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008			220901s2022 sz | s |||| 0|eng d
020			_a9783030973810
024	7		_a10.1007/978-3-030-97381-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aHaasl, Ryan J _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aNature in Silico _bPopulation Genetic Simulation and its Evolutionary Interpretation Using C++ and R _cby Ryan J Haasl
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVIII, 313 páginas) _b96 illus
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aIntroduction 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.
520			_aDramatic 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. .
776	0	8	_iPrinted edition: _z9783030973803
776	0	8	_iPrinted edition: _z9783030973827
776	0	8	_iPrinted edition: _z9783030973834
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-97381-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_BiomedLife_2022
999			_c393871 _d393871