Chapter title |
Mouse Models for Drug Discovery
|
---|---|
Chapter number | 2 |
Book title |
Mouse Models for Drug Discovery
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3661-8_2 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3659-5, 978-1-4939-3661-8
|
Authors |
Low, Benjamin E, Kutny, Peter M, Wiles, Michael V, Benjamin E. Low, Peter M. Kutny, Michael V. Wiles |
Editors |
Gabriele Proetzel, Michael V. Wiles |
Abstract |
Genetic modification of almost any species is now possible using approaches based on targeted nucleases. These novel tools now bypass previous limited species windows, allowing precision nucleotide modification of the genome at high efficiency, rapidly and economically. Here we focus on the modification of the mouse genome; the mouse, with its short generation time and comparatively low maintenance/production costs is the perfect mammal with which to probe the genome to understand its functions and complexities. Further, using targeted nucleases combined with homologous recombination, it is now possible to precisely tailor the genome, creating models of human diseases and conditions directly and efficiently in zygotes derived from any mouse strain. Combined these approaches make it possible to sequentially and progressively refine mouse models to better reflect human disease, test and develop therapeutics. Here, we briefly review the strategies involved in designing targeted nucleases (sgRNAs) providing solutions and outlining in detail the practical processes involved in precision targeting and modification of the mouse genome and the establishing of new precision genetically modified mouse lines. |
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Mendeley readers
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Unknown | 60 | 100% |
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Student > Ph. D. Student | 12 | 20% |
Student > Master | 8 | 13% |
Student > Bachelor | 5 | 8% |
Researcher | 4 | 7% |
Student > Doctoral Student | 3 | 5% |
Other | 7 | 12% |
Unknown | 21 | 35% |
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Biochemistry, Genetics and Molecular Biology | 15 | 25% |
Agricultural and Biological Sciences | 13 | 22% |
Medicine and Dentistry | 3 | 5% |
Neuroscience | 2 | 3% |
Computer Science | 1 | 2% |
Other | 3 | 5% |
Unknown | 23 | 38% |