Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, an...

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Autores principales:	Joonhoon Kim, Jennifer L Reed, Christos T Maravelias
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c7f44eb9a7484ffc8195c92a22515e0f
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spelling	oai:doaj.org-article:c7f44eb9a7484ffc8195c92a22515e0f2021-11-04T06:08:52ZLarge-scale bi-level strain design approaches and mixed-integer programming solution techniques.1932-620310.1371/journal.pone.0024162https://doaj.org/article/c7f44eb9a7484ffc8195c92a22515e0f2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21949695/?tool=EBIhttps://doaj.org/toc/1932-6203The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.Joonhoon KimJennifer L ReedChristos T MaraveliasPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 9, p e24162 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joonhoon Kim Jennifer L Reed Christos T Maravelias Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
description	The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.
format	article
author	Joonhoon Kim Jennifer L Reed Christos T Maravelias
author_facet	Joonhoon Kim Jennifer L Reed Christos T Maravelias
author_sort	Joonhoon Kim
title	Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
title_short	Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
title_full	Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
title_fullStr	Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
title_full_unstemmed	Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
title_sort	large-scale bi-level strain design approaches and mixed-integer programming solution techniques.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/c7f44eb9a7484ffc8195c92a22515e0f
work_keys_str_mv	AT joonhoonkim largescalebilevelstraindesignapproachesandmixedintegerprogrammingsolutiontechniques AT jenniferlreed largescalebilevelstraindesignapproachesandmixedintegerprogrammingsolutiontechniques AT christostmaravelias largescalebilevelstraindesignapproachesandmixedintegerprogrammingsolutiontechniques
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Large-scale bi-level strain design approaches and mixed-integer programming solution techniques.

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