Abstract

It is well established that signalling responses happen through complex networks. However, most signalling research still uses linear pathways as the ground truth. Moreover, signalling responses are highly dependent on context, such as tissue type, genetic background etc and therefore these static pathways are not always suitable. There is also a high bias in the literature towards kinases and pathways for which reagents and prior knowledge is readily available. This leaves a huge dark space in our understanding of cell signalling and significantly hinders studies of its general principles.

Our group uses data-driven and network-based approaches to understand and describe the organisation principles of cell signalling that allow the diverse and context-specific cell responses and phenotypes.

In this talk I will showcase different network-based methods that we have developed and/or use to extract phenotype-specific networks from omics data and use it to study different diseases. First, I will present a method that combines paired transcriptomics and imaging data to extract context-specific signalling networks, with the context in this case cell shape in breast cancer. The method is generalisable to any paired transcriptomics/phenotype data, and I will briefly mention how we have extended it to study the disease progression of non-alcoholic fatty liver disease. I will finally present a project where we integrated RNAseq, ATA Cseq and ChIP-seq data to create a network representative of endothelial dysfunction and performed in silico perturbations to identify potential targets for the condition.