Buch, Englisch, 272 Seiten
Monte Carlo Simulations of Microbes and Evolution
Buch, Englisch, 272 Seiten
ISBN: 978-1-394-31461-4
Verlag: Wiley
An expert discussion of simulation-based approaches to teaching genome sciences
In Digital Genomes: Monte Carlo Simulations of Microbes and Evolution, distinguished researcher Weigang Qiu delivers a comprehensive exploration of the role of Monte Carlo simulations in understanding complex biological processes. Beginning with an introduction to microbial evolution, computer simulations, and evolutionary algorithms, the book moves on to explore the evolution of DNA sequences and concepts like neutral evolution, Mendelian inheritance, Darwinian natural selection, and genome evolution.
Qiu offers exercises to help readers retain the concepts discussed within, as well as links to open-source code on a complimentary companion website. Those links point to code that serves as a programming recipe for solving evolutionary problems that can be implemented in Python, Bash, R, and other popular programming languages.
Readers will also find: - A thorough introduction to a new approach to teaching population genetics and evolution
- Comprehensive explorations of algorithm-centered, programming language-agnostic learning
- Practical exercises at the end of each chapter that clarify key concepts with guided application
- In-depth treatments of evolutionary mechanisms, like recombination, genetic linkage, balancing selection, genome evolution, bacterial clonality, and negative frequency-dependent selection
Perfect for senior undergraduate and graduate students studying population genetics, evolution, genetics, and bioinformatics, this book will also benefit researchers with an interest in evolutionary biology, genetics, microbiology, and virology.
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Table of Contents
Contents
Preface v
Contents ix
List of Tables xv
List of Algorithms xvii
List of Figures xxi
1 Introduction & Overview 1
1.1 Microbial evolution. 1
1.2 Probability by simulation. 2
1.3 A learner’s guide. 3
1.3.1 Notes on algorithms. 3
1.3.2 Content overview. 4
1.3.3 Algorithm-centered learning. 5
2 Digital Genomes 11
2.1 Random DNA sequences. 12
2.2 DNA replication and transcription. 14
2.3 Genetic code and DNA translation. 14
3 Digital Populations 23
3.1 Quantifying genetic diversity. 24
3.1.1 Haploid populations. 24
3.1.2 Diploid populations. 27
3.2 Genetic drift. 29
3.3 Mutation. 30
3.4 Recombination. 34
3.5 Natural selection. 35
3.6 Trait-associated SNPs. 37
3.6.1 Binary traits. 40
3.6.2 Quantitative traits. 40
4 Digital Species 47
4.1 Population divergence. 47
4.2 Species divergence. 52
4.3 Trait evolution on a tree: binary traits. 56
4.4 Trait evolution on a tree: quantitative traits. 58
5 Digital Life and Learning 63
5.1 Fitness landscape. 64
5.2 Fitness ascent with greedy and genetic algorithms. 66
5.3 Open-ended evolution with novelty search. 68
5.4 Self-optimization with Hebbing learning and Hopfield networks. 71
5.5 Foresighted evolution with perceptron. 73
5.6 Artificial life with finite automata. 78
5.7 Case study: evolution-inspired vaccine designs. 81
6 Gene Frequencies as Genetic Information 87
6.1 Frequentist vs. Bayesian paradigms. 88
6.2 Case Study: Taster/Non-taster gene frequencies in New York City. 89
6.3 Quantifying genetic information. 91
6.3.1 Diversity at a single locus. 94
6.3.2 Diversity at two loci. 95
6.3.3 Distance between populations. 98
6.4 Case Studies: Gene frequencies of Lyme bacteria in eastern United States. 99
6.4.1 Strains as bacterial individuals. 100
6.4.2 Hyper alleleic diversity at ospC. 102
6.4.3 Population divergence. 107
6.4.4 Association between loci. 107
7 Mendelian Genetics and Darwinian Selection 111
7.1 Neutral evolution. 112
7.1.1 Blending vs Mendenlian inheritance. 112
7.1.1.1 Blending inheritance. 112
7.1.1.2 Mendelian inheritance & Hardy-Weinberg Equilibrium. 113
7.1.2 Departure to HWE due to demography. 115
7.1.3 Case Study: Wahlund Effect in New York City. 116
7.2 Darwinian selection. 121
7.2.1 Haplotype selection. 121
7.2.2 Mutation-selection balance. 121
7.2.3 Genotype selection. 126
7.2.3.1 Heterozygote advantages and disadvantages. 126
7.2.3.2 Case Study: Sickle-cell over-dominance in Africa. 126
7.3 Frequency-dependent selection (FDS). 130
8 Stochastic Evolution with Genetic Drift 135
8.1 Forward- and backward-evolution simulations. 136
8.1.1 Track allele frequencies. 136
8.1.2 Track identities by descent. 137
8.1.3 The coalescent. 140
8.2 Neutral DNA polymophisms. 140
8.2.1 Nucleotide diversity. 144
8.2.2 Haplotype diversity with clonality. 145
8.3 Case study: Genetic structures of Lyme bacteria and tick vectors in eastern United
States. 149
8.3.1 Ixodes ticks: northern expansion and sourthern contraction. 150
8.3.2 Borrelia bacteria: diversifying selection. 152
9 Recombination: Genomic Footprints 157
9.1 Four gametes and novel haplotypes. 158
9.2 Linkage disequibrium. 162
9.3 Linkage decay over distance and time. 163
9.4 Haplotype diversity with recombinants. 164
9.5 Multilocus alleleic association. 170
9.6 Sequence of trees. 171
9.7 Case studies: Recombination in Lyme bacterial populations. 172
9.7.1 Four gametes at ospA. 172
9.7.2 Genomic evidence of recombination at ospA. 172
9.7.3 Recombination at ospC. 175
10 Recombination: Adaptive Consequences 181
10.1 Neutral and nearly neutral mutations. 182
10.1.1 Neutral mutations. 182
10.1.2 Molecular Clock. 182
10.1.3 Deleterious mutations. 184
10.1.4 Adaptive mutations. 186
10.1.5 Mutation with frequency-dependent fitness. 187
10.2 Essentiality of recombination and sex. 188
10.2.1 Muller’s ratchet. 188
10.2.2 Hill-Robertson effect. 189
10.3 Linked selection. 194
10.3.1 Genetic hitchhiking and genetic draft. 195
10.3.2 Background selection. 199
10.3.3 Genetic lift: linked frequency-dependent selection. 200
11 Microbial Genome Clusters: Causes and Consequences 211
11.1 Clonality-sexuality threshold. 212
11.2 Selective sweeps. 212
11.3 Immune selection. 218
11.4 Genome-wide association studies in bacteria. 220
Acknowledgments 229
Bibliography 233
Index 261
