Simulation of Genes and Genomes Forward in Time

<!DOCTYPE html> <html lang="en"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-9F3B5VV3T3"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-9F3B5VV3T3'); </script> <meta charset="utf-8"> <title> Simulation of Genes and Genomes Forward in Time | Bentham Science </title> <meta content="IE=edge" http-equiv="X-UA-Compatible"> <meta content="width=device-width, initial-scale=1" name="viewport">  <meta content="Mhic0Wi9VUqC0HOgGf2RENOz0gETvoRv4tMSCemf" name="csrf-token">  <meta name="description" content="The importance of simulation software in current and future evolutionary and genomic studies is just confirmed by the recent publication of several new sim" /> <meta name="keywords" content=" Simulation of Genes and Genomes Forward in Time"> <meta property="og:type" content="website" /> <meta property="og:title" content=" Simulation of Genes and Genomes Forward in Time" /> <meta property="og:url" content="https://www.eurekaselect.com/article/15760" /> <meta property="og:image" content="http://www.eurekaselect.com/images/bentham/CG-2020.jpg" /> <meta property="og:description" content=" The importance of simulation software in current and future evolutionary and genomic studies is just confirmed by the recent publication of several new simulation tools. The forward-in-time simulation strategy has, therefore, re-emerged as a complement of coalescent simulation. Additionally, more efficient coalescent algorithms, the same as new ideas about the combined use of backward and forward strategies have recently appeared. In the present work, a previous review is updated to include some new forward simulation tools. When simulating at the genome-scale the conflict between efficiency (i.e. execution speed and memory usage) and flexibility (i.e. complex modeling capabilities) emerges. This is the pivot around which simulation of evolutionary processes should improve. In addition, some effort should be made to consider the process of developing simulation tools from the point of view of the software engineering theory. Finally, some new ideas and technologies as general purpose graphic processing units are commented. " /> <meta property="og:site_name" content="http://www.eurekaselect.com" /> <meta name="citation_title" content=" Simulation of Genes and Genomes Forward in Time" /> <meta name="citation_author" content="Antonio Carvajal-Rodriguez" /> <meta name="citation_publish_on" content="2012-03-01 " /> <meta name="citation_journal_title" content="Current Genomics"> <meta name="citation_volume" content="11"> <meta name="citation_issue" content="1"> <meta name="citation_firstpage" content="58"> <meta name="citation_lastpage" content="61"> <meta name="citation_pdf_url" content="http://www.eurekaselect.com/article/15760"> <meta name="citation_doi" content="10.2174/138920210790218007" /> <script defer="" src="https://www.eurekaselect.com/js/app.js"></script> <script type="text/javascript" src="https://www.eurekaselect.com/js/jquery.min.js"></script>  <script src="https://www.eurekaselect.com/js/all.min.js"></script>   <link href="https://www.eurekaselect.com/css/bootstrap.min.css" rel="stylesheet"> 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The forward-in-time simulation strategy has, therefore, re-emerged as a complement of coalescent simulation. Additionally, more efficient coalescent algorithms, the same as new ideas about the combined use of backward and forward strategies have recently appeared. In the present work, a previous review is updated to include some new forward simulation tools. When simulating at the genome-scale the conflict between efficiency (i.e. execution speed and memory usage) and flexibility (i.e. complex modeling capabilities) emerges. This is the pivot around which simulation of evolutionary processes should improve. In addition, some effort should be made to consider the process of developing simulation tools from the point of view of the software engineering theory. 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The forward-in-time simulation strategy has, therefore, re-emerged as a complement of coalescent simulation. Additionally, more efficient coalescent algorithms, the same as new ideas about the combined use of backward and forward strategies have recently appeared. In the present work, a previous review is updated to include some new forward simulation tools. When simulating at the genome-scale the conflict between efficiency (i.e. execution speed and memory usage) and flexibility (i.e. complex modeling capabilities) emerges. This is the pivot around which simulation of evolutionary processes should improve. In addition, some effort should be made to consider the process of developing simulation tools from the point of view of the software engineering theory. 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