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Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform
Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental per...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430029/ https://www.ncbi.nlm.nih.gov/pubmed/28555150 http://dx.doi.org/10.3389/fpls.2017.00786 |
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author | Marshall-Colon, Amy Long, Stephen P. Allen, Douglas K. Allen, Gabrielle Beard, Daniel A. Benes, Bedrich von Caemmerer, Susanne Christensen, A. J. Cox, Donna J. Hart, John C. Hirst, Peter M. Kannan, Kavya Katz, Daniel S. Lynch, Jonathan P. Millar, Andrew J. Panneerselvam, Balaji Price, Nathan D. Prusinkiewicz, Przemyslaw Raila, David Shekar, Rachel G. Shrivastava, Stuti Shukla, Diwakar Srinivasan, Venkatraman Stitt, Mark Turk, Matthew J. Voit, Eberhard O. Wang, Yu Yin, Xinyou Zhu, Xin-Guang |
author_facet | Marshall-Colon, Amy Long, Stephen P. Allen, Douglas K. Allen, Gabrielle Beard, Daniel A. Benes, Bedrich von Caemmerer, Susanne Christensen, A. J. Cox, Donna J. Hart, John C. Hirst, Peter M. Kannan, Kavya Katz, Daniel S. Lynch, Jonathan P. Millar, Andrew J. Panneerselvam, Balaji Price, Nathan D. Prusinkiewicz, Przemyslaw Raila, David Shekar, Rachel G. Shrivastava, Stuti Shukla, Diwakar Srinivasan, Venkatraman Stitt, Mark Turk, Matthew J. Voit, Eberhard O. Wang, Yu Yin, Xinyou Zhu, Xin-Guang |
author_sort | Marshall-Colon, Amy |
collection | PubMed |
description | Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop. |
format | Online Article Text |
id | pubmed-5430029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54300292017-05-29 Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform Marshall-Colon, Amy Long, Stephen P. Allen, Douglas K. Allen, Gabrielle Beard, Daniel A. Benes, Bedrich von Caemmerer, Susanne Christensen, A. J. Cox, Donna J. Hart, John C. Hirst, Peter M. Kannan, Kavya Katz, Daniel S. Lynch, Jonathan P. Millar, Andrew J. Panneerselvam, Balaji Price, Nathan D. Prusinkiewicz, Przemyslaw Raila, David Shekar, Rachel G. Shrivastava, Stuti Shukla, Diwakar Srinivasan, Venkatraman Stitt, Mark Turk, Matthew J. Voit, Eberhard O. Wang, Yu Yin, Xinyou Zhu, Xin-Guang Front Plant Sci Plant Science Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop. Frontiers Media S.A. 2017-05-15 /pmc/articles/PMC5430029/ /pubmed/28555150 http://dx.doi.org/10.3389/fpls.2017.00786 Text en Copyright © 2017 Marshall-Colon, Long, Allen, Allen, Beard, Benes, von Caemmerer, Christensen, Cox, Hart, Hirst, Kannan, Katz, Lynch, Millar, Panneerselvam, Price, Prusinkiewicz, Raila, Shekar, Shrivastava, Shukla, Srinivasan, Stitt, Turk, Voit, Wang, Yin and Zhu. 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 | Plant Science Marshall-Colon, Amy Long, Stephen P. Allen, Douglas K. Allen, Gabrielle Beard, Daniel A. Benes, Bedrich von Caemmerer, Susanne Christensen, A. J. Cox, Donna J. Hart, John C. Hirst, Peter M. Kannan, Kavya Katz, Daniel S. Lynch, Jonathan P. Millar, Andrew J. Panneerselvam, Balaji Price, Nathan D. Prusinkiewicz, Przemyslaw Raila, David Shekar, Rachel G. Shrivastava, Stuti Shukla, Diwakar Srinivasan, Venkatraman Stitt, Mark Turk, Matthew J. Voit, Eberhard O. Wang, Yu Yin, Xinyou Zhu, Xin-Guang Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform |
title | Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform |
title_full | Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform |
title_fullStr | Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform |
title_full_unstemmed | Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform |
title_short | Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform |
title_sort | crops in silico: generating virtual crops using an integrative and multi-scale modeling platform |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430029/ https://www.ncbi.nlm.nih.gov/pubmed/28555150 http://dx.doi.org/10.3389/fpls.2017.00786 |
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