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Flux-sum analysis identifies metabolite targets for strain improvement

BACKGROUND: Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under...

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Autores principales: Lakshmanan, Meiyappan, Kim, Tae Yong, Chung, Bevan K. S., Lee, Sang Yup, Lee, Dong-Yup
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625974/
https://www.ncbi.nlm.nih.gov/pubmed/26510838
http://dx.doi.org/10.1186/s12918-015-0198-3
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author Lakshmanan, Meiyappan
Kim, Tae Yong
Chung, Bevan K. S.
Lee, Sang Yup
Lee, Dong-Yup
author_facet Lakshmanan, Meiyappan
Kim, Tae Yong
Chung, Bevan K. S.
Lee, Sang Yup
Lee, Dong-Yup
author_sort Lakshmanan, Meiyappan
collection PubMed
description BACKGROUND: Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets. RESULTS: In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology. CONCLUSIONS: The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-46259742015-10-30 Flux-sum analysis identifies metabolite targets for strain improvement Lakshmanan, Meiyappan Kim, Tae Yong Chung, Bevan K. S. Lee, Sang Yup Lee, Dong-Yup BMC Syst Biol Research BACKGROUND: Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets. RESULTS: In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology. CONCLUSIONS: The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-29 /pmc/articles/PMC4625974/ /pubmed/26510838 http://dx.doi.org/10.1186/s12918-015-0198-3 Text en © Lakshmanan et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Lakshmanan, Meiyappan
Kim, Tae Yong
Chung, Bevan K. S.
Lee, Sang Yup
Lee, Dong-Yup
Flux-sum analysis identifies metabolite targets for strain improvement
title Flux-sum analysis identifies metabolite targets for strain improvement
title_full Flux-sum analysis identifies metabolite targets for strain improvement
title_fullStr Flux-sum analysis identifies metabolite targets for strain improvement
title_full_unstemmed Flux-sum analysis identifies metabolite targets for strain improvement
title_short Flux-sum analysis identifies metabolite targets for strain improvement
title_sort flux-sum analysis identifies metabolite targets for strain improvement
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4625974/
https://www.ncbi.nlm.nih.gov/pubmed/26510838
http://dx.doi.org/10.1186/s12918-015-0198-3
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