Abstract
Modern biology has become a much more quantitative science, so there is a need to teach a quantitative approach to students. I have developed a course that teaches students some approaches to constructing computational models of biological mechanisms, both deterministic and with some elements of randomness; learning how concepts of probability can help to understand important features of DNA sequences; and applying a useful set of statistical methods to analysis of experimental data. The free, open-source, cross-platform program R serves well as the computer tool for the course, because of its high-level capabilities, excellent graphics, superb statistical capabilities, extensive contributed packages, and active development in bioinformatics.
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References
National Research Council: BIO 2010: Transforming Undergraduate Education for Future Research Biologists (2003)
The Comprehensive R Archive Network, http://cran.r-project.org/
The Bioconductor Project, http://www.bioconductor.org/
Bloomfield, V.A.: Computer Simulation and Data Analysis in Molecular Biology and Biophysics: An Introduction Using R. Springer, Heidelberg (2010) (in press)
Search the R Statistical Language, http://www.dangoldstein.com/search_r.html
Girke, T.: R & Bioconductor Manual, http://faculty.ucr.edu/tgirke/Documents/R_BioCond/R_BioCondManual.html
Short, T.: R Reference Card, http://cran.r-project.org/doc/contrib/Short-refcard.pdf
Dalgaard, P.: Introductory Statistics with R. Springer, Heidelberg (2002)
Verzani, J.: Using R for Introductory Statistics. Chapman & Hall/CRC, Boca Raton (2005)
Deonier, R.C., Tavaré, S., Waterman, M.S.: Computational Genome Analysis: An Introduction. Springer, Heidelberg (2005)
Gentleman, R.: R Programming for Bioinformatics. Chapman & Hall/CRC, Boca Raton (2008)
Paradis, E.: Analysis of Phylogenetics and Evolution with R. Springer, Heidelberg (2006)
Gentleman, R., Carey, V.J., Huber, W., Irizarry, R.A., Dudoit, S. (eds.): Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer, Heidelberg (2005)
Hahne, F., Huber, W., Gentleman, R., Falcon, S.: Bioconductor Case Studies (Use R). Springer, Heidelberg (2008)
Wilkinson, D.J.: Stochastic Modelling for Systems Biology. Chapman & Hall/CRC, Boca Raton (2006)
Keen, R.E., Spain, J.D.: Computer Simulation in Biology: A BASIC Introduction. Wiley-Liss, Chichester (1992)
Fall, C., Marland, E., Wagner, J., Tyson, J. (eds.): Computational Cell Biology. Springer, Heidelberg (2002)
Allman, E.S., Rhodes, J.A.: Mathematical Models in Biology: An Introduction. Cambridge University Press, Cambridge (2004)
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Bloomfield, V.A. (2009). Using R for Computer Simulation and Data Analysis in Biochemistry, Molecular Biology, and Biophysics. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2009. ICCS 2009. Lecture Notes in Computer Science, vol 5545. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01973-9_4
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DOI: https://doi.org/10.1007/978-3-642-01973-9_4
Publisher Name: Springer, Berlin, Heidelberg
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