Analysis of Biological Processes from co-expression networks

Gene expression experiments measure the activity of thousands of genes in response to different conditions. Using this rich data we can attempt to answer a number of important biological questions.

The Guilt-by-Association (GBA) principle, according to which genes with similar expression profiles are functionally associated, is widely applied for functional analyses using large heterogeneous collections of transcriptomics data. We have carried out analysis that showed that the use of such large collections could hamper GBA functional analysis for genes whose expression is condition specific. In these cases, a smaller set of condition related experiments should instead be used and we introduced a semi-supervised algorithm that can select functionally relevant experiments from large collections of transcriptomics experiments.

We have worked on the problem of detecting, from gene expression data, which biological processes are activated in a given condition. Another question on which we worked is that of selecting marker genes which can be representative of specific biological mechanisms. In fact, these markers can be used as readouts and help understanding the mechanisms, monitor the interactions between them and track the physiological effect they may exert. For example, as yeast cells grow, genes involved in various hormone pathways exhibit distinct similarity in expression patterns and form groups. Sensitive and specific markers which can track and report the dynamics of each group are important for investigating the mechanisms of response to each hormone, crosstalk between hormone pathways and the relationship between hormones and phenotypic effects.

In our lab research for the analysis of transcriptomics data has been funded by the BBSRC (grant BB/F00964X/1) and Royal Holloway, through the Agnes Grace Ellen Endowment.

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