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Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305945/ https://www.ncbi.nlm.nih.gov/pubmed/34357339 http://dx.doi.org/10.3390/metabo11070445 |
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author | Tinte, Morena M. Chele, Kekeletso H. van der Hooft, Justin J. J. Tugizimana, Fidele |
author_facet | Tinte, Morena M. Chele, Kekeletso H. van der Hooft, Justin J. J. Tugizimana, Fidele |
author_sort | Tinte, Morena M. |
collection | PubMed |
description | Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions. |
format | Online Article Text |
id | pubmed-8305945 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83059452021-07-25 Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview Tinte, Morena M. Chele, Kekeletso H. van der Hooft, Justin J. J. Tugizimana, Fidele Metabolites Review Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions. MDPI 2021-07-08 /pmc/articles/PMC8305945/ /pubmed/34357339 http://dx.doi.org/10.3390/metabo11070445 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Tinte, Morena M. Chele, Kekeletso H. van der Hooft, Justin J. J. Tugizimana, Fidele Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview |
title | Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview |
title_full | Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview |
title_fullStr | Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview |
title_full_unstemmed | Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview |
title_short | Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview |
title_sort | metabolomics-guided elucidation of plant abiotic stress responses in the 4ir era: an overview |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305945/ https://www.ncbi.nlm.nih.gov/pubmed/34357339 http://dx.doi.org/10.3390/metabo11070445 |
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