Model selection and parameter estimation for root architecture models using likelihood-free inference

Research output: Contribution to journalArticle

Colleges, School and Institutes

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.

Details

Original languageEnglish
Article number20190293
JournalJournal of The Royal Society Interface
Volume16
Issue number156
Early online date10 Jul 2019
Publication statusPublished - Jul 2019

Keywords

  • approximate Bayesian computation, likelihood-free inference, root growth, root system architecture