Public Defence: Leiv Rønneberg
MSc Leiv Tore Salte Rønneberg at Institute of Basic Medical Sciences will be defending the thesis “Bayesian modelling of high-throughput drug combination screens: Accounting for uncertainty when searching for drug synergy” for the degree of PhD (Philosophiae Doctor).
An electronic copy of the thesis may be ordered from the faculty up to 2 days prior to the public defence. Inquiries regarding the thesis after the public defence must be addressed to the candidate.
Trial Lecture – time and place
See Trial Lecture.
- First opponent: Professor Juho Rousu, Department of Computer Science, University of Aalto
- Second opponent: Professor Andrea Riebler, Department of Mathematical Sciences, Norwegian University of Science and Technology
- Third member and chair of the evaluation committee: Professor Marit Inngjerdingen, University of Oslo
Chair of the Defence
Professor II Vessela Kristensen, Faculty of Medicine, University of Oslo
Associate Professor Manuela Zucknick, Faculty of Medicine, University of Oslo
High-throughput drug sensitivity screens in cancer allows large libraries of compounds to be tested in-vitro to determine how a certain cancer responds to various treatment options. When two or more drugs are combined, researchers are frequently interested in finding drug combinations that are synergistic, where the effect of administering drugs simultaneously is greater than administering each drug on their own.
Drug sensitivity screens are noisy by nature, making the exact quantification of synergistic drug effects difficult, and potentially misleading if the noise sources are not accounted for. In this thesis, tools for working with large drug combination datasets are developed that account for the inherent uncertainty in the data. Specifically, the tools can be used for:
- Quantification of synergistic effects and synergy scores, through a Bayesian semi-parametric model that accurately reports the uncertainty inherent in the estimates.
- Discovery of biomarkers for drug synergy, connecting molecular characteristics of cancer cells to drug synergy.
- Predicting dose-response and synergistic effects in unperformed experiments, using incomplete and noisy training data from previous experiments
Contact the research support staff.