Publication details for Dr Junli LiuJackson, S.E., Vernon, I., Liu, J. & Lindsey, K. (2020). Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching. Statistical Applications in Genetics and Molecular Biology 19(2): 20180053.
- Publication type: Journal Article
- ISSN/ISBN: 1544-6115 (electronic), 2194-6302 (print)
- DOI: 10.1515/sagmb-2018-0053
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
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.