ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality the...

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Autores principales:	Zixiang Xu, Ping Zheng, Jibin Sun, Yanhe Ma
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0d683363b0124fb6a9cc32b118e7a33a
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spelling	oai:doaj.org-article:0d683363b0124fb6a9cc32b118e7a33a2021-11-18T08:42:34ZReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.1932-620310.1371/journal.pone.0072150https://doaj.org/article/0d683363b0124fb6a9cc32b118e7a33a2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24348984/?tool=EBIhttps://doaj.org/toc/1932-6203Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.Zixiang XuPing ZhengJibin SunYanhe MaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e72150 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zixiang Xu Ping Zheng Jibin Sun Yanhe Ma ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
description	Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.
format	article
author	Zixiang Xu Ping Zheng Jibin Sun Yanhe Ma
author_facet	Zixiang Xu Ping Zheng Jibin Sun Yanhe Ma
author_sort	Zixiang Xu
title	ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
title_short	ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
title_full	ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
title_fullStr	ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
title_full_unstemmed	ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
title_sort	reacknock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0d683363b0124fb6a9cc32b118e7a33a
work_keys_str_mv	AT zixiangxu reacknockidentifyingreactiondeletionstrategiesformicrobialstrainoptimizationbasedongenomescalemetabolicnetwork AT pingzheng reacknockidentifyingreactiondeletionstrategiesformicrobialstrainoptimizationbasedongenomescalemetabolicnetwork AT jibinsun reacknockidentifyingreactiondeletionstrategiesformicrobialstrainoptimizationbasedongenomescalemetabolicnetwork AT yanhema reacknockidentifyingreactiondeletionstrategiesformicrobialstrainoptimizationbasedongenomescalemetabolicnetwork
_version_	1718421426228166656

ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

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