Computer Simulation and Data Analysis in Molecular Biology and Biophysics

An Introduction Using R

  • Authors
  • Victor Bloomfield

Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)

Table of contents

  1. Front Matter
    Pages 1-16
  2. The Basics of R

    1. Front Matter
      Pages 1-1
    2. Victor Bloomfield
      Pages 3-22
    3. Victor Bloomfield
      Pages 23-47
    4. Victor Bloomfield
      Pages 49-67
    5. Victor Bloomfield
      Pages 69-82
  3. Simulation of Biological Processes

    1. Front Matter
      Pages 84-84
    2. Victor Bloomfield
      Pages 85-112
    3. Victor Bloomfield
      Pages 113-139
    4. Victor Bloomfield
      Pages 141-157
    5. Victor Bloomfield
      Pages 159-173
    6. Victor Bloomfield
      Pages 175-189
    7. Victor Bloomfield
      Pages 191-203
  4. Analyzing DNA and Protein Sequences

    1. Front Matter
      Pages 206-206
    2. Victor Bloomfield
      Pages 207-232
    3. Victor Bloomfield
      Pages 233-248
  5. Statistical Analysis in Molecular and Cellular Biology

    1. Front Matter
      Pages 250-250
    2. Victor Bloomfield
      Pages 251-277
    3. Victor Bloomfield
      Pages 279-308
  6. Back Matter
    Pages 1-12

About this book


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


Biophysics computing Data analysis in molecular biology Markov chain Microarray Quantitative biology R programming language Simulation Statistical computing graphics bioinformatics biology biophysics genetics systems biology quantitative models transcription using GNU S

Bibliographic information