| 书目名称 | Nature in Silico | | 副标题 | Population Genetic S | | 编辑 | Ryan J. Haasl | | 视频video | | | 概述 | 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 | | 图书封面 |  | | 描述 | 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 wi | | 出版日期 | Textbook 2022 | | 关键词 | Population genetics; Population genomics; Ecological genetics; Simulation‐based inference; Spatially]ex | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-97381-0 | | isbn_softcover | 978-3-030-97383-4 | | isbn_ebook | 978-3-030-97381-0 | | copyright | Springer Nature Switzerland AG 2022 |
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发表于 2025-3-21 17:23:37
