Lucido Abel, Basallo Oriol, Marin-Sanguino Alberto, Eleiwa Abderrahmane, Martinez Emilce Soledad, Vilaprinyo Ester, Sorribas Albert, Alves Rui
Systems Biology Group, Department Ciències Mèdiques Bàsiques, Faculty of Medicine, Universitat de Lleida, 25008 Lleida, Spain.
Institut de Recerca Biomèdica IRBLleida, 25198 Lleida, Spain.
Plants (Basel). 2025 Feb 5;14(3):470. doi: 10.3390/plants14030470.
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 ( L.), engineered through synthetic biology to enhance resilience against ( spp.) and drought.
全球粮食不安全和环境退化凸显了对更可持续农业解决方案的迫切需求。植物合成生物学成为开发此类解决方案的一条有前景但也有风险的途径。虽然合成生物学为增强作物性状提供了潜力，但它也带来了广泛环境破坏的风险。本综述强调了与植物合成生物学相关的复杂性和风险，同时展示了多尺度数学建模有效评估和减轻这些风险的潜力。尽管有潜力，但多尺度数学模型在植物中的应用仍未得到充分利用。在此，我们主张整合农业数据分析中的技术进步，以全面了解跨生物尺度的作物。通过回顾适用于植物的常见建模方法和技术，本文为创建和利用综合多尺度数学模型奠定了基础。通过参数估计、分岔分析和敏感性分析等建模技术，研究人员可以识别突变靶点并预测多效性效应，从而提高基因工程物种的安全性。为了证明这种方法的潜力，文中强调了正在进行的努力，即开发一个用于玉米（L.）的综合多尺度数学模型，该玉米通过合成生物学进行工程改造，以增强对（spp.）和干旱的抵御能力。
