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Digital Genomes
Monte Carlo Simulations of Microbes and Evolution
Weigang Qiu (Author)
9781394314614, Wiley
Paperback / softback, published 12 March 2026
240 pages
24.1 x 16.8 x 2 cm, 0.454 kg
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: 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.
List of Algorithms xi 1 Introduction and Overview 1 Part I Foundational Algorithms 9 2 Digital Genomes 11 3 Digital Populations 21 4 Digital Species 41 5 Digital Life and Learning 53 Part II Gene Evolution 71 6 Gene Frequencies as Genetic Information 73 7 Mendelian Genetics and Darwinian Selection 95 8 Stochastic Evolution with Genetic Drift 115 Part III Genome Evolution 133 9 Recombination: Genomic Footprints 135 10 Recombination: Adaptive Consequences 155 11 Microbial Genome Clusters: Causes and Consequences 181 Exercises 192
List of Figures xiii
List of Tables xvii
About the Author xix
Preface xxi
Acknowledgments xxiii
1.1 Microbial Evolution 1
1.2 Probability by Simulation 2
1.3 A Learner's Guide 3
2.1 Random DNA Sequences 12
2.2 DNA Replication and Transcription 13
2.3 Genetic Code and DNA Translation 14
3.1 Quantifying Genetic Diversity 22
3.2 Genetic Drift 27
3.3 Mutation 28
3.4 Recombination 30
3.5 Natural Selection 32
3.6 Trait-associated SNPs 36
4.1 Population Divergence 41
4.2 Species Divergence 44
4.3 Trait Evolution on a Tree: Binary Traits 46
4.4 Trait Evolution on a Tree: Quantitative Traits 48
5.1 Fitness Landscape 53
5.2 Fitness Ascent with Greedy and Genetic Algorithms 54
5.3 Open-ended Evolution with Novelty Search 56
5.4 Self-optimization with Hebbian Learning and Hopfield Networks 59
5.5 Foresighted Evolution with Perceptron 60
5.6 Artificial Life with Finite Automata 63
5.7 Case Study: Evolution-inspired Vaccine Designs 65
6.1 Frequentist vs. Bayesian Paradigms 74
6.2 Case Study: Taster/Nontaster Gene Frequencies in New York City 75
6.3 Quantifying Genetic Information 77
6.4 Case Studies: Gene Frequencies of Lyme Bacteria in Eastern United States 83
7.1 Neutral Evolution 95
7.2 Darwinian Selection 102
7.3 Frequency-dependent Selection 111
8.1 Forward- and Backward-evolution Simulations 116
8.2 Neutral DNA Polymorphisms 119
8.3 Case Study: Genetic Structures of Lyme Bacteria and Tick Vectors in the Eastern United States 127
9.1 Four Gametes and Novel Haplotypes 136
9.2 Linkage Disequilibrium 139
9.3 Linkage Decay Over Distance and Time 140
9.4 Haplotype Diversity with Recombinants 143
9.5 Multilocus Allelic Association 146
9.6 Sequence of Trees 148
9.7 Case Studies: Recombination in Lyme Bacterial Populations 149
10.1 Neutral and Nearly Neutral Mutations 156
10.2 Essentiality of Recombination and Sex 160
10.3 Linked Selection 163
11.1 Clonality-sexuality Threshold 182
11.2 Selective Sweeps 184
11.3 Immune Selection 186
11.4 Genome-wide Association Studies in Bacteria 188
Afterword 193
Bibliography 195
Index 211
Subject Areas: Biology, life sciences [PS]
