Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Wageningen, The Netherlands.
ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland.
Methods Mol Biol. 2022;2379:209-251. doi: 10.1007/978-1-0716-1791-5_13.
Mathematical modelling techniques are integral to current research in plant synthetic biology. Modelling approaches can provide mechanistic understanding of a system, allowing predictions of behaviour and thus providing a tool to help design and analyse biological circuits. In this chapter, we provide an overview of mathematical modelling methods and their significance for plant synthetic biology. Starting with the basics of dynamics, we describe the process of constructing a model over both temporal and spatial scales and highlight crucial approaches, such as stochastic modelling and model-based design. Next, we focus on the model parameters and the techniques required in parameter analysis. We then describe the process of selecting a model based on tests and criteria and proceed to methods that allow closer analysis of the system's behaviour. Finally, we highlight the importance of uncertainty in modelling approaches and how to deal with a lack of knowledge, noisy data, and biological variability; all aspects that play a crucial role in the cooperation between the experimental and modelling components. Overall, this chapter aims to illustrate the importance of mathematical modelling in plant synthetic biology, providing an introduction for those researchers who are working with or working on modelling techniques.
数学建模技术是当前植物合成生物学研究的重要组成部分。建模方法可以提供对系统的机制理解，允许对行为进行预测，从而提供帮助设计和分析生物回路的工具。在本章中，我们将概述数学建模方法及其对植物合成生物学的重要意义。从动力学的基础知识开始，我们描述了在时间和空间尺度上构建模型的过程，并强调了关键方法，如随机建模和基于模型的设计。接下来，我们重点介绍模型参数和参数分析所需的技术。然后，我们描述了基于测试和标准选择模型的过程，并介绍了允许更深入分析系统行为的方法。最后，我们强调了建模方法中不确定性的重要性以及如何处理知识不足、噪声数据和生物变异性；所有这些方面在实验和建模组件之间的合作中都起着至关重要的作用。总的来说，本章旨在说明数学建模在植物合成生物学中的重要性，为那些正在使用或致力于建模技术的研究人员提供介绍。
