [PubMed] [Google Scholar]
  18. G. An. Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med., 2 (2010), No. 41, 34. [Google Scholar]
  19. G. An, S. Christley. Agent-based modeling and biomedical ontologies: a roadmap. Computational Statistics, 3 (2011) No. 4, 343-356. [CrossRef] [Google Scholar]
  20. R. Levins. The strategy of model building in population biology. American Scientist, 54 (1966) 421–431. [Google Scholar]
  21. A. Clark, E. Wiebe. Scientific visualization for secondary and post-secondary schools. Journal of Technology Studies, 26 (2000), No. 1. [Google Scholar]
  22. H. Goldstein. The future of statistics within the curriculum. Teaching statistics, 29 (2006), No. 1, 8–9. [CrossRef] [Google Scholar]
  23. C. Konold, T. Higgins. Reasoning about data. In J. Kilpatrick, W. Martin, D. Schifter (Eds.), A research companion to principles and standards for school mathematics, Reston, VA, National Council of Teachers of Mathematics, (2003), 193–215. [Google Scholar]
  24. D. Haak, J. Hille, R. Lambers, E. Pitre, S. Freeman. Increased structure and active learning reduce the achievement gap in introductory biology. Science, 332 (2011), 1213–1216. [CrossRef] [PubMed] [Google Scholar]
  25. S. Ziliak, D. McCloskey. The cult of statistical significance. The University of Michigan Press, Ann Arbor, 2008. [Google Scholar]
  26. L. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1 (1978), 3–28. [CrossRef] [MathSciNet] [Google Scholar]
  27. N. Friedman, J. Halpern. Plausibility measures and default reasoning. Journal of ACM, 48 (2001), No. 4, 648-685. [CrossRef] [Google Scholar]
  28. G. Qi. A semantic approach for iterated revision in possibilistic logic. AAAI, (2008), 523–528. [Google Scholar]
  29. C. Schwarz, B. Reiser, E. Davis, L. Kenyon, A. Acher, D. Fortus, Y. Shwartz, B. Hug, J. Krajcik. Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching. 46 (2009), No. 6, 632–654. [CrossRef] [Google Scholar]
  30. D. Ost. Models, modeling and the teaching of science and mathematics. School Science and Mathematics, 87 (1987), No. 5, 363–370. [CrossRef] [Google Scholar]
  31. J. Odenbaugh. The strategy of model building in population biology. Biology and Philosophy, 21 (2006), 607–621. [CrossRef] [Google Scholar]
  32. G. Box. Robustness in the strategy of scientific model building. (May 1979) in R. Launer, G. Wilkinson, Editors, Robustness in Statistics: Proceedings of a Workshop, 1979. [Google Scholar]
  33. W. Wimsatt. False models as means to truer theories. In M. Nitecki, editor, Neutral models in biology; Oxford University Press, Oxford, (1987), 23–55. [Google Scholar]
  34. H. Bhadeshia. Mathematical models in materials science. Materials Science Technology, 24 (2008), 128–136. [CrossRef] [Google Scholar]
  35. J. Stewart, C.Passmore, J. Cartier. Project MUSE: Involving high school students in evolutionary biology through realistic problems and causal models. Biology International, 47 (2010), 78–90. [Google Scholar]
  36. J. Jungck. Genetic codes as codes: Towards a theoretical basis for Bioinformatics. In R. Mondaini (Universidade Federal do Rio de Janeiro, Brazil), Editor. BIOMAT 2008. World Scientific, Singapore, (2009), 300–331. [Google Scholar]
  37. A. Caldeira. Mathematical modeling and environmental education. Proceedings of the 11th International Congress on Mathematics Education, Monterrey, Mexico, July 6 - 13, 2008, (20009), (http://tsg.icme11.org/document/get/493). [Google Scholar]
  38. L. Steen. Data, shapes, symbols: Achieving balance in school mathematics. In B. Madison, L. Steen, Editors, Quantitative literacy: Why numeracy matters for schools and colleges. Mathematics Association of America, Washington, DC., (2003), 53–74. [Google Scholar]
  39. G. Wiggins. Get real! assessing for quantitative literacy. In B. Madison, L. Steen, Editors, Quantitative literacy: Why numeracy matters for schools and colleges. Princeton, NJ, National Council on Education and the Disciplines, (2003), 121–143. [Google Scholar]
  40. R. Richardson, W. Mccallum. The third R in literacy. In B. Madison, L. Steen, Editors, Quantitative literacy: Why numeracy matters for schools and colleges. Mathematics Association of America, Washington, DC., (2003), 99–106. [Google Scholar]
  41. D. Krathwohl. A revision of Bloom’s Taxonomy: An overview. Theory Into Practice, 41 (2002), No. 4, 212–218. [Google Scholar]
  42. H. Freudenthal. Weeding and sowing: Preface to a science of mathematics education. Dordrecht, Netherlands, 1980. [Google Scholar]
  43. K. Gravemeijer, J. Terwel. Hans Freudenthal: a mathematician on didactics and curriculum theory. J. Curriculum Studies, 32 (2000), No. 6, 777–796. [CrossRef] [Google Scholar]
  44. R. Khattar, C. Wien. Review of complexity and education: Inquiries into learning, teaching, and research by B. Davis, D. Sumara, 2006. New York and London: Lawrence Erlbaum Associates. Complicity, 7 (2010), No. 2, 122–125. [Google Scholar]
  45. M. Andresen. Teaching to reinforce the bonds between modelling and reflecting. In M. Blomhoj, S. Carreira, Editors, Mathematical applications and modelling in the teaching and learning of mathematics. Proceedings from Topic Study Group 21 at the 11th International Congress on Mathematical Education in Monterrey, Mexico, July 6-13, 2008, (2009), 73–83. (Available at http://diggy.ruc.dk:8080/retrieve/14388#page=77). [Google Scholar]
  46. M. Andresen. Modeling with the software ‘Derive’ to support a constructivist approach to teaching. International Electronic Journal of Mathematics Education, 2 (2007), No. 1, 1–15. [Google Scholar]
  47. G. Gadanidis, V. Geiger. A social perspective on technology-enhanced mathematical learning: from collaboration to performance. ZDM, 42 (2010), No. 1, 91–104. [CrossRef] [Google Scholar]
  48. L. Doorman, K. Gravemeijer. Emergent modeling: discrete graphs to support the understanding of change and velocity. ZDM, 41 (2009), No. 1/2. [CrossRef] [Google Scholar]
  49. K. Gravemeijer, M. Doorman. Context problems in realistic mathematics education: A calculus course as an example. Educational Studies in Mathematics, 39 (1999), 111–129. [CrossRef] [Google Scholar]
  50. K. Gravemeijer, M. Stephan. Emergent models as an instructional design heuristic. In Gravemeijer et al., (2002), 145–169. [Google Scholar]
  51. K. Gravemeijer, R. Lehrer, L. Verschaffel, B. Van Oers (Eds.). Symbolizing, modeling, and tool use in mathematics education. Dordrecht, Netherlands, Kluwer, 2002. [Google Scholar]
  52. D. Kondrashov. Using normal modes analysis in teaching mathematical modeling to biology students. Math. Model. Nat. Phenom., 6 (2011), No. 6, 278–294. [CrossRef] [EDP Sciences] [Google Scholar]
  53. J. Ellis-Monaghan, G. Pangborn. Using DNA self-assembly design strategies to motivate graph theory concepts. Math. Model. Nat. Phenom., 6 (2011), No. 6, 96–107. [CrossRef] [EDP Sciences] [Google Scholar]
  54. S. Robic, J. Jungck. Unraveling the tangled complexity of DNA: Combining mathematical modeling and experimental biology to understand replication, recombination and repair. Math. Model. Nat. Phenom., 6 (2011), No. 6, 108–135. [CrossRef] [EDP Sciences] [Google Scholar]
  55. R. Kerner. Self-assembly of icosahedral viral capsids: the combinatorial analysis approach. Math. Model. Nat. Phenom., 6 (2011), No. 6, 136–158. [CrossRef] [EDP Sciences] [Google Scholar]
  56. R. Robeva, B. Kirkwood, R. Davies. Boolean biology: Introducing boolean networks and finite dynamical systems models to biology and mathematics courses. Math. Model. Nat. Phenom., 6 (2011), No. 6, 39–60. [CrossRef] [EDP Sciences] [Google Scholar]
  57. J. Gill, K. Shaw, B. Rountree, Ca. Kehl, H. Chiel. Simulating kinetic processes in time and space on a lattice. Math. Model. Nat. Phenom., 6 (2011), No. 6, 159–197. [CrossRef] [EDP Sciences] [Google Scholar]
  58. J. Milton, A. Radunskaya, W. Ou, T. Ohira. A team approach to undergraduate research in biomathematics: Balance control. Math. Model. Nat. Phenom., 6 (2011), No. 6, 260–277. [CrossRef] [EDP Sciences] [Google Scholar]
  59. M. Cozzens. Food webs, competition graphs, and habitat formation. Math. Model. Nat. Phenom., 6 (2011), No. 6, 22–38. [CrossRef] [EDP Sciences] [Google Scholar]
  60. G. Hartvigsen. Using R to build and assess network models in biology. Math. Model. Nat. Phenom., 6 (2011), No. 6, 61–75. [CrossRef] [EDP Sciences] [Google Scholar]
  61. J. Knisley. Compartmental models of migratory dynamics. Math. Model. Nat. Phenom., 6 (2011), No. 6, 245–259. [CrossRef] [EDP Sciences] [Google Scholar]
  62. Y. Grossman, A. Berdanier, M. Custic, L. Feeley, S. Peake, A. Saenz, K. Sitton. Integrating photosynthesis, respiration, biomass partitioning, and plant growth: Developing a Microsoft Excelő-based simulation model of Wisconsin Fast Plants (Brassica rapa, Brassicaceae) growth with students. Math. Model. Nat. Phenom., 6 (2011), No. 6, 295–313. [CrossRef] [EDP Sciences] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.