Large-scale identification of genetic design strategies using local search

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Large-scale identification of genetic design strategies using local search

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dc.contributor.author Lun, Desmond S
dc.contributor.author Kelner, Jonathan A
dc.contributor.author Berger, Bonnie
dc.contributor.author Rockwell, Graham
dc.contributor.author Guido, Nicholas
dc.contributor.author Baym, Michael Hartmann
dc.contributor.author Galagan, James E
dc.contributor.author Church, George McDonald
dc.date.accessioned 2011-05-11T02:02:36Z
dc.date.issued 2009
dc.identifier.citation Lun, Desmond S., Graham Rockwell, Nicholas J. Guido, Michael Baym, Jonathan A. Kelner, Bonnie Berger, James E. Galagan, and George M. Church. 2009. Large-scale identification of genetic design strategies using local search. Molecular Systems Biology 5: 296. en_US
dc.identifier.issn 1744-4292 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4887111
dc.description.abstract In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing. en_US
dc.language.iso en_US en_US
dc.publisher Nature Publishing Group en_US
dc.relation.isversionof doi:10.1038/msb.2009.57 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2736654/pdf/ en_US
dash.license LAA
dc.title Large-scale identification of genetic design strategies using local search en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Molecular Systems Biology en_US
dash.depositing.author Guido, Nicholas
dc.date.available 2011-05-11T02:02:36Z
dash.affiliation.other HMS^Genetics en_US
dash.affiliation.other SPH^Immunology and Infectious Diseases TPH en_US
dash.affiliation.other HMS^Health Sciences and Technology en_US
dash.affiliation.other HMS^Genetics en_US

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