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A major challenge in plant developmental biology is to understand how plantgrowth is coordinated by interacting hormones and genes. To meet thischallenge, it is important to not only use experimental data, but alsoformulate a mathematical model. For the mathematical model to best describe thetrue biological system, it is necessary to understand the parameter space ofthe model, along with the links between the model, the parameter space andexperimental observations. We develop sequential history matching methodology,using Bayesian emulation, to gain substantial insight into biological modelparameter spaces. This is achieved by finding sets of acceptable parameters inaccordance with successive sets of physical observations. These methods arethen applied to a complex hormonal crosstalk model for Arabidopsis root growth.In this application, we demonstrate how an initial set of 22 observed trendsreduce the volume of the set of acceptable inputs to a proportion of 6.1 x10^(-7) of the original space. Additional sets of biologically relevantexperimental data, each of size 5, reduce the size of this space by a furtherthree and two orders of magnitude respectively. Hence, we provide insight intothe constraints placed upon the model structure by, and the biologicalconsequences of, measuring subsets of observations.

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