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Abstract
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 characterise growth parameters in different mutants. Our highly adaptable framework can be used to gain mechanistic insight into the
15 generation of observed root system architectures.
15 generation of observed root system architectures.
Original language | English |
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Article number | 20190293 |
Journal | Journal of The Royal Society Interface |
Volume | 16 |
Issue number | 156 |
Early online date | 10 Jul 2019 |
DOIs | |
Publication status | Published - Jul 2019 |
Bibliographical note
Publisher Copyright:© 2019 The Authors.
Keywords
- approximate Bayesian computation
- likelihood-free inference
- root growth
- root system architecture
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Dive into the research topics of 'Model selection and parameter estimation for root architecture models using likelihood-free inference'. Together they form a unique fingerprint.Projects
- 1 Finished
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Multiscale modelling of three-dimensional plant root growth
Dyson, R. (Principal Investigator)
Engineering & Physical Science Research Council
5/11/14 → 4/11/16
Project: Research Councils