Abstract
Computational models are useful tools to help understand signalling pathways in plant cells. A systems biology approach where models and experimental data are combined can provide experimentally verifiable predictions and novel insights. The brassinosteroid insensitive 1 (BRI1) receptor is one of the best-understood receptor systems in Arabidopsis with clearly described ligands, mutants and associated phenotypes. Therefore, BRI1-mediated signalling is attractive for mathematical modelling approaches to understand and interpret the spatial and temporal dynamics of signal transduction cascades in planta. To establish such a model, quantitative data sets incorporating local protein concentration, binding affinity and phosphorylation state of the different pathway components are essential. Computational modelling is increasingly employed in studies of plant growth and development. In this section, we have focused on the use of quantitative imaging of fluorescently labelled proteins as an entry point in modelling studies.
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