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Nature in silico = population geneti...

Haasl, Ryan J.

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Nature in silico = population genetic simulation and its evolutionary interpretation using C++ and R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nature in silico/ by Ryan J. Haasl.
Reminder of title:	population genetic simulation and its evolutionary interpretation using C++ and R /
Author:	Haasl, Ryan J.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xviii, 313 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Population genetics - Computer simulation. -
Online resource:	https://doi.org/10.1007/978-3-030-97381-0
ISBN:	9783030973810

Nature in silico = population genetic simulation and its evolutionary interpretation using C++ and R /
Haasl, Ryan J.

Nature in silicopopulation genetic simulation and its evolutionary interpretation using C++ and R /[electronic resource] :by Ryan J. Haasl. - Cham :Springer International Publishing :2022. - xviii, 313 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030973810

Standard No.: 10.1007/978-3-030-97381-0doiSubjects--Topical Terms:

3603807
Population genetics
--Computer simulation.

LC Class. No.: QH455

Dewey Class. No.: 576.58

Nature in silico = population genetic simulation and its evolutionary interpretation using C++ and R /
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520 $a 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.
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