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DOI: 10.5445/IR/1000028143
Veröffentlicht am 25.05.2018
DOI: 10.1186/1752-0509-4-126
Zitationen: 22
Web of Science
Zitationen: 15

Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Görke, R.; Meyer-Bäse, A.; He, H.; Emmett, M. R.; Conrad, C. A.

Background: 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.
Results: 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.
Conclusions: 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 de ... mehr

Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Jahr 2010
Sprache Englisch
Identifikator ISSN: 1752-0509
URN: urn:nbn:de:swb:90-281434
KITopen-ID: 1000028143
Erschienen in BMC systems biology
Band 4
Heft 1
Seiten 126
Nachgewiesen in Web of Science
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