"Microbial studies rarely concern evolution. The converse is also true: microbiology remains a stubborn outlier of the Modern and New Synthesis, the grand evolutionary integration of Mendelian genetics, Darwinian natural selection, molecular biology, andother biological triumphs (Woese and Goldenfeld, 2009; Koonin, 2009). With some notable exceptions (Robinson et al., 2010), this lack of systematic evolutionary analysis of microbial populations leaves significant gaps in the intellectual landscapes of both microbiology and evolutionary biology, an increasingly untenable situation considering the antiquity and ubiquity of microbial life on Earth, and the frequent emergence and global spread of deadly microbial pathogens. Meanwhile, breakthroughs in DNA sequencing technologies in recent decades have catapulted microbial ecology and evolution to the forefront of scientific and biomedical discoveries of our time (Lenski, 2017; Garud and Pollard, 2020). Equally frustrating to a college biology professor likeme are the difficulties and confusion students encounter when we throw them into the deep waters of human genetics first and foremost. As this book strives to demonstrate, the haploid but nonetheless sexual microbial systems are simpler to build the foundational understandings of the evolution of biological - and, in fact, artificial, cultural, and other non-biological - systems without compromising general laws and principles governing the evolutionary processes."-- Provided by publisher.
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.
