Integrative network-based approaches for modeling human disease

This thesis provides an overview of recent advances in the field of computational modeling of cellular systems, its major strengths, and limitations. We present various computational network-based approaches that integrate information from different regulatory levels to understand the mechanisms behind the onset and progression of multifactorial human disorders. For example, we report INTREGNET, a computational method for systematically identifying minimal sets of transcription factors (TFs) that can induce desired cellular transitions with increased efficiency. Furthermore, we introduce a novel integrative network-based approach for ranking Alzheimer’s disease (AD)-associated functional genetic and epigenetic variation. We also showed that particular pathways, such as sphingolipids (SL) function, are significantly dysregulated in AD. In-depth integrative analysis of these SL-related genes reveals their potential as biomarkers and for SL-targeted drug development for AD.