Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Genome-wide or large-scale methodologies employed in functional genomics such as DNA sequencing, transcription profiling, proteomics, and metabolite profiling have become important tools in many metabolic engineering strategies. These techniques allow the identification of genetic differences and insight into their cellular effects. In the field of inverse metabolic engineering mapping of differences between strains with different degree of a certain desired phenotype and subsequent identification of factors conferring that phenotype are an essential part. Therefore, the tools of functional genomics in particular have the potential to promote and expand inverse metabolic engineering. Here, we review the use of functional genomics methods in inverse metabolic engineering, examples are presented, and we discuss the identification of targets for metabolic engineering with low fold changes using these techniques.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1016/j.ymben.2003.11.005 | DOI Listing |
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