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Proceedings Paper

Dynamical complex network theory applied to the therapeutics of brain malignancies
Author(s): Anke Meyer-Bäse; Daniel Fratte; Adrian Barbu; Katja Pinker-Domenig
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Paper Abstract

An important problem in modern therapeutics at the metabolomic, transcriptomic or phosphoproteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel graph algorithms and modern control solutions applied to the graph networks resulting from specific experiments to discover disease-related pathways and drug targets in glioma cancer stem cells (GSCs). The theoretical frameworks provides us with the minimal number of ”driver nodes” necessary to determine the full control over the obtained graph network in order to provide a change in the network’s dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions.

Paper Details

Date Published: 20 May 2015
PDF: 8 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 949608 (20 May 2015); doi: 10.1117/12.2181816
Show Author Affiliations
Anke Meyer-Bäse, Florida State Univ. (United States)
Daniel Fratte, Florida State Univ. (United States)
Adrian Barbu, Florida State Univ. (United States)
Katja Pinker-Domenig, Medizinische Univ. Wien (Austria)
Memorial Sloan-Kettering Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)

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