Network-of-Network based -omics data integration

This research is carried out in the framework of Matheon supported by Einstein Foundation Berlin.

 
Project heads: Tim Conrad (FU), Christof Schütte (FU/ZIB)
Staff: Peter Koltai

Project Background

Pancreatic cancer is the fifth leading cause of cancer death in  Germany (see DKFZ Report, 2010). It is estimated that in 2030 it will be the second leading cause of cancer death incurring a cost of about 15,8 Billion US-Dollar worldwide to the public health systems.

Cancer is a systems disease

"Cancer is no more a disease of cells than a traffic jam is a disease  of cars. A lifetime of study of the internal-combustion engine would not help  anyone to understand our traffic problems.'" (Smithers1962). It is accepted that gene mutations are part of the process of cancer, but mutations alone are not enough. Cancer involves an interaction between neoplastic cells and surrounding tissue on many different levels, e.g. interaction of RNA molecules, proteins, and metabolites. But most available models are limited to only one or very few levels of interactions and describe a rather static view.

From single to multi source: data integration on a systems level

Current high-throughput -omics technologies have dramatically eased the production of part lists for a variety of organisms. What is still missing are the dynamic interactions among an organism's molecular parts, and the interactions between different biological levels, such as transcriptomics and proteomics. This is pivotal to better understanding of an organism's biology, and - in our case - to understand pancreas cancer.

Therefore, the aim of this project is two-fold: (1) use data acquired in our earlier projects to create a holistic integration of the aforementioned sources and levels for modeling pancreas cancer, which we call Network-of-Networks or short: NoN (in our context networks of different -omics levels, such as genomics, transcriptomics, proteomics and metabolomics. (2) A NoN is a very large and complex object and its structure differs significantly from other biological networks. Thus, new methods for complexity reduction and analyzing NoNs will be developed in this project.

The goal

In this project we aim to develop a new method that can be used to solve this task: the identification of minimal, yet robust fingerprints from very high-dimensional, noisy -omics data. Our method will be based on ideas from the areas of compressed sensing and machine learning.

Highlights

We developed methods for the analysis of undirected (e.g. protein-protein-interaction), directed (e.g. signal-transduction), and time-dependent biological network-or-networks. With these achievements we are now able to perform all the steps required for the full network-of-networks approach: find modules and other dominant structures (hubs, cycles) in large, not necessarily undirected multi-omics networks and follow their evolution in time. Concerning biological application we did first successful steps for the integration of transcriptomics, proteomics, and methylation data using network-of-network analysis.

Publications
  • R. Banisch, N. Djurdjevac, and Ch. Schutte. Reactive flows and unproductive cycles in irreversible markov chains. The European Physical Journal Special Topics, 224(12):2369{2387, September 2015.
  • N. Djurdjevac, Ralf Banisch, and Ch. Schutte. Modularity of directed networks: Cycle decomposition approach. Journal of Computational Dynamics, 2(1):1{24, August 2015.
  • P. Koltai and O. Junge. Quantized nonlinear feedback design by a split dynamic programming approach. Proceedings of the 21st International Symposium on Mathematical Theory of Networks and Systems (MTNS 2014), 2014.
  • Marco Sarich, Natasa Djurdjevac, Sharon Bruckner, Tim O. F. Conrad, and Christof Schutte. Modularity revisited: A novel dynamics-based concept for decomposing complex networks. Journal of Computational Dynamics, 1(1):191{212, 2014.
  • Ch. Schutte amd M. Sarich. A critical appraisal of Markov state models. The European Physical Journal Special Topics, 224(12):2445{2462, September 2015.
  • N. Djurdjevac, M.Weber, and Ch. Schuette. Finding dominant structures of nonreversible markov processes. Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal (submitted), 2015.
  • Marco Sarich and Christof Schutte. Utilizing hitting times for finding metastable sets in nonreversible Markov chains. submitted to Journal of Computational Dynamics, 2015.