Model selection and parameter estimation for root architecture models using likelihood-free inference.
Model selection and parameter estimation for root architecture models using likelihood-free inference.
复制标题
使用无似然推理的根架构模型的模型选择和参数估计。
DOI：
10.1098/rsif.2019.0293
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Ziegler C
中科院分区：
文献类型：
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作者：
Ziegler C
植物根系在生物圈、环境和农业中发挥着至关重要的作用，但控制其生长和结构的定量原理仍然知之甚少。通过建模和模拟，已经很好地研究了给定模型和参数可以产生什么根形式的“正向问题”，但对“逆向问题”的关注相对较少：哪些模型和参数负责产生实验观察到的根系？在这里，我们建议使用近似贝叶斯计算（ABC）来推断控制根生长和结构的机械参数，使我们能够使用观察到的根结构来学习和量化参数和模型结构的不确定性。我们演示了该平台在合成和实验根数据上的使用，并展示了如何使用它来识别不同突变体的生长机制和表征生长参数。我们高度适应性的框架可用于获得对观察到的根系统架构的生成的机械洞察。
Plant root systems play vital roles in the biosphere, environment and agriculture, but the quantitative principles governing their growth and architecture remain poorly understood. The ‘forward problem’ of what root forms can arise from given models and parameters has been well studied through modelling and simulation, but comparatively little attention has been given to the ‘inverse problem’: what models and parameters are responsible for producing an experimentally observed root system? Here, we propose the use of approximate Bayesian computation (ABC) to infer mechanistic parameters governing root growth and architecture, allowing us to learn and quantify uncertainty in parameters and model structures using observed root architectures. We demonstrate the use of this platform on synthetic and experimental root data and show how it may be used to identify growth mechanisms and characterize growth parameters in different mutants. Our highly adaptable framework can be used to gain mechanistic insight into the generation of observed root system architectures.