Network approaches for data-driven reconstruction of intracellular signalling

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.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2022

Author dc:creator

• Barker, Charles

Advisor dc:contributor.advisor

• Petsalaki, Evangelia

• Bioinformatics
• Melanoma
• Networks
• Phosphoproteomics
• Systems biology
• Transcriptomic

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/6aa6db4a-c620-4235-97fb-2aee7177ca75/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

Author Identifier

0000-0002-5223-6838

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/349932