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Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants
Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulati...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617106/ https://www.ncbi.nlm.nih.gov/pubmed/26557642 http://dx.doi.org/10.3389/fbioe.2015.00167 |
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author | Hill, Camilla Beate Czauderna, Tobias Klapperstück, Matthias Roessner, Ute Schreiber, Falk |
author_facet | Hill, Camilla Beate Czauderna, Tobias Klapperstück, Matthias Roessner, Ute Schreiber, Falk |
author_sort | Hill, Camilla Beate |
collection | PubMed |
description | Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions. |
format | Online Article Text |
id | pubmed-4617106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-46171062015-11-09 Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants Hill, Camilla Beate Czauderna, Tobias Klapperstück, Matthias Roessner, Ute Schreiber, Falk Front Bioeng Biotechnol Bioengineering and Biotechnology Life on earth depends on dynamic chemical transformations that enable cellular functions, including electron transfer reactions, as well as synthesis and degradation of biomolecules. Biochemical reactions are coordinated in metabolic pathways that interact in a complex way to allow adequate regulation. Biotechnology, food, biofuel, agricultural, and pharmaceutical industries are highly interested in metabolic engineering as an enabling technology of synthetic biology to exploit cells for the controlled production of metabolites of interest. These approaches have only recently been extended to plants due to their greater metabolic complexity (such as primary and secondary metabolism) and highly compartmentalized cellular structures and functions (including plant-specific organelles) compared with bacteria and other microorganisms. Technological advances in analytical instrumentation in combination with advances in data analysis and modeling have opened up new approaches to engineer plant metabolic pathways and allow the impact of modifications to be predicted more accurately. In this article, we review challenges in the integration and analysis of large-scale metabolic data, present an overview of current bioinformatics methods for the modeling and visualization of metabolic networks, and discuss approaches for interfacing bioinformatics approaches with metabolic models of cellular processes and flux distributions in order to predict phenotypes derived from specific genetic modifications or subjected to different environmental conditions. Frontiers Media S.A. 2015-10-21 /pmc/articles/PMC4617106/ /pubmed/26557642 http://dx.doi.org/10.3389/fbioe.2015.00167 Text en Copyright © 2015 Hill, Czauderna, Klapperstück, Roessner and Schreiber. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Hill, Camilla Beate Czauderna, Tobias Klapperstück, Matthias Roessner, Ute Schreiber, Falk Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants |
title | Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants |
title_full | Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants |
title_fullStr | Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants |
title_full_unstemmed | Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants |
title_short | Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants |
title_sort | metabolomics, standards, and metabolic modeling for synthetic biology in plants |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4617106/ https://www.ncbi.nlm.nih.gov/pubmed/26557642 http://dx.doi.org/10.3389/fbioe.2015.00167 |
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