Visual analysis and dynamical control of phosphoproteomic networks

Anke Meyer-Bäse, Robert Görke, Marc Lobbes, Mark R. Emmett, Carol L. Nilsson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents 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 framework applies to many other high-throughput data from experiments relevant to a variety of diseases. In addition to developing novel graph and control networks to predict therapeutic targets, these algorithms will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions.

Original languageEnglish (US)
Title of host publicationIndependent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
DOIs
StatePublished - Aug 9 2013
Event2013 Conference on Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI - Baltimore, MD, United States
Duration: May 1 2013May 3 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8750
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other2013 Conference on Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI
CountryUnited States
CityBaltimore, MD
Period5/1/135/3/13

Keywords

  • Glioma cancer stem cells
  • Graph theory
  • Nonlinear dynamics
  • Pathway analysis and control
  • Static and dynamic graph
  • Visualization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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