Comparative network visualisation

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.

We now want to develop novel visualization methods to allow us to identify key organizing principles, structure, commonality and specificity within the network models. Similarities and unique features of the different stress responses can then be revealed and compared. This will require the development of new visualization techniques for understanding large networks through multiple small diagrams that show those aspects of the network relevant to a particular task or query. Part of the project will be to develop visual representations for networks with many edges that are not too cluttered.

To apply (deadline: May 15th) or for further information please contact Dr Katherine Denby ( or Prof. Kim Marriott (