Re-Designing Regulatory Networks Underlying Plant Stress Responses

Plant responses to abiotic and biotic stress involve large-scale transcriptional reprogramming. We are elucidating the gene regulatory networks underlying these transcriptional responses to the environment using a combination of experimental and computational/mathematical tools. We generated high-resolution time series expression data from Arabidopsis leaves following pathogen infection (bacterial and fungal), drought, and high light. This time series data has enabled us to resolve the chronology of these stress responses and identify transient changes in gene expression. We have generated transcriptional network models predicting regulatory relationships between differentially expressed transcription factors and used network features to identify key regulators of Arabidopsis stress responses. Network comparison has highlighted interactions common to multiple responses and those that appear to be more stress-specific. We have extended the network models by identifying groups of genes co-regulated across multiple stress responses and validated the models by experimentally testing regulatory predictions from simulations. Simulations of the network models can be used to predict how to enhance beneficial transcriptional responses to stress and as such increase the stress tolerance of Arabidopsis.