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

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 syntheti

SUBMITTER: Ziegler C 

PROVIDER: S-EPMC6685013 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature