Determining and interpreting correlations in lipidomic networks found in glioblastoma cells.

Robert Görke, Anke Meyer-Bäse, Dorothea Wagner, Huan He, Mark Emmett, Charles A. Conrad

Research output: Contribution to journalArticle

22 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)126
Number of pages1
JournalBMC Systems Biology
Volume4
StatePublished - 2010
Externally publishedYes

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ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Görke, R., Meyer-Bäse, A., Wagner, D., He, H., Emmett, M., & Conrad, C. A. (2010). Determining and interpreting correlations in lipidomic networks found in glioblastoma cells. BMC Systems Biology, 4, 126.