Repository logo
 

Network approaches for data-driven reconstruction of intracellular signalling


Type

Thesis

Change log

Authors

Abstract

Intra-cellular signalling determines how cells process information. Through the integration of diverse chemical and physical stimuli, cells can enact transcription, among other changes to modulate growth, fate and survival. It is through the dysregulation of such processes that many diseases, including cancer originate. For many years, our study of signalling processes has been based on discrete ’pathways’, characterised mostly by small-scale studies. However, as more system-wide data becomes available, it is becoming increasingly obvious that intra-cellular signalling is more like a dense and inter-connected network, with intense and functional cross-talk between pathways. In my PhD project, I utilised both new ’omics’ data and a plethora of network-based approaches to guide our understanding of various signalling-related disease contexts. Networks provide a convenient framework with which to explicitly control the level of prior-knowledge required to understand complex ’omics’ data. Escaping the study bias that has so-far dominated our characterisation of intra-cellular signalling is vital if we are to progress in our understanding of complex cellular behaviours.

Description

Date

2022-09-28

Advisors

Petsalaki, Evangelia

Keywords

Bioinformatics, Melanoma, Networks, Phosphoproteomics, Systems biology, Transcriptomic

Qualification

Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Sponsorship
EMBL International PhD Program