Statistical analysis of large-scale genome-wide biological datasets frequently results in predictions of large networks that are difficult to interpret or to effectively compare. We are investigating how plants respond to the environment, and the gene regulatory networks underlying these responses, with the aim of developing crops with increased tolerance to unfavourable environments. We have extensive time series gene expression profiles, gene regulatory network models and experimental validation data for multiple environmental stress conditions.

Pathogens suppress host immunity by targeting a range of secreted proteins, pathogenicity effectors, to the cytoplasm of host cells. These effectors, once they have traversed the host plasma-membrane, are targeted to many subcellular locations where they disrupt the host immune system to enable pathogen growth and reproduction. We have used the technique of matrix yeast two hybrid to identify the host targets of 200 pathogenicity effectors. This has revealed a complex and highly interconnected interaction network.