2018/01/04 by Samuel E. Jackson, Ian Vernon, Jackson, Samuel E. +5
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Applications (stat.AP) #FOS: Computer and information sciences #Genetic Mapping and Diversity in Plants and Animals #Plant nutrient uptake and metabolism #Scientific Research and Discoveries #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.1801.01538
openalex publication_date 2018/01/04 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
A major challenge in plant developmental biology is to understand how plant\ngrowth is coordinated by interacting hormones and genes. To meet this\nchallenge, it is important to not only use experimental data, but also\nformulate a mathematical model. For the mathematical model to best describe the\ntrue biological system, it is necessary to understand the parameter space of\nthe model, along with the links between the model, the parameter space and\nexperimental observations. We develop sequential history matching methodology,\nusing Bayesian emulation, to gain substantial insight into biological model\nparameter spaces. This is achieved by finding sets of acceptable parameters in\naccordance with successive sets of physical observations. These methods are\nthen applied to a complex hormonal crosstalk model for Arabidopsis root growth.\nIn this application, we demonstrate how an initial set of 22 observed trends\nreduce the volume of the set of acceptable inputs to a proportion of 6.1 x\n10^(-7) of the original space. Additional sets of biologically relevant\nexperimental data, each of size 5, reduce the size of this space by a further\nthree and two orders of magnitude respectively. Hence, we provide insight into\nthe constraints placed upon the model structure by, and the biological\nconsequences of, measuring subsets of observations.\n