2008
DOI: 10.1186/1471-2105-9-499
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Natural computation meta-heuristics for the in silico optimization of microbial strains

Abstract: Background: One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been … Show more

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Cited by 98 publications
(113 citation statements)
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“…can be implemented through gene knockouts). Comparing both meta-heuristics for optimization, we observe that the SA and EA shown very similar performances, but the SA confirms a slight advantage, already reported in [11].…”
Section: Discussionsupporting
confidence: 70%
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“…can be implemented through gene knockouts). Comparing both meta-heuristics for optimization, we observe that the SA and EA shown very similar performances, but the SA confirms a slight advantage, already reported in [11].…”
Section: Discussionsupporting
confidence: 70%
“…In both cases, we show the results for our current approach using transcriptional information, compared to the results using the previous method based on reaction deletions [11]. It should be emphasized that all the setup is the same for both cases.…”
Section: Resultsmentioning
confidence: 99%
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