Probabilistic components of molecular interactions and drug responses ; Solutason lääkevasteiden ja molekyylivuorovaikutusten tilastollisia komponentteja

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto. fi Author Juuso Parkkinen Name of the doctoral dissertation Probabilistic components of molecular interactions and drug responses Publisher School of Science Unit Department of Information and Computer Science Series Aalto University publication series DOCTORAL DISSERTATIONS 105/2014 Field of research Bioinformatics Manuscript submitted 16 April 2014 Date of the defence 29 August 2014 Permission to publish granted (date) 4 June 2014 Language English Monograph Article dissertation (summary + original articles) Abstract A fundamental question in medicine is how cancer and other complex diseases operate on the molecular level. Identifying the detailed mechanisms and interactions of how diseases progress and respond to drug treatments is essential for developing effective therapies. Highthroughput molecular profiling technologies have provided vast amounts of measurement data of these phenomena. However, making sense of these masses of data is far from straightforward and requires advanced computational analysis methods.A fundamental question in medicine is how cancer and other complex diseases operate on the molecular level. Identifying the detailed mechanisms and interactions of how diseases progress and respond to drug treatments is essential for developing effective therapies. Highthroughput molecular profiling technologies have provided vast amounts of measurement data of these phenomena. However, making sense of these masses of data is far from straightforward and requires advanced computational analysis methods. Probabilistic component models have been proven an effective tool in analysing and integrating high-dimensional and noisy molecular profiling data sources, such as gene expression. Such models can identify coherent components from the data, and interpreting these components provides insights about the underlying biological processes, such as disease progression and drug responses. In this thesis, probabilistic component models are applied and extended to identify and analyse molecular interaction and drug response patterns. Identifying functionally coherent gene modules from high-throughput measurements is a central task in many biomedical applications. In this thesis, an earlier component model for network data is extended for capturing functional modules from combinations of gene expression and protein interaction data. The identified modules provide hypotheses for novel molecular pathways and protein functions. High-throughput drug treatment measurements have made possible the detailed analysis of molecular drug responses and toxicity. In this thesis, probabilistic component models are applied to detect coherent drug response patterns from gene expression data. These patterns provide detailed insights to drug mechanisms of action and are highly applicable in cancer therapy development. Moreover, by associating the identified drug response components to toxicological outcomes, the first comprehensive view of molecular toxicogenomic responses is constructed with high performance in drug toxicity prediction.