Conclusions

The increased complexity of a biological system is achieved through multiple regulatory input points. Plant responses to stress are orchestrated through a network that integrates signalling pathways characterized primarily by the production of JA, SA and ET. The identified regulatory steps in the network highlight the intricacy of the signalling networks involving various levels of control ranging from transcriptional to post-translational.

In this chapter, we have looked mainly at stress related signalling pathways in Arabidopsis thaliana and at the way in which they are combined into large signalling networks. Particularly, we have shown examples of how bioinfor-matics techniques combining microarray data offer a novel way to identify genes in such networks. Experimental techniques, using reverse genetic and mutant analysis have been used to verify "in silico" predictions. An important goal of current research has also been that of defining mathematical models that can be used for simulating the transmission of signals in such networks, from the environmental stimuli to the cell responses.

However, it is becoming evident that inference of signalling networks solely from transcriptomics data has several limitations. It has been shown in some model organisms that the integration of transcriptomics data together with protein-protein interaction data is extremely useful for inferring signalling networks. Unfortunately this type of data is still very limited for Arabidopsis. We believe that when this data will become available it will lead to a better mechanistic explanation and identification of crucial nodes in signalling pathways.

Acknowledgements The authors are grateful to Dr. Virginia Balbi for critical reading of the manuscript. Alessandra Devoto and Alberto Paccanaro research is supported by the Biotechnological and Biological Sciences Research Council (BBSRC) and Engineering and Physical Sciences Research Council (EPSRC).

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