Understanding Hormonal Crosstalk in Arabidopsis Root Development via Emulation and History Matching

TL;DR

This paper introduces a Bayesian emulation and history matching approach to explore and constrain the parameter space of a complex hormonal crosstalk model in Arabidopsis root development, integrating experimental data to refine biological understanding.

Contribution

It develops a sequential history matching methodology using Bayesian emulation to effectively identify acceptable model parameters based on experimental observations.

Findings

Significantly reduces parameter space volume with initial observations.

Further constrains parameters with additional experimental data.

Provides insights into model structure constraints and biological implications.

Abstract

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 x 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.