20 May 2015 Dynamical complex network theory applied to the therapeutics of brain malignancies
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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.
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Anke Meyer-Bäse, Anke Meyer-Bäse, Daniel Fratte, Daniel Fratte, Adrian Barbu, Adrian Barbu, Katja Pinker-Domenig, Katja Pinker-Domenig, } "Dynamical complex network theory applied to the therapeutics of brain malignancies", 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; https://doi.org/10.1117/12.2181816
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