Modeling proteomics data for investigating plant response to environmental stress

Over the last decade, there has been large scale improvement in our ability to detect the expression of genes and their products (proteins) and how differences in gene expression are associated with differences in an organisms' characteristics (e.g. plant growth). Current technology allows us to observe a snapshot of gene expression and protein production at a moment in time. The information from such a snapshot can then be used to create a crude pathway of expression - linking together the functions of the expressed genes to the characteristics of the organism.

Although these crude pathways indicate which genes are important for a particular characteristic, it doesn't fully explain system function - how the expression of each gene is related to each other. This is important for understanding how interactions between genes and proteins regulate the function of the whole system. Pathways then become components within a network of expression, aided by snapshots that are taken at multiple time-points.

To create networks of expression, a major development in innovative approaches to modelling gene expression data is now needed. Although data modelling and gene expression analysis are complementary techniques, they have not previously been combined and there is a lack of researchers with both skill sets. This project seeks to fill these gaps in knowledge and skills. The Co-I, who has extensive experience in 'Omics technologies) will be trained in state-of-the art modelling approaches for the analysis of biological systems. This training will then enable the Co-I to develop multivariate and structural equation modelling approaches to investigate system function at the level of gene and protein expression. Finally, these novel approaches will be used to analyze the relationship between environmental change and change in gene - phenotype networks using a large database with which the Co-I has produced with Nafferton Ecological Farming Group (NEFG).

This project is expected to generate innovative approaches to gene expression data analysis of benefit to the wider researcher community, and enhance UK leadership in environmental informatics. Furthermore, it will promote the knowledge exchange and further long-term collaborative frameworks between multi-disciplinary research communities.

We seek to develop a linkage between complex pathway and statistical modelling and the soil plant system that has been extensively studied by the Co-I. The project will not only expand the numerical skills and understanding of the Co-I, but will introduce a set of key skills into a subject area in a novel way. If successful in we anticipate that the results of this application will be a springboard for the use of these modelling approaches in analyzing complex gene-expression and other 'Omic systems.