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Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching

Jackson, S.E.; Vernon, I.; Liu, J.; Lindsey, K.

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Sam Jackson
Assistant Professor


A major challenge in plant developmental biology is to understand how plant growth is coordinated by interacting hormones and genes. To meet this challenge, it is important to not only use experimental data, but also formulate a mathematical model. For the mathematical model to best describe the true biological system, it is necessary to understand the parameter space of the model, along with the links between the model, the parameter space and experimental observations. We develop sequential history matching methodology, using Bayesian emulation, to gain substantial insight into biological model parameter spaces. This is achieved by finding sets of acceptable parameters in accordance with successive sets of physical observations. These methods are then applied to a complex hormonal crosstalk model for Arabidopsis root growth. In this application, we demonstrate how an initial set of 22 observed trends reduce the volume of the set of acceptable inputs to a proportion of 6.1 × 10−7 of the original space. Additional sets of biologically relevant experimental data, each of size 5, reduce the size of this space by a further three and two orders of magnitude respectively. Hence, we provide insight into the constraints placed upon the model structure by, and the biological consequences of, measuring subsets of observations.

Journal Article Type Article
Acceptance Date May 12, 2020
Online Publication Date Jul 13, 2020
Publication Date 2020-04
Deposit Date Aug 5, 2020
Publicly Available Date Jul 13, 2021
Journal Statistical Applications in Genetics and Molecular Biology
Print ISSN 2194-6302
Electronic ISSN 1544-6115
Publisher De Gruyter
Peer Reviewed Peer Reviewed
Volume 19
Issue 2
Article Number 20180053
Public URL


Published Journal Article (3.3 Mb)

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