Big Data integration and trans-Omics analysis for reconstructing relevant pathways and networks underlying neurodegenerative diseases

Risks and costs of neurodegenerative diseases constantly grow as the average expected age of humans increases. Due to population ageing - and according to the estimates of the WHO - the current net costs of 160 billions USD worldwide for such diseases like Alzheimer's disease will almost double during the next ten years. However, despite decades of research and despite of the considerable progress achieved in the identification of risk genes, relevant epigenetic modifications, potent biomarkers, environmental/latent risk factors, and dozens of disease-associated Single Nucleotide Polymorphisms (SNPs), the key conditional factors for an outbreak of several neurodegenerative diseases, e.g Alzheimer’s disease (AD) are still unknown. Also, the question whether there are commonalities in the various (patho)physiological processes associated with neurodegeneration remains yet unanswered. Practical implication of this lacking progress in research is the fact that the current medication for Alzheimer is not more efficient now then it was 20 years ago. It became, however, generally accepted that the underlying mechanisms are polyfactorial and depend on multiple (partly unknown) genetic and non-genetic variables, epigenetics and cellular component factors at different scales. 

Work of my research group (as well as of other colleagues in the field) will be introduced.  It aims at the point where the huge collections of disease-related data on various levels (involving Genomics-, Epigenomics-, Transcriptomics- and Proteomics data layers) have to be integrated – and subject to advanced computational and statistical methods on high-performance computing facilities.

 By making use of multi-omic measurements data – combined with co-designing new more advanced computational data integration methods for supercomputing facilities, the aim of this research is to reconstruct the global biochemical networks across multiple omic layers – and even across different diseases.

Susanne Gerber is currently Assistant Professor at the Johannes Gutenberg University of Mainz, associated both to the Faculty of Biology and the Center for Computational Sciences. She  obtained her PhD degree in 2011 from the Humboldt University of Berlin, Germany.  Afterwards, she did her Post-doc at the Università  della Svizzera Italiana in Illia Horenko’s Computational Time Series Group before she accepted the offer of an assistant professor-ship in 2015 and moved back to Germany. Her current research interests lie in the development of methodologies for the integration of Big biological data sets and analysis strategies for a better understanding of mechanisms underlying neurodegenerative diseases.  At the same time, she and her group are also investigating the other side of the medal, namely the biological background of healthy aging.