Show simple item record

dc.contributor.authorLun, Desmond S.en_US
dc.contributor.authorRockwell, Grahamen_US
dc.contributor.authorGuido, Nicholas J.en_US
dc.contributor.authorBaym, Michaelen_US
dc.contributor.authorKelner, Jonathan A.en_US
dc.contributor.authorBerger, Bonnieen_US
dc.contributor.authorGalagan, James E.en_US
dc.contributor.authorChurch, George M.en_US
dc.date.accessioned2012-01-11T21:10:25Z
dc.date.available2012-01-11T21:10:25Z
dc.date.issued2009-08-18
dc.identifier.citationLun, Desmond S, Graham Rockwell, Nicholas J Guido, Michael Baym, Jonathan A Kelner, Bonnie Berger, James E Galagan, George M Church. "Large-scale identification of genetic design strategies using local search" Molecular Systems Biology 5:296. (2009)
dc.identifier.issn1744-4292
dc.identifier.urihttps://hdl.handle.net/2144/3204
dc.description.abstractIn 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.isoen
dc.publisherNature Publishing Groupen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/
dc.subjectBi-level optimizationen_US
dc.subjectFlux balance analysisen_US
dc.subjectMetabolic engineeringen_US
dc.subjectMixed-integer linear programmingen_US
dc.subjectStrain optimizationen_US
dc.titleLarge-Scale Identification of Genetic Design Strategies Using Local Searchen_US
dc.typeArticleen_US
dc.identifier.doi10.1038/msb.2009.57
dc.identifier.pmid19690565
dc.identifier.pmcid2736654


This item appears in the following Collection(s)

Show simple item record

This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits distribution and reproduction in any medium, provided the original author and source are credited. Creation of derivative works is permitted but the resulting work may be distributed only under the same or similar licence to this one. This licence does not permit commercial exploitation without specific permission.