Literature DB >> 20581192

Method for designing and optimizing random-search libraries for strain improvement.

Daniel Klein-Marcuschamer1, Gregory Stephanopoulos.   

Abstract

Random searches have been the hallmark of directed evolution and have been extensively employed in the improvement of complex or poorly understood phenotypes such as tolerance to toxic compounds in the context of cellular engineering. While genome-wide mutagenesis followed by selection or screening has been a traditional means of phenotype improvement, the list of experimental methods for cellular engineering based on random searches is rapidly expanding. Adding to the confusion is the element of chance, which lengthens the process and most notably adds to the cost of phenotypic improvement programs. Here we present a method to systematize the effort of finding superior mutants by successively improving random libraries. The method, based on the quantification of phenotypic diversity, is then used to isolate more-robust strains.

Mesh:

Year:  2010        PMID: 20581192      PMCID: PMC2918980          DOI: 10.1128/AEM.00828-10

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  23 in total

1.  Computational method to reduce the search space for directed protein evolution.

Authors:  C A Voigt; S L Mayo; F H Arnold; Z G Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2001-03-27       Impact factor: 11.205

2.  Genome shuffling of Lactobacillus for improved acid tolerance.

Authors:  Ranjan Patnaik; Susan Louie; Vesna Gavrilovic; Kim Perry; Willem P C Stemmer; Chris M Ryan; Stephen del Cardayré
Journal:  Nat Biotechnol       Date:  2002-07       Impact factor: 54.908

3.  Phenotypic alteration of eukaryotic cells using randomized libraries of artificial transcription factors.

Authors:  Kyung-Soon Park; Dong-ki Lee; Horim Lee; Yangsoon Lee; Young-Soon Jang; Yong Ha Kim; Hyo-Young Yang; Seong-il Lee; Wongi Seol; Jin-Soo Kim; Sung-Il Lee
Journal:  Nat Biotechnol       Date:  2003-09-07       Impact factor: 54.908

4.  DNA sequences of promoter regions for the str and spc ribosomal protein operons in E. coli.

Authors:  L E Post; A E Arfsten; F Reusser; M Nomura
Journal:  Cell       Date:  1978-09       Impact factor: 41.582

5.  Low copy number plasmids for regulated low-level expression of cloned genes in Escherichia coli with blue/white insert screening capability.

Authors:  C G Lerner; M Inouye
Journal:  Nucleic Acids Res       Date:  1990-08-11       Impact factor: 16.971

6.  Solution structure of the activator contact domain of the RNA polymerase alpha subunit.

Authors:  Y H Jeon; T Negishi; M Shirakawa; T Yamazaki; N Fujita; A Ishihama; Y Kyogoku
Journal:  Science       Date:  1995-12-01       Impact factor: 47.728

7.  Functional map of the alpha subunit of Escherichia coli RNA polymerase: insertion analysis of the amino-terminal assembly domain.

Authors:  M Kimura; A Ishihama
Journal:  J Mol Biol       Date:  1995-05-12       Impact factor: 5.469

8.  DNA-binding determinants of the alpha subunit of RNA polymerase: novel DNA-binding domain architecture.

Authors:  T Gaal; W Ross; E E Blatter; H Tang; X Jia; V V Krishnan; N Assa-Munt; R H Ebright; R L Gourse
Journal:  Genes Dev       Date:  1996-01-01       Impact factor: 11.361

9.  Promoter recognition and promoter strength in the Escherichia coli system.

Authors:  M Brunner; H Bujard
Journal:  EMBO J       Date:  1987-10       Impact factor: 11.598

10.  Whole-genome resequencing of Escherichia coli K-12 MG1655 undergoing short-term laboratory evolution in lactate minimal media reveals flexible selection of adaptive mutations.

Authors:  Tom M Conrad; Andrew R Joyce; M Kenyon Applebee; Christian L Barrett; Bin Xie; Yuan Gao; Bernhard Ø Palsson
Journal:  Genome Biol       Date:  2009-10-22       Impact factor: 13.583

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  2 in total

1.  Computational identification of adaptive mutants using the VERT system.

Authors:  James Winkler; Katy C Kao
Journal:  J Biol Eng       Date:  2012-04-02       Impact factor: 4.355

Review 2.  Combinatorial approaches for inverse metabolic engineering applications.

Authors:  Georgios Skretas; Fragiskos N Kolisis
Journal:  Comput Struct Biotechnol J       Date:  2013-03-11       Impact factor: 7.271

  2 in total

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