Computer Simulation and Data Analysis in Molecular Biology and Biophysics

1. Front Matter

   Pages 1-16

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2. The Basics of R

   1. Front Matter

      Pages 1-1

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   2. Calculating with R

      • Victor Bloomfield

      Pages 3-22

   3. Plotting with R

      • Victor Bloomfield

      Pages 23-47

   4. Functions and Programming

      • Victor Bloomfield

      Pages 49-67

   5. Data and Packages

      • Victor Bloomfield

      Pages 69-82

3. Simulation of Biological Processes

   1. Front Matter

      Pages 84-84

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   2. Equilibrium and Steady State Calculations

      • Victor Bloomfield

      Pages 85-112

   3. Differential Equations and Reaction Kinetics

      • Victor Bloomfield

      Pages 113-139

   4. Population Dynamics

      • Victor Bloomfield

      Pages 141-157

   5. Diffusion and Transport

      • Victor Bloomfield

      Pages 159-173

   6. Regulation and Control of Metabolism

      • Victor Bloomfield

      Pages 175-189

   7. Models of Regulation

      • Victor Bloomfield

      Pages 191-203

4. Analyzing DNA and Protein Sequences

   1. Front Matter

      Pages 206-206

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   2. Probability and Population Genetics

      • Victor Bloomfield

      Pages 207-232

   3. DNA Sequence Analysis

      • Victor Bloomfield

      Pages 233-248

5. Statistical Analysis in Molecular and Cellular Biology

   1. Front Matter

      Pages 250-250

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   2. Statistical Analysis of Data

      • Victor Bloomfield

      Pages 251-277

   3. Microarrays

      • Victor Bloomfield

      Pages 279-308

6. Back Matter

   Pages 1-12

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This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.

• 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

From the reviews: “This book targets biochemistry and molecular biophysics students who have prior knowledge of a few subjects, including mathematics, statistics, and modeling, and possess very basic programming skills. Examples and illustrations are ample throughout the text, and the writing style is plain and efficient. … this book mostly appropriate for students or researchers who have prior exposure to the related subjects. Summing Up: Recommended. Upper-division undergraduates through researchers/faculty.” (D. Papamichail, Choice, Vol. 47 (5), January, 2010)

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