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Determining and interpreting correlations in lipidomic networks found in glioblastoma cells

Görke, R. 1; Meyer-Bäse, A.; He, H.; Emmett, M. R.; Conrad, C. A.
1 Karlsruher Institut für Technologie (KIT)

Abstract:

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 death) and thus opens a new window to biomedical frontiers.


Verlagsausgabe §
DOI: 10.5445/IR/1000028143
Originalveröffentlichung
DOI: 10.1186/1752-0509-4-126
Scopus
Zitationen: 29
Web of Science
Zitationen: 27
Dimensions
Zitationen: 26
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2010
Sprache Englisch
Identifikator ISSN: 1752-0509
urn:nbn:de:swb:90-281434
KITopen-ID: 1000028143
Erschienen in BMC systems biology
Verlag Springer Fachmedien Wiesbaden
Band 4
Heft 1
Seiten 126
Nachgewiesen in Dimensions
Web of Science
Scopus
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