Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology
Global food insecurity and environmental degradation highlight the urgent need for more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology offers the potential for enhanced crop traits, it also entail...
| Autores principales: | , , , , , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
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MDPI
2025
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/21607 https://www.mdpi.com/2223-7747/14/3/470 https://doi.org/10.3390/plants14030470 |
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| author | Lucido, Abel Basallo, Oriol Marin-Sanguino, Alberto Eleiwa, Abderrahmane Martinez, Emilce Soledad Vilaprinyo, Ester Sorribas, Albert Alves, Rui |
| author_browse | Alves, Rui Basallo, Oriol Eleiwa, Abderrahmane Lucido, Abel Marin-Sanguino, Alberto Martinez, Emilce Soledad Sorribas, Albert Vilaprinyo, Ester |
| author_facet | Lucido, Abel Basallo, Oriol Marin-Sanguino, Alberto Eleiwa, Abderrahmane Martinez, Emilce Soledad Vilaprinyo, Ester Sorribas, Albert Alves, Rui |
| author_sort | Lucido, Abel |
| collection | INTA Digital |
| description | Global food insecurity and environmental degradation highlight the urgent need for more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology offers the potential for enhanced crop traits, it also entails risks of extensive environmental damage. This review highlights the complexities and risks associated with plant synthetic biology, while presenting the potential of multiscale mathematical modeling to assess and mitigate those risks effectively. Despite its potential, applying multiscale mathematical models in plants remains underutilized. Here, we advocate for integrating technological advancements in agricultural data analysis to develop a comprehensive understanding of crops across biological scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper establishes a foundation for creating and utilizing integrated multiscale mathematical models. Through modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, thereby enhancing the safety of genetically engineered species. To demonstrate the potential of this approach, ongoing efforts are highlighted to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga (Striga spp.) and drought. |
| format | info:ar-repo/semantics/artículo |
| id | INTA21607 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA216072025-03-10T11:31:36Z Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology Lucido, Abel Basallo, Oriol Marin-Sanguino, Alberto Eleiwa, Abderrahmane Martinez, Emilce Soledad Vilaprinyo, Ester Sorribas, Albert Alves, Rui Ciencia Vegetal Maíz Modelos Matemáticos Biología Sintética Plant Sciences Maize Mathematical Models Synthetic Biology Multiscale Global food insecurity and environmental degradation highlight the urgent need for more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology offers the potential for enhanced crop traits, it also entails risks of extensive environmental damage. This review highlights the complexities and risks associated with plant synthetic biology, while presenting the potential of multiscale mathematical modeling to assess and mitigate those risks effectively. Despite its potential, applying multiscale mathematical models in plants remains underutilized. Here, we advocate for integrating technological advancements in agricultural data analysis to develop a comprehensive understanding of crops across biological scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper establishes a foundation for creating and utilizing integrated multiscale mathematical models. Through modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, thereby enhancing the safety of genetically engineered species. To demonstrate the potential of this approach, ongoing efforts are highlighted to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga (Striga spp.) and drought. EEA Pergamino Fil: Lucido, Abel. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Lucido, Abel. Instituto de Investigación Biomédica de Lérida; España Fil: Lucido, Abel. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España Fil: Basallo, Oriol. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Basallo, Oriol. Instituto de Investigación Biomédica de Lérida; España Fil: Basallo, Oriol. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España Fil: Marin-Sanguino, Alberto. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Marin-Sanguino, Alberto. Instituto de Investigación Biomédica de Lérida; España Fil: Marin-Sanguino, Alberto. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España Fil: Eleiwa, Abderrahmane. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Eleiwa, Abderrahmane. Instituto de Investigación Biomédica de Lérida; España Fil: Eleiwa, Abderrahmane. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España Fil: Martinez, Emilce. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Pergamino. Laboratorio de Semillas. Banco Activo de Germoplasma; Argentina Fil: Martinez, Emilce. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Martinez, Emilce. Instituto de Investigación Biomédica de Lérida; España Fil: Vilaprinyo, Ester. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Vilaprinyo, Ester. Instituto de Investigación Biomédica de Lérida; España Fil: Vilaprinyo, Ester. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España Fil: Sorribas, Albert. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Sorribas, Albert. Instituto de Investigación Biomédica de Lérida; España Fil: Sorribas, Albert. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España Fil: Alves, Rui. Universidad de Lleida. Facultad de Medicina. Departamento de Ciencias Médicas Básicas. Grupo de Biología de Sistemas; España Fil: Alves, Rui. Instituto de Investigación Biomédica de Lérida; España Fil: Alves, Rui. MathSys2Bio. Grup de Recerca Consolidat de la Generalitat de Catalunya; España 2025-03-10T11:25:33Z 2025-03-10T11:25:33Z 2025-02 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/20.500.12123/21607 https://www.mdpi.com/2223-7747/14/3/470 2223-7747 (online) https://doi.org/10.3390/plants14030470 eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf MDPI Plants 14 (3) : 470. (February 2025) |
| spellingShingle | Ciencia Vegetal Maíz Modelos Matemáticos Biología Sintética Plant Sciences Maize Mathematical Models Synthetic Biology Multiscale Lucido, Abel Basallo, Oriol Marin-Sanguino, Alberto Eleiwa, Abderrahmane Martinez, Emilce Soledad Vilaprinyo, Ester Sorribas, Albert Alves, Rui Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology |
| title | Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology |
| title_full | Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology |
| title_fullStr | Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology |
| title_full_unstemmed | Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology |
| title_short | Multiscale mathematical modeling in systems biology : a framework to boost plant synthetic biology |
| title_sort | multiscale mathematical modeling in systems biology a framework to boost plant synthetic biology |
| topic | Ciencia Vegetal Maíz Modelos Matemáticos Biología Sintética Plant Sciences Maize Mathematical Models Synthetic Biology Multiscale |
| url | http://hdl.handle.net/20.500.12123/21607 https://www.mdpi.com/2223-7747/14/3/470 https://doi.org/10.3390/plants14030470 |
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