Determining and interpreting correlations in lipidomic networks found in glioblastoma cells
1 Department of Computer Science, Karlsruhe Institute of Technology, Karlsruhe D-76128, Germany
2 Department of Scientific Computing, Florida State University, Tallahassee, Florida 32310-4120, USA
3 Ion Cyclotron Resonance Program, National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310-4005, USA
4 Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA
5 The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
BMC Systems Biology 2010, 4:126 doi:10.1186/1752-0509-4-126Published: 7 September 2010
Intelligent and multitiered quantitative analysis of biological systems rapidly evolves to a key technique in studying biomolecular cancer aspects. Newly emerging advances in both measurement as well as bio-inspired computational techniques have facilitated the development of lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases.
We present computational approaches to study the response of glioblastoma U87 cells to gene- and chemo-therapy. To identify distinct biomarkers and differences in therapeutic outcomes, we develop a novel technique based on graph-clustering. This technique facilitates the exploration and visualization of co-regulations in glioblastoma lipid profiling data. We investigate the changes in the correlation networks for different therapies and study the success of novel gene therapies targeting aggressive glioblastoma.
The novel computational paradigm provides unique "fingerprints" by revealing the intricate interactions at the lipidome level in glioblastoma U87 cells with induced apoptosis (programmed cell death) and thus opens a new window to biomedical frontiers.