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Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Group Leader Laura Cantini, Institut Pasteur, France
- Second opponent: Professor Markus List, Technical University of Munich, Germany
- Third member and chair of the evaluation committee: Associate professor Valeria Vitelli, University of Oslo
Chair of the Defence
Professor Tero Aittokallio, University of Oslo
Principal Supervisor
Group Leader, Marieke Kuijjer, University of Oslo
Summary
This thesis lies at the intersection of computational biology, network science, and cancer systems biology. It investigates whether gene regulatory networks (GRNs) can improve multi-omics data integration, enhance prediction of cancer patient survival, and be analyzed more effectively using scalable network methods. It also examines how accessibility and reproducibility of GRN analyses can be strengthened.
To address these questions, sample-specific GRNs were integrated with ten cancer multi-omics datasets from TCGA using the MOFA+ framework, with validation in an independent liver cancer cohort. A new algorithm, HEDGEHOG, was developed to detect hierarchical and overlapping communities in large bipartite biological networks and applied to 34 tissue-specific GRNs from GTEx. In parallel, an online platform, NetBooks, was created to provide reproducible workflows, code, and tutorials.
Results show that incorporating GRNs into multi-omics models improves associations with patient survival in several cancer types and highlights biologically meaningful pathways, including fatty-acid metabolism in liver cancer. HEDGEHOG identifies hundreds of stable network communities per tissue, many corresponding to known biological processes, demonstrating its value for interpreting large regulatory systems. The reproducibility resources further support transparent and reusable analyses.
Overall, the thesis demonstrates that integrating regulatory network information with multi-omics data yields more informative models of disease biology and that scalable network methods combined with open tools can substantially advance systems-level cancer research.
Additional information
Contact the research support staff.