Gao Yonggang, Zhao Cheng
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Front Plant Sci. 2024 Oct 17;15:1361183. doi: 10.3389/fpls.2024.1361183. eCollection 2024.
Plant growth and development are characterized by systematic and continuous processes, each involving intricate metabolic coordination mechanisms. Mathematical models are essential tools for investigating plant growth and development, metabolic regulation networks, and growth patterns across different stages. These models offer insights into secondary metabolism patterns in plants and the roles of metabolites. The proliferation of data related to plant genomics, transcriptomics, proteomics, and metabolomics in the last decade has underscored the growing importance of mathematical modeling in this field. This review aims to elucidate the principles and types of metabolic models employed in studying plant secondary metabolism, their strengths, and limitations. Furthermore, the application of mathematical models in various plant systems biology subfields will be discussed. Lastly, the review will outline how mathematical models can be harnessed to address research questions in this context.
植物的生长和发育具有系统性和连续性过程，每个过程都涉及复杂的代谢协调机制。数学模型是研究植物生长发育、代谢调控网络以及不同阶段生长模式的重要工具。这些模型为了解植物次生代谢模式及代谢物的作用提供了见解。过去十年中，与植物基因组学、转录组学、蛋白质组学和代谢组学相关的数据激增，凸显了数学建模在该领域日益重要的地位。本综述旨在阐明用于研究植物次生代谢的代谢模型的原理、类型、优势及局限性。此外，还将讨论数学模型在植物系统生物学各个子领域的应用。最后，本综述将概述如何利用数学模型来解决这方面的研究问题。
