The Driving Regulators of the Connectivity Protein Network of Brain Malignancies

A. Tahmassebi*, K. Pinker-Domenig, G. Wengert, M. Lobbes, A. Stadlbauer, N.C. Wildburger, F.J. Romero, D.P. Morales, E. Castillo, A. Garcia, G. Botella, A. Meyer-Base

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

An important problem in modern therapeutics at the proteomic 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 modern control concepts, such as pinning controllability and observability applied to the glioma cancer stem cells (GSCs) protein graph network with known and novel association to glioblastoma (GBM). The theoretical frameworks provides us with the minimal number of "driver nodes", which are necessary, and their location 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, to design and test novel therapeutic solutions.
Original languageEnglish
Title of host publicationSMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XIV
PublisherSPIE-INT SOC OPTICAL ENGINEERING
Number of pages8
ISBN (Print)9781510609341
DOIs
Publication statusPublished - 2017
EventConference on Smart Biomedical and Physiological Sensor Technology XIV: At SPIE Commercial + Scientific Sensing and Imaging - Anaheim, United States
Duration: 9 Apr 201710 Apr 2017
https://www.proceedings.com/spie10216.html

Publication series

SeriesProceedings of SPIE
Volume10216
ISSN0277-786X

Conference

ConferenceConference on Smart Biomedical and Physiological Sensor Technology XIV
Country/TerritoryUnited States
CityAnaheim
Period9/04/1710/04/17
Internet address

Keywords

  • Graph theory
  • nonlinear dynamics
  • dynamic graph
  • pathway analysis and control
  • glioma cancer stem cells

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