Nature in Silico: Population Genetic Simulation and its Evolutionary Interpretation Using C++ and R
Autor Ryan J. Haaslen Limba Engleză Paperback – 3 sep 2023
Dramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient—and covered here—the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussedin the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation.
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Specificații
ISBN-13: 9783030973834
ISBN-10: 3030973832
Pagini: 313
Ilustrații: XVIII, 313 p. 96 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.47 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030973832
Pagini: 313
Ilustrații: XVIII, 313 p. 96 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.47 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland
Cuprins
Introduction and relevance.- Retrospective and prospective simulation.- Data structures and computational efficiency.- Mutation.- Population size and genetic drift.- Migration and population structure.- Meiotic recombination.- Natural selection.- Implementing all five factors simultaneously.- Modeling different life histories.- Spatially-explicit simulation.- Calculating summary statistics and visualization.- Approximate Bayesian computation: preliminaries.- Approximate Bayesian computation: implementation.- Comparing simulated genetic data to 1000 Genomes data.- The spread of the invasive species Japanese hops in the Upper Midwest, USA.
Notă biografică
Ryan J. Haasl is an Associate Professor of Biology at the University of Wisconsin-Platteville. He holds an M.A. in Entomology from the University of Kansas and a Ph.D. in Genetics from the University of Wisconsin-Madison. His research focuses on the use of simulation and statistical computing to explore favorite topics such as natural selection targeting microsatellites, phylogenomics, and the consolidation of microevolutionary dynamics and macroevolutionary pattern. He is passionate about teaching genetics and evolutionary biology to undergraduate students and fostering public literacy in the biological sciences through outreach.
Textul de pe ultima copertă
Dramatic advances in computing power enable simulation of DNA sequences generated by complex microevolutionary scenarios that include mutation, population structure, natural selection, meiotic recombination, demographic change, and explicit spatial geographies. Although retrospective, coalescent simulation is computationally efficient—and covered here—the primary focus of this book is forward-in-time simulation, which frees us to simulate a wider variety of realistic microevolutionary models. The book walks the reader through the development of a forward-in-time evolutionary simulator dubbed FORward Time simUlatioN Application (FORTUNA). The capacity of FORTUNA grows with each chapter through the addition of a new evolutionary factor to its code. Each chapter also reviews the relevant theory and links simulation results to key evolutionary insights. The book addresses visualization of results through development of R code and reference to more than 100 figures. All code discussed in the book is freely available, which the reader may use directly or modify to better suit his or her own research needs. Advanced undergraduate students, graduate students, and professional researchers will all benefit from this introduction to the increasingly important skill of population genetic simulation.
Caracteristici
Provides numerous coding examples throughout the book Contains coding problems at the end of each main chapter with solutions provided for select examples online Contains 100 figures showing summaries of genetic data, results of analysis, or conceptual ideas