{"title":"合成代谢途径的高通量评价。","authors":"Justin R Klesmith, Timothy A Whitehead","doi":"10.1142/S233954781640001X","DOIUrl":null,"url":null,"abstract":"<p><p>A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.</p>","PeriodicalId":22332,"journal":{"name":"TECHNOLOGY","volume":"4 1","pages":"9-14"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S233954781640001X","citationCount":"2","resultStr":"{\"title\":\"High-throughput evaluation of synthetic metabolic pathways.\",\"authors\":\"Justin R Klesmith, Timothy A Whitehead\",\"doi\":\"10.1142/S233954781640001X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.</p>\",\"PeriodicalId\":22332,\"journal\":{\"name\":\"TECHNOLOGY\",\"volume\":\"4 1\",\"pages\":\"9-14\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1142/S233954781640001X\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TECHNOLOGY\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S233954781640001X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2015/12/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TECHNOLOGY","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S233954781640001X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2015/12/16 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
High-throughput evaluation of synthetic metabolic pathways.
A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.
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