Practical steps to digital organism models, from laboratory model species to 'Crops in silico
© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissionsoup.com.
| Publié dans: | Journal of experimental botany. - 1985. - 70(2019), 9 vom: 29. Apr., Seite 2403-2418 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2019
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| Accès à la collection: | Journal of experimental botany |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Arabidopsis thaliana biochemical kinetics community standards computational modelling data science whole-cell modelling |
| Résumé: | © The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissionsoup.com. A recent initiative named 'Crops in silico' proposes that multi-scale models 'have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts' in plant science, particularly directed to crop species. To that end, the group called for 'a paradigm shift in plant modelling, from largely isolated efforts to a connected community'. 'Wet' (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in 'dry' (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from Systems Biology and crop modelling. Here we outline the approaches that seem most relevant to connected, 'digital organism' initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison with the Systems Biology Markup Language (SBML) community, we note computational resources and community structures that will help to realize the potential for plant Systems Biology to connect with a broader crop science community |
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| Description: | Date Completed 13.07.2020 Date Revised 15.02.2023 published: Print Citation Status MEDLINE |
| ISSN: | 1460-2431 |
| DOI: | 10.1093/jxb/ery435 |