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Controlling Directed Protein Interaction Networks in Cancer

Krishna Kanhaiya, Eugen Czeizler, Christian Gratie, Ion Petre, Controlling Directed Protein Interaction Networks in Cancer. TUCS Technical Reports 1155, TUCS, 2016.


Control theory is a well-established approach in network science, with known
applications in bio-medicine and cancer research. We build on recent results for full
and structural controllability of directed networks, which gives a set of driver nodes
able to control the whole network, or an a-priori defined part of it, respectively. We
develop a novel approach for the structural controllability of cancer networks and
demonstrate it for the analysis of breast, pancreatic, and ovarian cancer. We build in
each case a signalling transduction (STN) protein-protein interaction (PPI) network
and focus on the so-called “essential proteins” specific to each cancer type in our
study. We show that the cancer essential proteins are efficiently controllable from a
(relatively small) computable set of driver nodes. Moreover, we adjust the method
to find the driver nodes among FDA-approved drug-target nodes. Interestingly, we
find that while many of the drugs acting on our driver nodes are part of known
cancer therapies, some of them are not used for the cancer types analyzed here;
also some drug-target driver nodes identified by our algorithms are not known to
be used in any cancer therapy. Overall we show that a better understanding of the
control dynamics of cancer through mathematical modelling could pave the way
for new efficient therapeutic approaches and personalized medicine.


Full publication in PDF-format

BibTeX entry:

  title = {Controlling Directed Protein Interaction Networks in Cancer},
  author = {Kanhaiya, Krishna and Czeizler, Eugen and Gratie, Christian and Petre, Ion},
  number = {1155},
  series = {TUCS Technical Reports},
  publisher = {TUCS},
  year = {2016},

Belongs to TUCS Research Unit(s): Computational Biomodeling Laboratory (Combio Lab)

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