Computer Simulation and Data Analysis in Molecular Biology and Biophysics (eBook)

An Introduction Using R

Victor Bloomfield (Autor)

eBook Download: PDF

2009 | 2009
XVI, 321 Seiten
Springer New York (Verlag)
978-1-4419-0083-8 (ISBN)

Infos

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.

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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, "e;BIO 2010: Transforming Undergraduate Education for Future - search Biologists"e; [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.

Inhalt

Preface 6
Contents 10
Part I The Basics of R 18
Chapter 1 Calculating with R 19
1.1 Installing R 19
1.2 Finding help with R 20
1.4 Arithmetic 21
1.5 Complex numbers 21
1.6 Assigning variables 22
1.7 Standard mathematical functions 23
1.8 Vectors 24
1.9 Generating sequences 27
1.10 Logical vectors 29
1.12 Other data structures 35
Chapter 2 Plotting with R 39
2.1 Some common plots 39
2.2 Customizing plots 44
2.3 Superimposing data series in a plot 55
2.4 Placing two or more plots in a .gure 58
2.5 Error bars 60
2.6 Locating and identifying points on a plot 61
2.7 Problems 62
Chapter 3 Functions and Programming 64
3.1 Built-in functions in R 64
3.2 User-de.ned functions 67
3.3 Programming 70
3.4 Numerical analysis with R 73
3.5 Problems 80
Chapter 4 Data and Packages 83
4.1 Writing and reading data to .les 83
4.2 Packages 86
4.3 Data frames 87
4.4 Factors 89
4.5 The contributed package seqinr 90
4.6 Problems 95
Part II Simulation of Biological Processes 97
Chapter 5 Equilibrium and Steady State Calculations 98
5.1 Calculation of the concentration of species in a reacting mixture at equilibrium 98
5.2 Single strand–double helix equilibrium in oligonucleotides 111
5.3 Steady-state enzyme kinetics 116
5.4 Non-linear least-squares .tting 122
5.5 Problems 123
Chapter 6 Differential Equations and Reaction Kinetics 126
6.1 Analytically solvable models 126
6.2 Numerical integration of ODEs 132
6.3 Stochastic differential equations 146
6.4 Problems 148
Chapter 7 Population Dynamics: Competition, Predation, and Infection 153
7.1 Models of homogeneous populations of organisms 153
7.2 Models of microbial growth 158
7.3 Models of Epidemics 161
7.4 Problems 167
Chapter 8 Diffusion and Transport 170
8.1 Transport by simple diffusion 170
8.2 Diffusion in a driving .eld: electrophoresis 177
8.3 Countercurrent diffusion 177
8.4 Diffusion as a random process: Brownian motion 178
8.5 Compartmental models in physiology and pharmacokinetics 180
8.6 Problems 183
Chapter 9 Regulation and Control of Metabolism 185
9.1 Successive enzyme reactions 185
9.2 Metabolic control analysis 191
9.3 Biochemical systems theory 194
9.4 Problems 198
Chapter 10 Models of Regulation 200
10.1 Regulation of transcription: Feed-forward loops 200
10.2 Regulation of signaling: Bacterial chemotaxis 202
10.3 Regulation of development: Morphogenesis 206
10.4 Problems 211
Part III Analyzing DNA and Protein Sequences 213
Chapter 11 Probability and Population Genetics 214
11.1 Some fundamentals of probability 214
11.2 Stochastic population models 217
11.4 Probability distribution functions 223
11.5 Population Genetics 235
11.6 Problems 238
Chapter 12 DNA Sequence Analysis 240
12.1 Getting a sequence from theWeb 240
12.2 Single base sequences and frequencies 244
12.3 Dinucleotide sequences and frequencies 246
12.4 Simulation of restriction sites 251
12.5 Detecting periodicity in a sequence 253
12.6 Problems 255
Part IV Statistical Analysis in Molecular and Cellular Biology 256
Chapter 13 Statistical Analysis of Data 257
13.1 Summary statistics for a single group of data 257
13.2 Statistical comparison of two samples 264
13.3 Analysis of spectral data 273
13.4 Problems 282
Chapter 14 Microarrays 284
14.1 Introduction 284
14.2 Preprocessing overview 285
14.3 The affy package 286
14.4 Importing data not in standard form 289
14.5 The need for preprocessing 289
14.6 Preprocessing steps 291
14.7 Using the results of preprocessing 296
14.8 Statistical analysis of differential gene expression 298
14.9 Detecting groups of genes 302
14.10 Power analysis 308
14.11 Problems 312
Appendix A Basic String Manipulations in R 314
Elementary string manipulations 314
Genetic code translation 316
References 318
Index 321

Produkt-Details

• Erscheint lt. Verlag: 5.6.2009
• Reihe/Serie: Biological and Medical Physics, Biomedical Engineering
• Reihe/Serie: Biological and Medical Physics, Biomedical Engineering
• Zusatzinfo: XVI, 321 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Angewandte Physik
• Themenwelt: Technik ► Bauwesen
• Schlagworte: Bioinformatics • Bioinformatics analysis • Biology • Biophysics • Biophysics computing • Computational Biology • Data analysis in molecular biology • Genetics • Markov Chain • microarray • Quantitative biology • R programming language • Simulation • Statistical computing graphics • systems biology quantitative models • transcription • using GNU S
• ISBN-10: 1-4419-0083-7 / 1441900837
• ISBN-13: 978-1-4419-0083-8 / 9781441900838