title: Modelling signaling networks underlying plant defence.
abstract: Transcriptional reprogramming plays a significant role in governing plant responses to pathogens. The underlying regulatory networks are complex and dynamic, responding to numerous input signals. Most network modelling studies to date have used large-scale expression data sets from public repositories but defence network models with predictive ability have also been inferred from single time series data sets, and sophisticated biological insights generated from focused experiments containing multiple network perturbations. Using multiple network inference methods, or combining network inference with additional data, such as promoter motifs, can enhance the ability of the model to predict gene function or regulatory relationships. Network topology can highlight key signaling components and provides a systems level understanding of plant defence.
authors: Department of Life Sciences, Imperial College London, Silwood Park SL5 7PY, UK.
authors: Oliver
authors: O
authors: Windram
authors: School of Life Sciences and Warwick Systems Biology Centre, University of Warwick, CV4 7AL, UK. Electronic address: k.j.denby@warwick.ac.uk.
authors: Katherine J
authors: KJ
authors: Denby
mesh_terms: D019295:Computational Biology; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D010944:Plants / Q000276:immunology*; D015398:Signal Transduction