Regulatory networks and computational systems biology
Integrative data analysis of multilevel omics data sets is considered the key element in a systems biology / systems medicine approach to derive novel insights about molecular pathways involved in disease aetiology and progression to be used for diagnosis and therapeutic intervention. In particular it is becoming evident that each molecular layer in the flow of genetic information from DNA, RNA to proteins, which are the main effector molecules that determine metabolite levels, contains unique information about the disease process. The overarching aim of this subproject is to characterize molecular pathways and regulatory mechanisms involved in disease aetiology and progression by using integrative data analysis and multilevel modelling. In particular we will:
• Identify deregulated key transcription factors and their target genes using differential expression results in a case control setup.
• Identify posttranscriptional regulatory mechanisms using integrated analysis of deregulated miRNAs and their mRNA and protein targets.
• Identify candidate causal variants for published and GHS GWA loci using heart eQTL data, DNA methylation data and publicly available chromatin data in conjunction with computational sequence analysis.
• Integrate the components of the AF associated regulatory networks, relate them to metabolite concentrations and translate results to potential blood-based omics markers.