Computer Simulation and Data Analysis in Molecular Biology and Biophysics [electronic resource] :An Introduction Using R / by Victor Bloomfield.
by Bloomfield, Victor [author.]; SpringerLink (Online service).
Material type:
BookSeries: Biological and Medical Physics, Biomedical Engineering: Publisher: New York, NY : Springer New York, 2009.Description: online resource.ISBN: 9781441900838.Subject(s): Physics | Bioinformatics | Biochemistry | Cytology | Biology -- Data processing | Biomedical engineering | Physics | Computational Biology/Bioinformatics | Cell Biology | Computer Appl. in Life Sciences | Biomedical Engineering | Biochemistry, general | Biophysics/Biomedical PhysicsOnline resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| MAIN LIBRARY | Available |
The Basics of R -- Calculating with R -- Plotting with R -- Functions and Programming -- Data and Packages -- Simulation of Biological Processes -- Equilibrium and Steady State Calculations -- Differential Equations and Reaction Kinetics -- Population Dynamics -- Diffusion and Transport -- Regulation and Control of Metabolism -- Models of Regulation -- Analyzing DNA and Protein Sequences -- Probability and Population Genetics -- DNA Sequence Analysis -- Statistical Analysis in Molecular and Cellular Biology -- Statistical Analysis of Data -- Microarrays.
This book provides an introduction, suitable for advanced undergraduates and beginning graduate students, to two important aspects of molecular biology and biophysics: computer simulation and data analysis. It introduces tools to enable readers to learn and use fundamental methods for constructing quantitative models of biological mechanisms, both deterministic and with some elements of randomness, including complex reaction equilibria and kinetics, population models, and regulation of metabolism and development; to understand how concepts of probability can help in explaining important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data from spectroscopic, genomic, and proteomic sources. These quantitative tools are implemented using the free, open source software program R. R provides an excellent environment for general numerical and statistical computing and graphics, with capabilities similar to Matlab®. Since R is increasingly used in bioinformatics applications such as the BioConductor project, it can serve students as their basic quantitative, statistical, and graphics tool as they develop their careers
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