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Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways
Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an impro...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5958428/ https://www.ncbi.nlm.nih.gov/pubmed/29795980 http://dx.doi.org/10.1177/1177932218775076 |
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author | Tominaga, Daisuke Kawaguchi, Hideo Hori, Yoshimi Hasunuma, Tomohisa Ogino, Chiaki Aburatani, Sachiyo |
author_facet | Tominaga, Daisuke Kawaguchi, Hideo Hori, Yoshimi Hasunuma, Tomohisa Ogino, Chiaki Aburatani, Sachiyo |
author_sort | Tominaga, Daisuke |
collection | PubMed |
description | Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an improvement, doing so is not easy. Numerous reaction steps have been reported; however, the actual reaction steps activated vary or change according to the conditions. Furthermore, to build mathematical models for a dynamical analysis, the reaction mechanisms and parameter values must be known; however, to date, sufficient information has yet to be published for many cases. In addition, experimental observations are expensive. A new mathematical approach that is applicable to small sample data, and that requires no detailed reaction information, is strongly needed. S-system is one such model that can use smaller samples than other ordinary differential equation models. We propose a simplified S-system to apply minimal quantities of samples for a dynamic analysis of the metabolic pathways. We applied the model to the phenyl lactate production pathway of Escherichia coli. The model obtained suggests that actually activated reaction steps and feedback are inhibitions within the pathway. |
format | Online Article Text |
id | pubmed-5958428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-59584282018-05-24 Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways Tominaga, Daisuke Kawaguchi, Hideo Hori, Yoshimi Hasunuma, Tomohisa Ogino, Chiaki Aburatani, Sachiyo Bioinform Biol Insights Original Research Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an improvement, doing so is not easy. Numerous reaction steps have been reported; however, the actual reaction steps activated vary or change according to the conditions. Furthermore, to build mathematical models for a dynamical analysis, the reaction mechanisms and parameter values must be known; however, to date, sufficient information has yet to be published for many cases. In addition, experimental observations are expensive. A new mathematical approach that is applicable to small sample data, and that requires no detailed reaction information, is strongly needed. S-system is one such model that can use smaller samples than other ordinary differential equation models. We propose a simplified S-system to apply minimal quantities of samples for a dynamic analysis of the metabolic pathways. We applied the model to the phenyl lactate production pathway of Escherichia coli. The model obtained suggests that actually activated reaction steps and feedback are inhibitions within the pathway. SAGE Publications 2018-05-16 /pmc/articles/PMC5958428/ /pubmed/29795980 http://dx.doi.org/10.1177/1177932218775076 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Tominaga, Daisuke Kawaguchi, Hideo Hori, Yoshimi Hasunuma, Tomohisa Ogino, Chiaki Aburatani, Sachiyo Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways |
title | Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways |
title_full | Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways |
title_fullStr | Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways |
title_full_unstemmed | Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways |
title_short | Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways |
title_sort | mathematical model for small size time series data of bacterial secondary metabolic pathways |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5958428/ https://www.ncbi.nlm.nih.gov/pubmed/29795980 http://dx.doi.org/10.1177/1177932218775076 |
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