Integrative modeling and inference in high dimensional genomic and metabolic data

Measuring the state of a biological sample can be done in several ways, depending on the research question gene expression microarrays can quantify the expression levels thousands of genes simultaneously while various types of mass spectrometry can measure the abundance of chemical compounds in the sample. Typically one experiment would involve only one type of data which would then by analyzed accordingly. As it is well known that the biological systems investigated do not operate independently but affect each other, this approach is not providing a comprehensive picture of interacting processes.

This objective of this thesis is to provide modeling techniques that allow the combined analysis of microarray and LC–MS data in order to discover associations between genes and metabolic compounds, as well as tools that aid in making conclusions and comparisons of such findings. 

Academic advisors: Anders Tolver
Claus Ekstrøm
Kirsten Jørgensen
Søren Bak

Assessment committee:

Chairman Professor Carsten Wiuf, Math, University of Copenhagen

Ass Prof. Lars-Gustav Snipen Norwegian University of Life Sciences

Ass Prof. Bernt Guldbrandtsen,  Aarhus University